Dynamical properties of low dimensional CuGeO3 and NaV2O5 systems

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Mobilgear SHC 46M 高技术、超高粘度、合成开式齿轮油说明书

Mobilgear SHC 46M 高技术、超高粘度、合成开式齿轮油说明书

Product Data Sheet Mobilgear SHC 46MHigh Technology, Ultra High Viscosity,Synthetic Open Gear Oil Product DescriptionMobilgear SHC 46M is a speciality ultra high viscosity lubricant primarily intended for use in heavily loaded, low and medium speed gears where boundary lubrication conditions prevail.Mobilgear SHC 46M is formulated from wax-free synthetic base stocks which have exceptional oxidation and thermal properties and remarkable excellent low-temperature fluidity. The combination of a naturally highviscosity index and a unique additive system enables the product to provide outstanding performance under severe high and low temperature operating conditions. The base stocks have inherently low traction properties which result in low fluid friction in the load zone of non-conforming surfaces such as gears. Reduced fluidfriction produces lower oil operating temperatures and improved gear efficiency.Mobilgear SHC 46M exceeds Falk minimum viscosity requirements for intermittent lubrication of gears.Mobilgear SHC 46M does not contain any solvent.BenefitsMobilgear SHC 46M offers the following benefits:•Extended gear life resulting from outstanding load-carrying, anti-wear and tackiness properties derived from proprietary thick film lubrication•Reduction in pump replacement costs due to their pumpability at ambient temperature•Improved safety gear efficiency and lower operating temperatures arising from low traction properties •Reduced lubricant consumption and disposal costs•Absence of hard-packed deposits and the elimination of the need for gear cleaning with subsequent reduction in downtime and maintenance costs•Improved safety through absence of solvent•Easier distribution/meteringApplicationMobilgear SHC 46M is primarily recommended for open gears where asphaltic, diluent-containing lubricantshave historically been used. Mobilgear SHC 46M is also recommended for the replacement of semi-fluid greases.Mobilgear SHC 46M can be applied with conventional single line or dual line spray systems without costly circulation system modifications. It is pumpable at temperatures as low as +6°C but, at lower temperatures, the product may have to be heated to achieve the proper spray distribution pattern.Health and SafetyBased on available toxicological information, it has been determined that this product poses no significant health risk when used and handled properly.Details on handling, as well as health and safety information, can be found in the Material Safety Data Bulletin which can be obtained through Mobil Oil Company Ltd., by telephoning 01372 22 2000.Typical physical characteristics are given in the table. These are intended as a guide to industry and are not necessarily manufacturing or marketing specifications.Typical CharacteristicsMobilgear SHC 46MTest MethodISO VG-46000Viscosity, cSt at 40ºC ASTM D4*******Viscosity, cSt at 100ºC ASTM D4451250Viscosity Index ASTM D2270210Specific Gravity ASTM D12980.924Flash Point, ºC ASTM D92228Pour Point, ºC ASTM D97+6Rust Protection, Distilled Water ASTM D665PassRust Protection, Synthetic Sea Water ASTM D665PassCopper Corrosion ASTM D1301A/1BTimken OK Load, kg ASTM D250926.7FZG Gear Test, Fail Stage DIN 5135412+4-Ball Wear, Scar mm ASTM D22660.9754-Ball Weld Load, kg ASTM D2596315Colour ASTM D1500 1.5Due to continual product research and development, the information contained herein is subject to change without notice.Mobil Oil Company LimitedActing as Agent for Mobil Lubricants UK LimitedExxonMobil House, Ermyn WayLeatherhead, Surrey, KT22 8UXTelephone: 01372 22 2000。

暗物质英语定义

暗物质英语定义

暗物质英语定义Dark matter, also known as invisible matter, is a mysterious substance that makes up a significant portion of the universe. Although it cannot be directly observed, its existence is inferred from its gravitational effects on visible matter. In this article, we will explore the definition of dark matter, its properties, and its implications for our understanding of the cosmos.Dark matter is believed to account for approximately 85% of the total matter in the universe. Its presence is necessary to explain the observed rotational velocities of galaxies and the gravitational lensing effects observed in clusters of galaxies. Unlike ordinary matter, dark matter does not interact with electromagnetic radiation, making it invisible to telescopes and other instruments that rely on light detection.One of the key properties of dark matter is its non-baryonic nature. Baryonic matter, which includes protons and neutrons, makes up only a small fraction of the total matter in the universe. Dark matter, on the other hand, consists of particles that do not interact via the strong nuclear force, which binds protons and neutrons together. Instead, dark matter particles are thought to interact primarily through gravity and weak nuclear forces.The exact nature of dark matter remains unknown, but several theoretical candidates have been proposed. One possibility is that dark matter consists of weakly interacting massive particles (WIMPs). These hypothetical particles would have masses larger than those of ordinary matter particles and would interact only weakly with other particles. Another candidate is the axion, a hypothetical particle that could explain the absence of certain symmetry violations in the strong nuclear force.The search for dark matter is a major focus of modern astrophysics and particle physics. Scientists employ a variety of experimental techniques to detect or indirectly infer the presence of dark matter. These include direct detection experiments, which aim to detect the rare interactions between dark matter particles and ordinary matter, and indirect detection experiments, which look for the products of dark matter annihilation or decay.Understanding the nature of dark matter is crucial for our understanding of the universe's evolution and structure formation. The presence of dark matter has profound implications for the Big Bang theory and the formation of galaxies and galaxy clusters. It is believed that dark matter played a crucial role in the formation of the large-scale structure of the universe, acting as a gravitational seed for the formation of galaxies and galaxy clusters.In addition to its gravitational effects, dark matter also influences the distribution of ordinary matter. The presence of dark matter affects the growth of structures in the universe, leading to the formation of cosmic web-like structures composed of filaments and voids. These structures can be observed through large-scale surveys of galaxies and the cosmic microwave background radiation.In conclusion, dark matter is a mysterious substance that constitutes a significant portion of the universe. Although invisible and non-baryonic, its presence is inferred from its gravitational effects on visible matter. The search for dark matter is an active area of research, with scientists employing various experimental techniques to shed light on its nature. Understanding dark matter is crucial for our understanding of the universe's evolution and structure formation.。

SMC - IA ... D IA ... S IA m

SMC - IA ... D IA ... S IA m

IA ... DIA ... SDescriptionThe rack and pinion pneumatic actuator IA Motion combines innovative design features with the latest technology, materials and protection coatings available, resulting in one of the highest grade pneumatic actuators on the market.Product features• FunctionIA...D double acting IA...S single acting• Nominal torque15 ÷ 10007 Nm(double acting at 6 bar air supply)• Supply pressure 3 ÷ 8 bar (IA1000D 3 ÷ 7 bar) • Supply fluids Filtered air or neutral gas • Working temperature -40°C ÷ 80°C• ConnectionMounting face for valves according to EN ISO 5211,for solenoid valves and accessories to VDI/VDE 3845 (NAMUR)• LubricationFactory lubricated for the life of actuator under normal working conditions• ATEXActuator IP68, standard in compliance with ATEX 94/9/ECDesign properties• Compact design with identical body and end caps for double acting and spring return types, allowing field conversion by adding or removing spring cartridges.• Body made of extruded aluminium with internal and external ALODUR ® corrosion protection, with honed cylinder surface for a higher cycle life and lower coefficient of friction.• Symmetric rack and pinion design for high-cycle life and fast operation. Reverse rotation can be accomplished by inverting the pistons.• Two independent external travel stop adjustments, enabling an easy and precise adjustment of -5°÷15° / 75°÷95°, in order to get a precise valve positioning.• One-piece blow-out proof, electroless nickel-plated drive shaft with bearing guided one-piece pinion for improved safety and max. cycle life.• Fully machined piston teeth for accurate low backlash rack and pinion engagement and maximum efficiency.• Pistons standard anodised for higher life.• Multifunction position indicator, adaptable to all kinds of limit and proximity switches.• Preloaded spring cartridges with coated springs for simple versatile range and corrosion resistance. Spring return actuator can be disassembled without danger on field.• High quality bearings and seals for low friction, high cycle life and a wide operating temperature range.• End caps, anodized and Polyester ® coated (RAL 5021).• All used screws in stainless steel for life time corrosion resistance.• Full compliance to the latest specifications: EN ISO 5211, VDI/VDE 3845, NAMUR and ATEX (Directive 94/9/CE).• Every single actuator is tested and provided with a unique serial number for traceability.❷❹❷❹❷❹❷❹IA...D ✈IA...S ✈IA...DIA...SIA050-100IA200-1000FunctionMaterialsYour benefits• High quality actuator designed for high-cycle life.• Multiple mounting circles and shafts to fit most quarter turn valves.• Easy conversion from double to single acting and vice versa.• Lower inventory with greater flexibility.• Position indicator with graduated ring indicating acurate angle.• Two external travel stop adjustmentsfor easy valvepositioning -5°÷15° / 75°÷95°.• Extensive size range to fit the requested torque at lowest costs.• Full compliance to latest worldwide standards.IA...D double acting actuatorAir supplied to port ❷ moves pistons toward endposition.(→ 90° counterclockwise rotation)IA...S single acting actuatorAir supplied to port ❷ moves pistons toward endposition, compressing springs (→ 90° counterclockwise rotation)Air supplied to port ❹ moves pistons toward center position.(→ 90° clockwise rotation)Air failure allows springs to move pistons toward center position(→ 90° clockwise rotation)IA200D .F05 - F0714❶❷❸❹Type codeAvailable options:• 5 different external coatings.• Stainless steel AISI 303, 430 or 316 drive shaft.• High and low temperature versions.• 0 ÷ 90° adjustable travel stop.• Cost efficient lock out capability.• Other drive shaft connections.• Rotation 120° and 180° and intermediate such as 135°.• 3 position actuators.• Stainless steel actuators.Please contact our technical department for more information about these options.0 ÷ 90° adjustable travel stopMd0º90ºMd0º90ºIA...D IA...STorques [Nm]Opening angle Opening angleTorques [Nm]1) IA045 S33, IA050 - IA550. S12 = Standard version of InterApp. Other number of springs on requestAAAAACCCCBBBB B ØZ1ØZ1ØZ1ØZ1ØZ1PPPP P CIA ... D IA ... St O ”t C ”[kg]t O ”t C ”[kg]0,150,200,750,200,250,90,20,251,150,250,31,260,250,31,70,30,351,90,30,353,00,40,53,40,40,54,20,50,64,81) BSP / ISO 228 / DIN 259V(l) Volume in litre, V O = OPEN, V C = CLOSE To calculate the air consumption, multiply the volume in litre by the supply pressure.t O / t C t O = opening time / t C = closing time, in secondsThe above mentioned operating times are obtained under the following conditions:- Air supply pressure min. 5,5 bar (80 psi) - at room temperature - medium clean air - actuator stroke 90° - actuator without resistance load Caution: obviously, during operation, if one or more of the above listed criteria differ, the operating time will be different.The technical data are noncommittal and do not assure you of any properties. Please refer to our general sales conditions. Modifications without notice. AccessoriesOur wide range of accessories includes all kinds of position indicators, solenoid valves, positioners, Bus systems, manual emergency overdrives, etc. Please refer to the corresponding documentation or download it from our website.Limit switchesSolenoid valveProximity switchAS-InterfacePositionerActuator size, solenoid valve and air supply pipe accordingtable below.© 2020 InterApp AG, all rights reserved。

Analyzing the Properties of Pigments and Dyes

Analyzing the Properties of Pigments and Dyes

Analyzing the Properties of Pigmentsand DyesPigments and dyes are two types of colorants used in various industries such as art, fashion, cosmetics, and printing. Both pigments and dyes have unique properties that make them suitable for different applications. In this article, we will analyze the properties of pigments and dyes.What are Pigments?Pigments are colorants that do not dissolve in the medium in which they are dispersed. Pigments are insoluble but dispersible in water, oil, or another medium. Pigments are used in a wide range of applications such as paints, inks, plastics, ceramics, and textiles. Pigments come in a variety of forms such as powders, pastes, or granules.The Properties of PigmentsPigments have several unique properties that make them ideal for various applications. Here are some of the properties of pigments:1. LightfastnessOne of the essential properties of pigments is lightfastness. The lightfastness of a pigment refers to its ability to retain its color when exposed to light. Pigments with a high level of lightfastness are resistant to fading, whereas pigments with low lightfastness will fade quickly.2. OpacityOpacity is the ability of pigments to block light. Pigments with high opacity can cover a surface entirely, whereas pigments with low opacity will allow some of the underlying surface color to show through.3. Chemical StabilityPigments must be chemically stable when exposed to various chemicals, whether they come into contact with solvents, acids, or bases. Any chemical reaction with the medium can cause a change in color or degrade the quality of the pigment.4. Particle SizeThe particle size of a pigment determines its dispersibility and the resulting color intensity. Smaller particles make pigments more translucent, whereas larger particles make pigments more opaque.5. Color StrengthThe color strength of a pigment is the intensity of its color when used at maximum concentration. Pigments with high color strength require less material to produce vivid, vibrant colors.What are Dyes?Dyes are colorants that dissolve in the medium in which they are used. Dyes are soluble in water, oil, or another medium. Dyes are used in a wide range of applications such as textiles, paper, leather, and food. Dyes come in various forms such as liquids or powders.The Properties of DyesDyes have several unique properties that make them ideal for various applications. Here are some of the properties of dyes:1. SolubilitySolubility is the ability of dyes to dissolve in liquid or other mediums. It is the essential property of dyes, which allows it to penetrate deep into the fiber and produce a vibrant color.2. WashfastnessWashfastness is the ability of dyes to resist fading when exposed to water. Dyes with high washfastness will retain their color even after repeated exposure to water and detergents.3. LightfastnessLightfastness is the ability of dyes to resist fading when exposed to light. Dyes with high lightfastness will retain their color even when exposed to sunlight or artificial light sources.4. AffinityThe affinity of dyes is the ability to attach themselves to the surface of the material they are applied to. Dyes with high affinity are more likely to produce uniform and vibrant colors.5. Color RangeOne of the essential properties of dyes is the ability to produce a wide range of colors. Dyes can create bright and vivid colors in various shades, hues, and tones.In ConclusionPigments and dyes are two unique types of colorants used for various applications. Pigments are insoluble but dispersible in water, oil, or another medium, while dyes are soluble. Pigments have properties such as lightfastness, opacity, and chemical stability, while dyes have properties such as solubility, washfastness, and affinity. Understanding the properties and differences between pigments and dyes can help you choose the best one for your specific needs.。

SOIL MECHANICS lecture

SOIL MECHANICS lecture

SOIL MECHANICSLECTURE NOTESLECTURE # 1SOIL AND SOIL ENGINEERING* The term Soil has various meanings, depending upon the general field in which it is being considered.*To a Pedologist ... Soil is the substance existing on the earth's surface, which grows and develops plant life.*To a Geologist ..... Soil is the material in the relative thin surface zone within which roots occur, and all the rest of the crust is grouped under the term ROCK irrespective of its hardness.*To an Engineer .... Soil is the un-aggregated or un-cemented deposits of mineral and/or organic particles or fragments covering large portion of the earth's crust.* Soil Mechanics is one of the youngest disciplines of Civil Engineering involving the study of soil, its behavior and application as an engineering material.*According to Terzaghi (1948): "Soil Mechanics is the application of laws of mechanics and hydraulics to engineering problems dealing with sediments and other unconsolidated accumulations of solid particles produced by the mechanical and chemical disintegration of rocks regardless of whether or not they contain an admixture of organic constituent."* Geotechnical Engineering ..... Is a broader term for Soil Mechanics.* Geotechnical Engineering contains:- Soil Mechanics (Soil Properties and Behavior)- Soil Dynamics (Dynamic Properties of Soils, Earthquake Engineering, Machine Foundation)- Foundation Engineering (Deep & Shallow Foundation)- Pavement Engineering (Flexible & Rigid Pavement)- Rock Mechanics (Rock Stability and Tunneling)- Geosynthetics (Soil Improvement)Soil Formation* Soil material is the product of rock* The geological process that produce soil isWEATHERING (Chemical and Physical).* Variation in Particle size and shape depends on:- Weathering Process- Transportation Process* Variation in Soil Structure Depends on:- Soil Minerals- Deposition Process* Transportation and DepositionFour forces are usually cause the transportation and deposition of soils1- Water ----- Alluvial Soil 1- Fluvial2- Estuarine3- Lacustrine4- Coastal5- Marine2- Ice ---------- Glacial Soils 1- Hard Pan2- Terminal Moraine3- Esker4- Kettles3- Wind -------- Aeolin Soils 1- Sand Dunes2- Loess4- Gravity ----- Colluvial Soil 1- TalusWhat type of soils are usually produced by the different weathering & transportation process?- Boulders- Gravel Cohesionless- Sand (Physical)- Silt Cohesive- Clay (Chemical)* These soils can be- Dry- Saturated - Fully- Partially* Also they have different shapes and texturesLECTURE # 2SOIL PROPERTIESPHYSICAL AND INDEX PROPERTIES1- Soil Composition- Solids- Water-Air2- Soil Phases- Dry- Saturated * Fully Saturated* Partially Saturated- Submerged3- Analytical Representation of Soil:For the purpose of defining the physical and index properties of soil it is more convenient to represent the soil skeleton by a block diagram or phase diagram. 4- Weight - Volume Relationships:WeightW t = W w + W sVolumeV t = V v + V s = V a + V w + V s 1- Unit Weight - Density* Also known as- Bulk Density- Soil Density-Unit Weight-Wet DensityRelationships Between Basic Properties:Examples:________________________________________________________________________Index PropertiesRefers to those properties of a soil that indicate the type and conditions of the soil, and provide a relationship to structural properties such as strength, compressibility, per meability, swelling potential, etc.________________________________________________________________________1- PARTICLE SIZE DISTRIBUTION* It is a screening process in which coarse fractions of soil are separated by means of series of sieves.* Particle sizes larger than 0.074 mm (U.S. No. 200 sieve) are usually analyzed by means of sieving. Soil materials finer than 0.074 mm (-200 material) are analyzed by means of sedimentation of soil particles by gravity (hydrometer analysis).1-1 MECHANICAL METHODU.S. Standard Sieve:Sieve No. 4 10 20 40 60 100 140 200 -200Opening in mm 4.76 2.00 0.84 0.42 0.25 0.149 0.105 0.074 -Cumulative Curve:* A linear scale is not convenient to use to size all the soil particles (opening from 200 mm to 0.002 mm).* Logarithmic Scale is usually used to draw the relationship between the % Passing and the Particle size.Example:Parameters Obtained From Grain Size Distribution Curve:1- Uniformity Coefficient C u (measure of the particle size range)Cu is also called Hazen CoefficientCu = D60/D10C u < 5 ----- Very UniformC u = 5 ----- Medium UniformC u > 5 ----- Nonuniform2- Coefficient of Gradation or Coefficient of Curvature C g(measure of the shape of the particle size curve)C g = (D30)2/ D60 x D10C g from 1 to 3 ------- well graded3- Coefficient of Permeabilityk = C k (D10)2 m/secConsistency Limits or Atterberg Limits:- State of Consistency of cohesive soil1- Determination of Liquid Limit:2- Determination of Plastic Limit:3- Determination of Plasticity IndexP.I. = L.L. - P.L. 4- Determination of Shrinkage Limit5- Liquidity Index:6- Activity:SOIL CLASSIFICATION SYSTEMS* Why do we need to classify soils ?To describe various soil types encountered in the nature in a systematic way and gathering soils that have distinct physical properties in groups and units.* General Requirements of a soil Classification System:1- Based on a scientific method2- Simple3- Permit classification by visual and manual tests.4- Describe certain engineering properties5- Should be accepted to all engineers* Various Soil Classification Systems:1- Geologic Soil Classification System2- Agronomic Soil Classification System3- Textural Soil Classification System (USDA)4-American Association of State Highway Transportation Officials System (AASHTO) 5- Unified Soil Classification System (USCS)6- American Society for Testing and Materials System (ASTM)7- Federal Aviation Agency System (FAA)8- Others1- Unified Soil Classification (USC) System:The main Groups:G = GravelS = Sand.........................M = SiltC = Clay........................O = Organic........................* For Cohesionless Soil (Gravel and Sand), the soil can be Poorly Graded or Well GradedPoorly Graded = PWell Graded = W* For Cohesive Soil (Silt & Clay), the soil can be Low Plastic or High Plastic Low Plastic = LHigh Plastic = HTherefore, we can have several combinations of soils such as:GW = Well Graded GravelGP = Poorly Graded GravelGM = Silty GravelGC = Clayey GravelPassing Sieve # 4SW = Well Graded SandSP = Poorly Graded SandSM = Silty SandSC = Clayey SandPassing Sieve # 200ML = Low Plastic SiltCL = Low Plastic ClayMH = High Plastic SiltCH = High Plastic ClayTo conclud if the soil is low plastic or high plastic use Gassagrande's Chart________________________________________________________________________ 2- American Association of State Highway Transportation Officials System (AASHTO):- Soils are classified into 7 major groups A-1 to A-7Granular A-1 {A-1-a - A-1-b}(Gravel & Sand) A-2 {A-2-4 - A-2-5 - A-2-6 - A-2-6}A-3More than 35% pass # 200A-4Fine A-5(Silt & Clay) A-6A-7Group Index:_________________________________________________ ___3- Textural Soil Classification System (USDA)* USDA considers only:SandSiltClayNo. Gravel in the System* If you encounter gravel in the soil ------- Subtract the % of gravel from the 100%.* 12 Subgroups in the systemExample: ********MOISTURE DENSITY RELATIONSHIPS(SOIL COMPACTION)INTRODUCTION:* In the construction of highway embankments, earth dams, and many other engineering projects, loose soils must be compacted to increase their unit weight.* Compaction improves characteristics of soils:1- Increases Strength2- Decreases permeability3- Reduces settlement of foundation4- Increases slope stability of embankments* Soil Compaction can be achieved either by static or dynamic loading:1- Smooth-wheel rollers2- Sheepfoot rollers3- Rubber-tired rollers4- Vibratory Rollers5- Vibroflotation_____________________________________________________________________________________________General Principles:* The degree of compaction of soil is measured by its unit weight, , and optimum moisture content, w c.* The process of soil compaction is simply expelling the air from the voids.or reducing air voids* Reducing the water from the voids means consolidation.Mechanism of Soil Compaction:* By reducing the air voids, more soil can be added to the block. When moisture is added to the block (water content, w c, is increasing) the soil particles will slip more oneach other causing more reduction in the total volume, which will result in adding moresoil and, hence, the dry density will increase, accordingly.* Increasing W c will increaseUp to a certain limit (Optimum moister Content, OMC)After this limitIncreasing W c will decreaseDensity-Moisture RelationshipKnowing the wet unit weight and the moisture content, the dry unit weight can be determined from:The theoretical maximum dry unit weight assuming zero air voids is:I- Laboratory Compaction:* Two Tests are usually performed in the laboratory to determine the maximum dry unit weight and the OMC.1- Standard Proctor Test2- Modified Proctor TestIn both tests the compaction energy is:1- Standard Proctor TestFactors Affecting Compaction:1- Effect of Soil Type2- Effect of Energy on Compaction3- Effect of Compaction on Soil Structure4- Effect of Compaction on Cohesive Soil PropertiesII- Field CompactionFlow of Water in SoilsPermeability and Seepage* Soil is a three phase medium -------- solids, water, and air* Water in soils occur in various conditions* Water can flow through the voids in a soil from a point of high energy to a point of low energy.* Why studying flow of water in porous media ???????1- To estimate the quantity of underground seepage2- To determine the quantity of water that can be discharged form a soil3- To determine the pore water pressure/effective geostatic stresses, and to analyze earth structures subjected to water flow.4- To determine the volume change in soil layers (soil consolidation) and settlement of foundation.* Flow of Water in Soils depends on:1- Porosity of the soil2- Type of the soil - particle size- particle shape- degree of packing3- Viscosity of the fluid - Temperature- Chemical Components4- Total head (difference in energy) - Pressure head- Velocity head- Elevation headThe degree of compressibility of a soil is expressed by the coefficient of permeability of the soil "k."k cm/sec, ft/sec, m/sec, ........Hydraulic GradientBernouli's Equation:For soilsFlow of Water in Soils1- Hydraulic Head in SoilTotal Head = Pressure head + Elevation Headh t = h p + h e- Elevation head at a point = Extent of that point from the datum- Pressure head at a point = Height of which the water rises in the piezometer above the point.- Pore Water pressure at a point = P.W.P. = g water . h p*How to measure the Pressure Head or the Piezometric Head???????Tips1- Assume that you do not have seepage in the system (Before Seepage)2- Assume that you have piezometer at the point under consideration3- Get the measurement of the piezometric head (Water column in the Piezometer before seepage) = h p(Before Seepage)4- Now consider the problem during seepage5- Measure the amount of the head loss in the piezometer (Dh) or the drop in the piezometric head.6- The piezometric head during seepage = h p(during seepage) = h p(Before Seepage) - DhGEOSTATIC STRESSES&STRESS DISTRIBUTIONStresses at a point in a soil mass are divided into two main types:I- Geostatic Stresses ------ Due to the self weight of the soil mass.II- Excess Stresses ------ From structuresI. Geostatic stressesI.A. Vertical StressVertical geostatic stresses increase with depth, There are three 3 types of geostatic stresses1-a Total Stress, s total1-b. Effective Stress, s eff, or s'1-c Pore Water Pressure, uTotal Stress = Effective stress + Pore Water Pressures total = s eff + uGeostatic Stress with SeepageWhen the Seepage Force = H g sub -- Effective Stress s eff = 0 This case is referred asBoiling or Quick ConditonI.B. Horizontal Stress or Lateral Stresss h = k o s'vk o = Lateral Earth Pressure Coefficients h is always associated with the vertical effective stress, s'v.never use total vertical stress to determine s h.II. Stress Distribution in Soil Mass:When applying a load on a half space medium the excess stresses in the soil will decrease with depth.Like in the geostatic stresses, there are vertical and lateral excess stresses.1. For Point LoadThe excess vertical stress is according to Boussinesq (1883):- I p = Influence factor for the point load- Knowing r/z ----- I1 can be obtained from tablesAccording to Westergaard (1938)where h = s (1-2m / 2-2m) m = Poisson's Ratio2. For Line LoadUsing q/unit length on the surface of a semi infinite soil mass, the vertical stress is:3. For a Strip Load (Finite Width and Infinite Length): The excess vertical stress due to load/unit area, q, is:Where I l = Influence factor for a line load3. For a Circular Loaded Area:The excess vertical stress due to q is:。

The structure of dark matter halos. Observation versus theory

The structure of dark matter halos. Observation versus theory
tion. All halo density profiles can be fit accurately by the simple formula,
ρ(r)
=
ρ¯ (r/rs)(1 +
r/rs)2
(1)
where ρ¯ and rs are two free parameters. It is very interesting that Navarro et al 12,14 find a strong correlation between ρ¯ and rs. Dark matter halos seem
observations. Early cosmological calculations did not have enough resolution
in order to resolve the density structure of DM halos in detail. Recent highresolution simulations 11,12,14 however have shown that in the inner and outer regions dark matter halos depart significantly from an r−2 power-law distribu-
Unfortunately it is difficult to observationally verify these numerical results as galaxies are in general gravitationally dominated by their visible baryonic components in the inner regions, while in the outer regions there is not enough visible material in order to measure accurately a rotation curve. In the inner region the inferred DM profiles will depend strongly on how much baryonic mass is subtracted, which in turn depends on the assumed baryonic mass-tolight ratio. The situation becomes even more complicated by the fact that a dominating baryonic component will gravitationally affect and change the cold dark matter density profile 13.

MATHEMATICS(PUREANDAPPLIED):(纯粹数学和应用)

MATHEMATICS(PUREANDAPPLIED):(纯粹数学和应用)

Professor & Head of DepartmentNT Bishop, MA(Cambridge), PhD(Southampton), FRASSenior LecturersJ Larena, MSc(Paris), PhD(Paris)D Pollney, PhD(Southampton)CC Remsing, MSc(Timisoara), PhD(Rhodes)Vacant LecturersEOD Andriantiana, PhD(Stellenbosch)V Naicker, MSc(KwaZulu-Natal)AL Pinchuck, MSc(Rhodes), PhD(Wits)Lecturer, Academic Development M Lubczonok, Masters(Jagiellonian)Mathematics (MA T) is a six-semester subject and Applied Mathematics (MAP) is a four-semester subject. These subjects may be taken as major subjects for the degrees of BSc, BA, BJourn, BCom, BBusSci, BEcon and BSocSc, and for the diploma HDE(SEC).To major in Mathematics, a candidate is required to obtain credit in the following courses: MAT1C; MAM2; MAT3. See Rule S.23.To major in Applied Mathematics, a candidate is required to obtain credit in the following courses: MAT1C, MAM2; MAP3. See Rule S.23.The attention of students who hope to pursue careers in the field of Bioinformatics is drawn to the recommended curriculum that leads to postgraduate study in this area, in which Mathematics is a recommended co-major with Biochemistry, and for which two years of Computer Science and either Mathematics or Mathematical Statistics are prerequisites. Details of this curriculum can be foundin the entry for the Department of Biochemistry, Microbiology and Biotechnology.See the Departmental Web Page http://www.ru.ac.za/departments/mathematics/ for further details, particularly on the content of courses.First-year level courses in MathematicsMathematics 1 (MAT1C) is given as a year-long semesterized two-credit course. Credit in MAT1C must be obtained by students who wish to major in certain subjects (such as Applied Mathematics, MATHEMATICS (PURE AND APPLIED)Physics and Mathematical Statistics) and by students registered for the BBusSci degree.Introductory Mathematics (MAT1S) is recommended for Pharmacy students and for Science students who do not need MAT1C or MAT1C1.Supplementary examinations may be recommended for any of these courses, provided that a candidate achieves a minimum standard specified by the Department.Mathematics 1L (MA T1L) is a full year course for students who do not qualify for entry into any of the first courses mentioned above. This is particularly suitable for students in the Social Sciences and Biological Sciences who need to become numerate or achieve a level of mathematical literacy. A successful pass in this course will give admission to MA T1C.First yearMAT1CThere are two first-year courses in Mathematics for candidates planning to major in Mathematics or Applied Mathematics. MAT1C1 is held in thefirst semester and MAT1C2 in the second semester. Credit may be obtained in each course separately and, in addition, an aggregate mark of at least 50%will be deemed to be equivalent to a two-creditcourse MAT1C, provided that a candidate obtains the required sub-minimum (40%) in each component. Supplementary examinations may be recommended in either course, provided that a candidate achieves a minimum standard specified by the department. Candidates obtaining less than 40% for MAT1C1 are not permitted to continue with MAT1C2.MAT1C1 (First semester course): Basic concepts (number systems, functions), calculus (limits,continuity, differentiation, optimisation, curvesketching, introduction to integration), propositional calculus, mathematical induction, permutations, combinations, binomial theorem, vectors, lines andplanes, matrices and systems of linear equations.MAT1C2 (Second semester course): Calculus (integration, applications of integration, improper integrals), complex numbers, differential equations, partial differentiation, sequences and series.MAT1S (Semester course: Introductory Mathematics) (about 65 lectures)Estimation, ratios, scales (log scales), change of units, measurements; Vectors, systems of equations, matrices, in 2-dimensions; Functions: Review of coordinate geometry, absolute values (including graphs); Inequalities; Power functions, trig functions, exponential functions, the number e (including graphs); Inverse functions: roots, logs, ln (including graphs); Graphs and working with graphs; Interpretation of graphs, modeling; Descriptive statistics (mean, standard deviation, variance) with examples including normally distributed data; Introduction to differentiation and basic derivatives; Differentiation techniques (product, quotient and chain rules); Introduction to integration and basic integrals; Modeling, translation of real-world problems into mathematics.MAT 1L: Mathematics Literacy This course helps students develop appropriatemathematical tools necessary to represent and interpret information quantitatively. It also develops skills and meaningful ways of thinking, reasoning and arguing with quantitative ideas in order to solve problems in any given context.Arithmetic: Units of scientific measurement, scales, dimensions; Error and uncertainty in measure values.Fractions and percentages - usages in basic science and commerce; use of calculators and spreadsheets. Algebra: Polynomial, exponential, logarithmic and trigonometric functions and their graphs; modelling with functions; fitting curves to data; setting up and solving equations. Sequences and series, presentation of statistical data.Differential Calculus: Limits and continuity; Rules of differentiation; Applications of Calculus in curvesketching and optimisation.Second Year Mathematics 2 comprises two semesterized courses,MAM201 and MAM202, each comprising of 65 lectures. Credit may be obtained in each course seperately. An aggregate mark of 50% will grant the two-credit course MAM2, provided a sub-minimumof 40% is achieved in both semesters. Each semester consists of a primary and secondary stream which are run concurrently at 3 and 2 lectures per week, respectively. Additionally, a problem-based course in Mathematical Programming contributes to the class record and runs throughout the academic year.MAM201 (First semester):Advanced Calculus (39 lectures): Partial differentiation: directional derivatives and the gradient vector; maxima and minima of surfaces; Lagrange multipliers. Multiple integrals: surface and volume integrals in general coordinate systems. Vector calculus: vector fields, line integrals, fundamental theorem of line integrals, Green’s theorem, curl and divergence, parametric curves and surfaces.Ordinary Differential Equations (20 lectures): First order ordinary differential equations, linear differential equations of second order, Laplace transforms, systems of equations, series solutions, Green’s functions.Mathematical Programming 1 (6 lectures): Introduction to the MATLAB language, basic syntax, tools, programming principles. Applicationstaken from MAM2 modules. Course runs over twosemesters.MAM202 (Second semester):Linear Algebra (39 lectures): Linear spaces, inner products, norms. Vector spaces, spans, linear independence, basis and dimension. Linear transformations, change of basis, eigenvalues, diagonalization and its applications.Groups and Geometry (20 lectures): Number theory and counting. Groups, permutation groups, homomorphisms, symmetry groups in 2 and 3 dimensions. The Euclidean plane, transformations and isometries. Complex numbers, roots of unity and introduction to the geometry of the complex plane.Mathematical Programming 2 (6 lectures): Problem-based continuation of Semester 1.Third-year level courses inMathematics and Applied Mathematics Mathematics and Applied Mathematics are offered at the third year level. Each consists of four modules as listed below. Code TopicSemester Subject AM3.1 Numerical analysis 1 Applied MathematicsAM3.2 Dynamical systems 2 Applied Mathematics AM3.4 Partial differentialequations 1 Applied Mathematics AM3.5 Advanced differentialequations 2 Applied MathematicsM3.1 Algebra 2 MathematicsM3.2 Complex analysis 1 MathematicsM3.3 Real analysis 1 MathematicsM3.4 Differential geometry 2 Mathematics Students who obtain at least 40% in all of the above modules will be granted credit for both MAT3 and MAP3, provided that the average of the Applied Mathematics modules is at least 50% AND the average of the Mathematics modules is at least 50%. Students who obtain at least 40% for any FOUR of the above modules and with an average mark over the four modules of at least 50%, will be granted credit in either MAT3 or MAP3. If three or four of the modules are from Applied Mathematics then the credit will be in MAP3, otherwise it will be in MAT3.Module credits may be carried forward from year to year.Changes to the modules offered may be made from time-to-time depending on the interests of the academic staff.Credit for MAM 2 is required before admission to the third year courses.M3.1 (about 39 lectures) AlgebraAlgebra is one of the main areas of mathematics with a rich history. Algebraic structures pervade all modern mathematics. This course introduces students to the algebraic structure of groups, rings and fields. Algebra is a required course for any further study in mathematics.Syllabus: Sets, equivalence relations, groups, rings, fields, integral domains, homorphisms, isomorphisms, and their elementary properties.M3.2 (about 39 lectures) Complex Analysis Building on the first year introduction to complex numbers, this course provides a rigorous introduction to the theory of functions of a complex variable. It introduces and examines complex-valued functions of a complex variable, such as notions of elementary functions, their limits, derivatives and integrals. Syllabus: Revision of complex numbers, Cauchy- Riemann equations, analytic and harmonic functions, elementary functions and their properties, branches of logarithmic functions, complex differentiation, integration in the complex plane, Cauchy’s Theorem and integral formula, Taylor and Laurent series, Residue theory and applications. Fourier Integrals.M3.3 (about 39 lectures) Real AnalysisReal Analysis is the field of mathematics that studies properties of real numbers and functions on them. The course places great emphasis on careful reasoning and proof. This course is an essential basis for any further study in mathematics.Syllabus: Topology of the real line, continuity and uniform continuity, Heine-Borel, Bolzano-Weierstrass, uniform convergence, introduction to metric spaces.M3.4 (about 39 lectures) Differential Geometry Roughly speaking, differential geometry is concerned with understanding shapes and their properties in terms of calculus. This elementary course on differential geometry provides a perfect transition to higher mathematics and its applications. It is a subject which allows students to see mathematics for what it is - a unified whole mixing together geometry, calculus, linear algebra, differential equations, complex variables, calculus of variations and topology.Syllabus: Curves (in the plane and in the space), curvature, global properties of curves, surfaces, the first fundamental form, isometries, the second fundamental form, the normal and principal curvatures, the Gaussian and mean curvatures, the Gauss map, geodesics.AM3.1 (about 39 lectures) Numerical Analysis Many mathematical problems cannot be solved exactly and require numerical techniques. These techniques usually consist of an algorithm which performs a numerical calculation iteratively until certain tolerances are met. These algorithms can be expressed as a program which is executed by a computer. The collection of such techniques is called “numerical analysis”.Syllabus: Systems of non-linear equations, polynomial interpolation, cubic splines, numerical linear algebra, numerical computation of eigenvalues, numerical differentiation and integration, numerical solution of ordinary and partial differential equations, finite differences,, approximation theory, discrete Fourier transform.AM3.2 (about 39 lectures) Dynamical Systems This module is about the dynamical aspects of ordinary differential equations and the relations between dynamical systems and certain fields of applied mathematics (like control theory and the Lagrangian and Hamiltonian formalisms of classical mechanics). The emphasis is on the mathematical aspects of various constructions and structures rather than on the specific physical/mechanical models. Syllabus Linear systems; Linear control systems; Nonlinear systems (local theory); Nonlinear control systems; Nonlinear systems (global theory); Applications : elements of optimal control and/or geometric mechanics.AM3.4 (about 39 lectures) Partial Differential EquationsThis course deals with the basic theory of partial differential equations (elliptic, parabolic and hyperbolic) and dynamical systems. It presents both the qualitative properties of solutions of partial differential equations and methods of solution. Syllabus: First-order partial equations, classification of second-order equations, derivation of the classical equations of mathematical physics (wave equation, Laplace equation, and heat equation), method of characteristics, construction and behaviour of solutions, maximum principles, energy integrals. Fourier and Laplace transforms, introduction to dynamical systems.AM3.5 (about 39 lectures)Advanced differential equationsThis course is an introduction to the study of nonlinearity and chaos. Many natural phenomena can be modeled as nonlinear ordinary differential equations, the majority of which are impossible to solve analytically. Examples of nonlinear behaviour are drawn from across the sciences including physics, biology and engineering.Syllabus:Integrability theory and qualitative techniques for deducing underlying behaviour such as phase plane analysis, linearisations and pertubations. The study of flows, bifurcations, the Poincare-Bendixson theorem, and the Lorenz equations.Mathematics and Applied Mathematics Honours Each of the two courses consists of either eight topics and one project or six topics and two projects.A Mathematics Honours course usually requires the candidate to have majored in Mathematics, whilst Applied Mathematics Honours usually requires the candidate to have majored in Applied Mathematics. The topics are selected from the following general areas covering a wide spectrum of contemporary Mathematics and Applied Mathematics: Algebra; Combinatorics; Complex Analysis; Cosmology; Functional Analysis; General Relativity; Geometric Control Theory; Geometry; Logic and Set Theory; Measure Theory; Number Theory; Numerical Modelling; Topology.Two or three topics from those offered at the third-year level in either Mathematics or Applied Mathematics may also be taken in the case of a student who has not done such topics before. With the approval of the Heads of Department concerned, the course may also contain topics from Education, and from those offered by other departments in the Science Faculty such as Physics, Computer Science, and Statistics. On the other hand, the topics above may also be considered by such Departments as possible components of their postgraduate courses.Master’s and Doctoral degrees in Mathematics or Applied MathematicsSuitably qualified students are encouraged to proceed to these degrees under the direction of the staff of the Department. Requirements for these degrees are given in the General Rules.A Master’s degree in either Mathematics or Applied Mathematics may be taken by thesis only, or by a combination of course work and a thesis. Normally four examination papers and/or essays are required apart from the thesis. The whole course of study must be approved by the Head of Department.。

Absolute and convective instabilities of a viscous film flowing down a vertical fiber2007

Absolute and convective instabilities of a viscous film flowing down a vertical fiber2007

244502-1
© 2007 The American Physical Society
PRL 98, 244502 (2007)
PHYSICAL REVIEW LETTERS
week endform film thicknesses 0:6R < hN < 3R (corresponding values of dimensionless parameters will be given later). The flow rate is measured with computer-controlled scales placed below the collecting tank. During an experiment, the liquid height variation in the tank (hence the flow rate variation) is less than 1%. A linear camera and a fast digital camera are mounted on micrometric assemblies allowing for a precise alignment of the field of view with the film for the linear camera and a precise displacement along the fiber for the fast one. Spatiotemporal diagrams obtained with the linear camera (using a vertical pixel line) allow for a sensitive detection of the film-thickness variations. Fluctuations of the film thickness with time at a given position are also recorded by orienting horizontally the pixel line. A snapshot of the flow at the inlet is depicted in Fig. 1(b) for a small flow rate on a thin fiber. A self-sustained dynamics is observed. Immediately after the inlet, the axisymmetric film of uniform thickness emerging from the capillary meniscus breaks up spontaneously into a droplike wave train. Depending on the flow rate and the fiber radius, two different regimes can be identified from the spatiotemporal evolution of the film thickness (see Fig. 2). At low flow rates and relatively small fiber radii, a regular primary wave train is observed at a constant distance from the inlet. Sufficiently close to the inlet, a power spectrum of the time variations of the film thickness reveals a well-defined frequency. Further downstream, a secondary instability disorganizes the flow. However, for even smaller fiber radii and over a rather narrow interval of (small) flow rates (the thinner the fiber the wider the range of flow rates), the waves propagate at constant speed, shape, and frequency all along the fiber [see Fig. 1(c)]. This global mode regime was observed initially by Kliakhandler et al. [9]. At larger flow rates and any radius, the primary wave train is irregular, its frequency spectrum is much broader and its onset location fluctuates in time. For thick fibers (R 0:47 mm) the regular wave regime was never observed. A quantity of particular interest is ‘‘the healing length’’ defined as the distance from the inlet to the location at

Dynamic Motion Planning in Low Obstacle Density Environments

Dynamic Motion Planning in Low Obstacle Density Environments

Dynamic Motion Planningin Low Obstacle Density Environments Robert-Paul Berretty Mark Overmars A.Frank van der Stappen Department of Computer Science,Utrecht University,P.O.Box80.089,3508TB Utrecht,The Netherlands.AbstractA fundamental task for an autonomous robot is to plan its own motions.Ex-act approaches to the solution of this motion planning problem suffer from highworst-case running times.The weak and realistic low obstacle density(L.O.D.)assumption results in linear complexity in the number of obstacles of the freespace[11].In this paper we address the dynamic version of the motion planningproblem in which a robot moves among moving polygonal obstacles.The ob-stacles are assumed to move along constant complexity polylines,and to respectthe low density property at any given time.We will show that in this situation acell decomposition of the free space of size can be computedin time.The dynamic motion planning problem is then solvedin time.We also show that these results are close to optimal. Keywords:Motion planning,low obstacle density,moving obstacles,cell decompo-sition.1IntroductionRobot motion planning concerns the problem offinding a collision-free path for a robot in a workspace with a set of obstacles from an initial placement to a final placement.The parameters required to specify a placement of the robot are referred to as the degrees of freedom of the robot.The motion planning problem is often studied as a problem in the configuration space,which is the set of parametric representations of the placements of the robot.The free space FP is the sub-space of of placements for which the robot does not intersect any obstacle in.A feasible motion for the robot corresponds to a curve from to in FP(or its closure).Motion planning is a difficult problem.In general,many instances of the robot mo-tion planning problem are P-SPACE-complete,even if the obstacles are stationary[5]. For a constant-complexity robot moving amidst stationary obstacles polynomial time algorithms have been shown to exist.The running time is exponential in the number of degrees of freedom of the robot[7].For an-DOF robot,the complexity of the free space,can be as high as and the motion planning problem will,therefore,in general have a worst case running time close to.Research is partially supported by the Dutch Organization for Scientific Research(N.W.O.).We address the motion planning problem for a robot operating in an environment with moving obstacles.This problem is also referred to as the dynamic motion plan-ning problem.In general,when the obstacles in the workspace are allowed to move, the motion planning problem becomes even more complicated.For example,Reif and Sharir[6]showed that,when obstacles in a3-dimensional workspace are allowed to ro-tate,the motion planning problem is PSPACE-hard if the velocity modulus is bounded, and NP-hard otherwise.(A similar result was obtained by Sutner and Maass[8].) Canny and Reif[3]showed that dynamic motion planning for a point in the plane,with a bounded velocity modulus and an arbitrary number of convex polygonal obstacles, is NP-hard,even when the obstacles are convex and translate at constant linear veloc-ities.They also showed that the2-dimensional dynamic motion planning problem for a translating robot with bounded velocity modulus,among polygonal obstacles that translate atfixed linear velocity,can be solved using an algorithm that is polygo-nal in the total number of vertices of and,if the number of obstacles is bounded. However,their algorithm takes exponential time in the number of moving obstacles.Van der Stappen et al.[11](see also[10])showed that modelling robots in re-alistic workspaces has a profound influence on the complexity of solving the static motion planning problem,mainly independent of the number of degrees of freedom of the robot.They gave a description of environments with a so-called low obsta-cle density which leads to a surprising gain in efficiency for several instances of the motion planning problem.An environment has the low obstacle density property if any region in the workspace intersects a constant number of obstacles that are larger than the size of the region.(See below for a more precise definition.)Under the low obstacle density assumption,the exact motion planning problem for an-DOF robot was efficiently solved,using the cell decomposition approach.The low obstacle density of the workspace implies a linear combinatorial complexity of the free space, even for-DOF robots.For a robot moving amidst stationary obstacles the cell decomposition of the free space has size and is computable in time. Vleugels[12]extended these results to multiple robots simultaneously operating in the same workspace.De Berg et al.[2]gave an overview of several realistic input models and gave experimental results on scenes based on real input data,which showed that the‘hidden’constant in the low obstacle density assumption was indeed low.We demonstrate that the low obstacle density property can also be used to ef-ficiently plan a motion for a robot with degrees of freedom moving in a2-dimensional workspace with non-stationary obstacles.The obstacles are allowed to translate in the workspace along polyline trajectories,with afixed speed per segment. The motion planning problem is then solved in time,using a cell decomposition of size.Note that these bounds do not depend on(assuming is constant).We also show that this result is close to optimal,by giving an example where the robot has to perform simple motions to get from its start to its goal position.In this paper we willfirst present an overview of the method used in the paper of Van der Stappen et al.[11].The computation of the cell decomposition for the dynamic low obstacle density motion planning problem is treated in Sections3and4; the algorithm to compute a feasible path through the cell decomposition is presented Section5.Section6concludes the paper.2Low Obstacle DensityIn this section we recall some of the definitions and results from the paper by Van der Stappen et al.[11]on motion planning in low density environments.The authors focus in particular on the large class of motion planning problems with configuration spaces of the form,where is the-dimensional workspace and is some -dimensional rest space.Let us use the reach of a robot as a measure for its maximum size;the reach of is defined as the maximum radius that the minimal enclosing hypersphere of the robot,centered at its reference point,can ever have(in any placement of).The reach of the robot is assumed to be comparable to the size of the smallest obstacle.The robot has constant complexity and moves in a workspace with constant-complexity obstacles.The workspace satisfies the static low obstacle density property which is defined as follows.Property2.1Let I R be a space with a set of non intersecting obstacles.Then I R is said to be a static low(obstacle)density space if for any region I R with minimal enclosing hyper-sphere radius,the number of obstacles with minimal enclosing hyper-sphere radius at least intersecting is bounded by a constant. Van der Stappen et al.[11]showed that,under the circumstances outlined above,the complexity of the free space is linear in the number of obstacles.The configuration space contains hyper-surfaces of the form,consisting of placements of the robot in which a robot feature is in contact with an obstacle feature.We shall denote the fact that is a feature of some object or object set by.The arrangement of all(constant-complexity)constraint hyper-surfacesdivides the higher-dimensional configuration space into free cells and forbidden cells.Van der Stappen et al.[11]considered so-called cylindri-fiable configuration spaces which have the property that the subspace —referred to as the base space—can be partitioned into constant complexity regions satisfyingA partition that satisfies this constraint is called a cylindrical partition.In words,the lifting of the region into the configuration space is intersected by a constant number of constraint hyper-surfaces.These hyper-surfaces subdivide the cylinder into constant-complexity free and forbidden cells.The cylindrical partition of therefore almost immediately gives us a cell decomposition of the free portion FP of. Theorem2.2states that the transformation of a cylindrical partition of the base space into a cell decomposition of the free space can be accomplished in time proportional to the size of the cylindrical partition.Theorem2.2[11]Let be the set of regions of a cylindrical partition of a base space and let be the set of region adjacencies.Let the regions of be of constant com-plexity.Then the cell decomposition of the free space calculated by lifting the regions of the base partition into the configuration space consists of constant complexity subcells.Furthermore,the complexity of the decomposition and the time to compute it is.Note that the size of the cylindrical partition determines the size of the cell decompo-sition.The low obstacle density motion planning problem outlined above was shown to yield a cylindrifiable configuration space,in which the workspace is a valid base space.Small and efficiently computable cylindrical partitions of have led to opti-mal cell decompositions and thus efficient solutions to the motion planning problem (see[11]for details).In this paper,we show that the configuration space of the dynamic version of the low obstacle density motion planning problem is cylindrifiable as well.Wefind a cylindrical partition of an appropriate base space that leads to an almost optimal size cell decomposition.3A Dynamic Base Space3.1Problem StatementWe now focus on the dynamic robot motion planning problem,subjected to low ob-stacle density.We show that the framework outlined in Section2can be used to plan a motion for a robot with degrees of freedom,moving in a2-dimensional workspace with non-stationary obstacles.The obstacles translate in the workspace, and can only change speed or direction a constant number of times.We will use a cell-decomposition based on a cylindrical partition,similar to Section2.Since dynamic motion planning is tedious to deal with,we split the problem into sub-problems.We first formally define the problem and state some useful properties of the base space for the dynamic motion planning problem.In Section4,we construct a cylindrical decomposition,and in Section5,we compute the actual path for the robot.The dynamic low obstacle density motion planning problem is defined as follows.The workspace of the robot is the2-dimensional Euclidean space I R and contains a collection of obstacles,each moving along a polyline at constant speed per line segment.The robot has constant complexity and its reach is bounded by, where is a constant and is a lower bound on the minimal enclosing hyper-sphere radii of all obstacles.Each obstacle is polygonal and has constant complexity.Any constraint hyper-surface in the configuration space corresponding to the set of robot placements in which a certain robot feature is in contact with a certain obstacle,is algebraic of bounded degree.The robot is placed at the initial placement at time and has to be at the goal placement at time.At any time between and,the workspace with obstacles satisfies the low obstacle density property.A standard approach when dealing with moving obstacles is to augment the sta-tionary configuration space with an extra time dimension.In this manner,we obtainthe configuration-time space.When planning the motion of our robot through the configuration-time space,we have to make sure that the path is time-monotone—the robot is not allowed to move back in time.Thefirst objective in solving the dynamic low obstacle density motion planning problem is to obtain a cylindrical partition that consists of constant complexity regions.An appropriate choice for a base space is the Cartesian product of the2-dimensional workspace and time.This way,the config-uration time space is of the form I R I R I R, where is some-dimensional rest space.3.2Characteristics of the Base SpaceThe base space can be considered as a3-dimensional Euclidean space. In our dynamic motion planning setting,we only consider the work-time space slice I R.Wefirst look at the situation where the obstacles move along a line in the ter,we extend the result to the polyline case.Definition3.1Let and let be a curve in.Then the columnis defined by,where denotes the Minkowski sum operator.The column is the volume swept by in the work-time space as its ref-erence point follows the curve.In our application,the curve describes the translational motion of an obstacle and is therefore time-monotone.A point belongs to if and only if covers the point at time.Definition3.2Let be a square centered at the origin,having side length. Then.The Minkowski sum encloses.No point in has a distance larger than to.We denote the arrangement of the boundaries of the grown obstacle columns by.We will show that this arrangement is of complexity.Let us for a moment consider afixed obstacle at afixed time.We consider the boundary of the grown obstacle.Now,if the reference point of the robot is placed outside,the robot cannot collide with the obstacle.If the reference point of the robot is inside the grown obstacle,there might be configurations in which the the robot intersects the obstacle.Since both the robot and the obstacles have constant complexity,the arrangement of constraint hypersurfaces in at time,when lifted into the configuration space,has constant complexity as well.We exploit this observation to build a partition of the base space.We say that an obstacle is in the proximity of another obstacle if and intersect,hence and intersect.Theorem3.3The complexity of the arrangement of the boundaries of the grown obstacle columns is.Proof:The complexity of the arrangement is determined by the number of ver-tices.A vertex results from an intersection of three columns.A necessary conditionfor three columns to intersect is that the corresponding obstacles are less than apart at some moment in time.We show that the number of such triples is.We charge each such triple to a pair of obstacles.For this we choose the smallest obsta-cle of the three and the one(of the remaining two)that last entered’s proximity. Assume that an obstacle enters the proximity of.(Note that can enter’s proximity at most times because and have constant complexity and both move along line paths.)A third obstacle involved in a triple must al-ready be in the proximity of at the time of arrival of in order to be charged to the pair(,).By Property2.1,there are only larger obstacles in’s proximity at any time,so is chosen from a set of size.As a result,only triples are charged to each of the pairs(,).Each of these triples contribute a constant number of vertices to because the obstacles,, and have constant complexity and move along line paths.Therefore,the complex-ity of is bounded by.It is easy to see that a2-face of a column in thefinal arrangement is divided into a number of parts,of which some are non-convex.The following theorem states that the 2-faces of the arrangement are polygons without holes.This property turns out to be important in the sequel.Theorem3.4The faces of are polygonal and have no holes.Proof:The faces of are formed by the possibly intersecting faces of the columns.Since the columns are polyhedra,the arrangement has polygonal faces.It remains to prove that the faces do not contain holes.A face of the arrangement has a hole iff a column penetrates the interior of this face without intersecting its boundary.We distinguish the bottom and top faces and the side faces of the columns.The bottom and top faces of the columns,i.e.the intersections of the columns with and,are the boundaries of the Minkowski sums of the obstacles at their positions at and and.A grown obstacle cannot be fully contained in another grown obstacle,otherwise the obstacles would also intersect, which is not the case.Therefore,the top and bottom faces of columns are faces without holes.The side faces of the columns are the possibly intersecting walls that connect the top and bottom faces of the columns.Assume,for a contradiction,that(a part of)some side face of has a hole.There must be another columnwhich intersects this face.We call the smallest time coordinate of the hole,and the largest time coordinate.Note that.Without loss of generality, wefix object,such that its speed becomes zero,and adjust the speed of the other objects accordingly.After this transformation,we consider the2-dimensional vertical projection onto the workspace of and(i.e.at and respectively.See Figure1).Note that and are grown using the same square.It is easy to see that,dependent on the location of the obstacle with respect to the projection of,intersects at or which is impossible by assumption.So,the faces of are polygonal and have no holes.If we extend the setting to the case in which obstacles translate along polylines, the complexity of the arrangement does not increase asymptotically—inFigure1:The2-dimensional scene with the dark grey area depicting the intersection of two obstacles at.the proof of Theorem3.3,the chargings to the obstacle pair caused by obstacle are,in the worst case,multiplied by a constant factor.Unfortunately,the2-faces of are no longer polygons without holes.We can resolve this by adding extra faces to the arrangement.For every time at which one of the obstacles changes speed,we add a plane.This way,the area between two successive planes is a work-time space slice where all obstacles move in afixed direction with afixed speed. The arrangements on the newly introduced planes are cross sections of the work-time space.They are arrangements of possibly intersecting grown obstacle boundaries and have linear complexity because the obstacles statisfy the low obstacle density property at any time[11].We compute a triangulation of these2-dimensional arrangements to assure that their faces have no holes.Since we have polyline vertices,the total added complexity is.We will show that every cylinder,defined by a3-cell of the arrange-ment,is intersected by a constant number of constraint hyper-surfaces.We define the coverage of a region.Definition3.5In words,the coverage of a region is the set of obstacles whose columns,which are computed after growing the obstacles,intersect the region.The following result follows from the low density property and the observation that all points in a single 3-cell of the arrangement of column boundaries lie in exactly the same collection of columns.Lemma3.6The regions,defined by the cells of have.Lemma3.7shows that the partition of the base space into regions with is a cylindrical partition.The proof is very similar to the proof of Lemma3.6 of Van der Stappen et al.[11]and has been omitted.Lemma3.7Let be such that.ThenThe only problem is that the complexity of the cells of is not necessarily constant.So,we must refine the partition to create constant complexity subcells.This is discussed in Section4.gadget with fencesFigure 2:The quadratic lowerbound construction.3.3Complexity of the Free SpaceIn the previous subsection we showed that the work-time space of the robot can be partitioned into regions with total combinatorial complexity .Furthermore,by Lemmas 3.6and 3.7,each region,when lifted into the configuration-time space is inter-sected by at most a constant number of constraint hyper-surfaces of bounded algebraic degree.Therefore,a decomposition of the configuration space into free and forbid-den cells of combinatorial complexity exists.Obviously,this bound is an upper bound on the complexity of the free space for our dynamic motion planning setting.Theorem 3.8The complexity of the free space of the dynamic low obstacle densitymotion planning problem is.We will now demonstrate that this bound is worst-case optimal,even in the situation where the robot is only allowed to translate and the obstacles move along lines.To this end,we give a problem instance with obstacles,for which any path for the robot has complexity.Consider the workspace in Figure 2.The grey rectangular robotmust translate from positionto .The gadget in the middle forces the robot to make moves to move from left to right.It can easily be constructed fromstationary obstacles.The big black obstacle at the bottom right moves very slowly to the right.So it takes a long time before the robot can actually get out of the gadget to go to its goal.Now a small obstacle moves from the left to the right,through the gaps in the middle of the gadget.This forces the robot to go to the right as well.Only there can it move slightly further up to let the obstacle pass.But then a new obstacle comes from the right through the gaps,forcing the robot to move to the left of the gadget to let the obstacle pass above it.This is repeated times after which the big obstacle is finally gone and the robot can move to its goal.The robot has to move times through the gadget,each time making moves,leading to a total of moves.As ,the total number of moves is .It is easily verified that at any moment the low obstacle density property is satisfied.Theorem 3.9The complexity of the free space of the dynamic low obstacle density motion planning problem for a translating robot is .Actually,the example shows a much stronger result.Not only does it give a bound on the complexity of the free space,but also on the complexity of a single cell in the free space and on the complexity of any dynamic motion planning algorithm. Theorem3.10The complexity of any algorithm for the dynamic low obstacle density motion planning problem(even for a translating robot)is lower bounded by.4Decomposing the Base SpaceWe still need to decompose the arrangement of columns into constant complexity subcells.To this end,we construct a vertical decomposition of the arrangement.Since the vertical decomposition refines the cells of the arrangement, the subcells of thefinal decomposition still have constant-size coverage.The approach we use[1]requires that the columns in the work-time space,as described in Section 3.2,are in general position.This can be achieved by an appropriate perturbation of the vertices of the columns.Before we can calculate a vertical decomposition we have to triangulate the2-faces of the columns.Triangulation does not increase the asymptotic complexity of the arrangement.After triangulation,the2-faces of the arrangement might coincide,though.It is easily verified that the vertical decomposition algorithm still works with these introduced degeneracies.To bound the space we add two hori-zontal planes at time and(the start and goal time)and only consider the area in between.To bound the space in the-and-direction we also add a triangular prism far around the relevant region of the work-time space.4.1The Vertical DecompositionLet be a set of possibly intersecting triangles in3space.The vertical decomposition of the arrangement decomposes each cell of into subcells,and is defined as follows(see[1]):from every point on an edge of—this can be a part of a triangle edge or of the intersection of two triangles—we extend a vertical ray in positive and negative-direction to thefirst triangle above and thefirst triangle below this point.This way we create a vertical wall for every edge,which we call a primary wall.We obtain a multi-prismatic decomposition of into subcells, the multi-prisms,with a unique polygonal bottom and top face;the vertical projections of both faces are exactly the same.However,the number of vertical walls of a cylinder need not be constant and the cylinder may not be simply connected.We triangulate the bottom face as in the planar case.The added segments are extended upward vertically until they meet the top face.The walls thus erected are the secondary walls.Each subcell of the vertical decomposition is now a box with a triangular base and top, connected by vertical walls.(Note that,for navigation purposes,our notion of vertical decomposition is slightly different from other notions of vertical decomposition that construct secondary walls using a planar vertical decomposition of the projections of the top and bottom faces.)Theorem4.1The vertical decomposition of the arrangement in the work-time space consists of constant complexity subcells,and can be com-puted in time.Proof:Tagansky[9]proved that the vertical decomposition of the entire arrange-ment of a set of triangles in I R consists of subcells where is the complexity of the arrangement.Application of this result to the arrangement of grown obstacle column boundaries,which satisfies,yields the complexity bound.We can compute the vertical decomposition using an algorithm by De Berg et al.[1].This algorithm runs in time,where is the combi-natorial complexity of the vertical decomposition.As,the bound follows.To faciliate navigation,we want each subcell to have a constant number of neighbors. The common boundary of a subcell and one of its neighbors can be a secondary wall,a primary wall,or a2-face of the arrangement.It is easy to see that the number of neighbors sharing a primary or a secondary wall with is bounded by a constant.Let us now consider the maximum number of neighbors,sharing a part of a triangle of with.Unfortunately the arrangements of walls ending on the top and bottom side of the triangle can be very different,and can in general be as complex as the complexity of the full decomposition which is only upper-bounded by.Simply connecting the subcells at the top of the triangle to the subcells at the bottom of the triangle could result in a number of neighbors that is hard to bound by anything better than for each subcell.However,as we will show,we can connect the subcells at the top and bottom of a face by a symbolic, infinitely thin tetrahedralization.This tetrahedralization will increase the combina-torial complexity of the vertical decomposition by a factor of at most,but assures that the number of neighbors per subcell is bounded by a constant.Since this method is quite complicated,we dedicate the following subsection to it.This will lead to the following result:Theorem4.2There exists a cylindrical decomposition of the base space for the dy-namic low obstacle density motion planning problem consisting ofconstant complexity subcells and a constant number of neighbors per subcell.This decomposition can be computed in time.4.2Tetrahedralizing between PolygonsTo reduce the number of neighbors of the subcells we will extend the vertical decom-position with a symbolic connecting structure,that increases the total combinatorial complexity of the vertical decomposition by a factor of.As a result,the number of neighbors per subcell of the cell decomposition with the connecting struc-ture will be bounded by a constant.For each face of the arrangement, this structure connects the subcells at the top side with the subcells at the bottom side. The structure we use is a symbolic,infinitely thin tetrahedralization.To simplify the discussion,we assume that the face for which we construct the connecting structure is horizontal.(This is not a constraint,but just a matter of definition.)Throughout this section the vertical direction is parallel to the normal of the face.Both the top and the bottom side of the face contain a triangulated2-dimensional arrangement,say and,created by the intersecting faces and the walls that end on it.Such triangulations with extra vertices in their interior are referred to as Steinertriangulations;the extra vertices are called Steiner points.The arrangements and are normally different;they do not share Steiner points.We separate the top and bottom of every face in the arrangement.Imagine that the top of the face is at height and the bottom at height.We tetrahedralize the space between the top and bottom arrangement,by adding a number of Steiner points between the top and bottom face. (Remember that this is only done in a symbolic way.In reality,the top and bottom face lie in the same plane.The vertical distance is only used to define the adjacencies of the added(flat)subcells.)We distinguish between the convex and the non-convex faces.Note that non-convex faces indeed exist,since a column can cut out a part of another column.The-orem3.4gives us that the cut out parts are never strictly included in the open interior of a2-face of a column.Wefirst show how to tetrahedralize the space between two different Steiner triangulations and of the same convex simple polygon.Our tetrahedralization has two layers joined at height by a Steiner triangulation of.This triangulation has one Steiner point:is triangulated using a star of edges from to all vertices of.Both and are different Steiner triangulations of the same polygon,therefore the vertical projections of the boundaries of and are equivalent.We tetrahedralize between and by adding a face from every edge of to.The result is a tetrahedralized pyramid where each tetrahedron corresponds to a triangle of.To triangulate the complement of this pyramid in the layer between and,we connect the boundaries of and by vertical faces between the boundary edges.For every face introduced by connecting the boundaries,we add a Steiner point in the middle of.We connect to all vertices on and connect each resulting triangle to (see Figure3).These triangles complete the tetrahedralization of the space between and.The tetrahedralization between and is constructed in the same way. It is easy to see that the number of tetrahedra created is linear in the complexity of the triangulations and.Unfortunately,faces need not be convex.So we must also show how to tetrahe-dralize the space between two different Steiner triangulations of the same non-convex simple polygon.(As indicated above we know that the polygon has no holes.This is crucial here.)We again add a Steiner triangulation of between and.In the non-convex case we have to use a more sophisticated Steiner triangulation.For this we use a triangulation by Hershberger and Suri[4]that was originally designed for ray shooting in simple polygons.This triangulation has three important properties.Let be the number of edges of:1.It introduces Steiner points with each Steiner point directly connected tothe boundary of by at least one triangulation edge;2.Every line segment that lies inside intersects at most triangles of;3.The triangulation can be computed in time.We can derive the following lemma from the properties of.Lemma4.3Let be a polygon with vertices and without holes,Let be the triangulation of as described in[4].Let be a triangle inside,and let be the。

埃伦费斯定理

埃伦费斯定理

It is often said that Ehrenfest’s theorem shows that expectation values obey the classical dynamical laws. This slogan is not quite true. In particular, the expectation value of position does not necessarily obey Newton’s second law. A true version of Newton’s second law for the expectation value would read
Harmonic Oscillator: Definitions, the Hamiltonian We now begin a survey of key properties of one of the workhorse models of quantum
mechanics: the simple harmonic oscillator (SHO). This model is useful because it is analytically quite tractable, it illustrates a wide variety of quantum mechanical features both
and use the time independence of the state vector to obtain (exercise)
d2 dt2 X (t) = F (t), where F is the force. This result is Ehrenfest’s theorem.

数学会七十周年年会会议日程.

数学会七十周年年会会议日程.

数学会七十周年年会会议日程7月24日8:00 --- 21:00 大会注册7月25日9:00 --- 11:30 开幕式12:00 午餐(国际学术交流中心) 14:00---16:00 大会报告16:20---17:50 分组报告18:00 晚餐(代表所在宾馆)7月26日8:00 --- 10:00 大会报告10:20---11:50 分组报告12:00 午餐(国际学术交流中心) 14:00---18:00 分组报告18:00 晚宴(国际学术交流中心)7月27日8:00 --- 10:00 大会报告10:20---11:50 分组报告12:00 午餐(国际学术交流中心) 14:00---18:00 游览刘公岛18:00 晚餐(代表所在宾馆)7月28日8:00 --- 10:00 大会报告10:20---11:50 分组报告12:00 午餐(国际学术交流中心)14:00---17:15 分组报告18:00 晚餐(代表所在宾馆)7月29日8:00---9:30 分组报告10:00---12:00 15-分钟报告12:00 午餐(国际学术交流中心)14:00---14:45 分组报告15:00---16:00 大会报告16:20 闭幕式18:00 晚餐(代表所在宾馆)数学会七十周年年会报告日程7月25日上午9:00 --- 11:00 大会开幕式主持人:11:00---11:30 全体代表合影7月25日下午14:00---16:00 大会报告主持人:报告人: Shing-Tung Yau (丘成桐)报告题目: Geometric Analysis报告人: Martin Groetschel报告题目: Mathematical Aspects of Public Transport16:20---17:50 分组报告第一组主持人: 报告时间报告人报告题目16:20---17:05 Ming-Chang Kang康明昌Noether’s Problem for non--abelian Groups17:05---17:50 Jing Yu于靖Recent progress on transcendence overfunction fields第二组主持人: 报告时间报告人报告题目16:20---17:05 Xiu-Xiong Chen陈秀雄Geometry of foliation by holomorphic discs andits application in Kaehler geometry17:05---17:50 Xiao-Hua Zhu朱小华Recent development in Kähler-Ricci flow第三组主持人: 报告时间报告人报告题目16:20---17:05 You-Jin Zhang张友金Classification of Bihamiltonian IntegrableHierarchies and Frobenius Manifolds17:05---17:50 Xian-Zhe Dai戴先哲On the Stability of Kähler-Einstein Metrics第四组主持人: 报告时间报告人报告题目16:20---17:05 Tao Tang汤涛Moving Mesh Methods for Computational FluidDynamics17:05---17:50 Shi Jin金石Hamiltonian-preserving schemes for the Liouvilleequation with discontinuous Hamiltonians第五组主持人: 报告时间报告人报告题目16:20---17:05 Zi-Kun Wang王梓坤Infinite Dimensional(γ,δ)-OU Process WithMulti-parameters17:05---17:50 Louis Chen陈晓云Stein's method and its diverse applicability7月26日上午8:00---10:00 大会报告主持人:报告人: Efim I Zelmanov报告题目: Abstract Infinite Groups报告人: Yum-Tong Siu (肖荫堂)报告题目: Algebraic Geometry and Estimates of the Cauchy-Riemann Operator10:20---11:50 分组报告第一组主持人:报告时间报告人报告题目10:20---11:05 Jie-Tai Yu余解台Automorphic Conjugacy Problems ForPolynomial and Free Algebras11:05---11:50 Chao-Hua Jia贾朝华Approximation to Irrational Number by RationalNumbers with Prime Denominators第二组主持人: 报告时间报告人报告题目10:20---11:05 Xiao-Chun Rong戎小春Positively curved manifolds with symmetry11:05---11:50 Xiao-Song Lin林晓松Hyperbolic Geometry and Quantum Topology第三组主持人: 报告时间报告人报告题目10:20---11:05 Gui-Qiang Chen陈贵强Divergence-Measure Fields, GeometricMeasures, and Conservation Laws11:05---11:50 Yan-Yan Li李岩岩Liouville theorems and isolated singularity第四组主持人: 报告时间报告人报告题目10:20---11:05 Jie Shen沈杰Fast Spectral-Galerkin Method: Algorithms,Analysis and Applications11:05---11:50 Qiang Du杜强Phase field modeling of bio-membranes andconnections to the Willmore problem第五组主持人: 报告时间报告人报告题目10:20---11:05 Shi-Ge Peng彭实戈Conditional Nonlinear Expectations NonlinearMarkov Chains and Dynamic Risk Measures11:05---11:50 Bin Yu郁彬Internet Tomograpy7月26日下午14:00---18:00 分组报告第一组主持人: 报告时间报告人报告题目14:00---14:45 Jian-Ya Liu刘建亚Subconvexity and Ramanujan Bounds forAutomorphic L-functions14:45---15:30 Sheng-Li Tan谈胜利On the Riemann-Roch Problem for AlgebraicSurfaces15:30---16:15 Chang-Chang Xi惠昌常On the finitistic dimension conjecture16:15---16:30 休息16:30---17:15 Fei Xu徐飞Spinor genera in characteristic 217:15---18:00 Kai-Ming Zhao赵开明Vertex operator representations of Lie algebras第二组主持人: 报告时间报告人报告题目14:00---14:45 Yue-Fei Wang王跃飞On Mahler Measures14:45---15:30 Ti-Jun Xiao肖体俊Evolution Equations with Dynamic BoundaryConditions15:30---16:15 Chang-Ping Wang王长平Conformal geometry of submanifoldsinLorentzian space16:15---16:30 休息16:30---17:15 Le Dung Trang The problem of Equisingularity第三组主持人: 报告时间报告人报告题目14:00---14:45 Li-Qun Zhang张立群On the regularity of ultraparabolic equations14:45---15:30 Bo Zhang张波Integral Equation Methods for Rough SurfaceScattering15:30---16:15 Yun-Ping Jiang蒋云平Renormalization Methods in Dynamical Systems16:15---16:30 休息16:30---17:15 Xiang-Dong Ye叶向东Local properties of dynamical systems17:15---18:00 Chong-Qing Cheng程崇庆Dynamical Instability of Nearly IntegrableHamiltonian Systems第四组主持人: 报告时间报告人报告题目14:00---14:45 Wei-Nan E鄂维南Mathematical Theory of Solids: From Atomic toMacroscopic Scales14:45---15:30 Chao-Ping Xing邢朝平Packing, Codes and Algebraic Curves15:30---16:15 Ai-Hui Zhou周爱辉Finite Element Computations in QuantumChemistry16:15---16:30 休息16:30---17:15 Yue-Sheng Xu许跃生A Multiparameter Regularization Method forIll-Posed Operator Equations and its pplications17:15---18:00 Ping-Wen Zhang张平文The Structure and Stability of Stationary Solutionsof Doi-Onsager Equation第五组主持人: 报告时间报告人报告题目14:00---14:45 Han-Fu Chen陈翰馥Identification and Adaptive Regulation for aClass of Nonlinear Systems14:45---15:30 Bing Cheng程兵A Structural Theory of Asset Pricing and theEquity Premium Puzzle15:30---16:15 Ji-Feng Zhang张纪峰System Identification Using Set-Valued OutputObservations16:15---16:30 休息16:30---17:15 Yuan Wang王沅On Control System Represented by DifferentialInclusions17:15---18:00 Zhi Geng耿直Algorithms for learning graphical modelswith high dimension variables7月27日上午8:00 --- 10:00 大会报告主持人:报告人: Gang Tian (田刚)报告题目: Geometry and Analysis of 4-mainfolds报告人: John Ball报告题目: Quasiconvexity, compatibility of gradients, and phase transformations 10:20---11:50 分组报告第一组主持人: 报告时间报告人报告题目10:20---11:05 Hao Chen陈豪Quantum entanglement and algebraic geometry11:05---11:50 Qi Feng冯琦The Inner Models Program第二组主持人: 报告时间报告人报告题目10:20---11:05 Li-Ming Ge葛力明On Kadison's Transitive Algebra Problem11:05---11:50 Jing-Song Huang黄劲松From Fourier Series to Harmonic Analysison Lie groups第三组主持人: 报告时间报告人报告题目10:20---11:05 Dao-Min Cao曹道民Multiscale-bump standing waves with a criticalfrequency for nonlinear Schrödinger equations11:05---11:50 Mei-Rong Zhang章梅荣Rotation number approach to eigenvalues andapplications to stability of periodic solutions第四组主持人: 报告时间报告人报告题目10:20---11:05 Hua Chen陈化On the Nonlinear Singular Partial DifferentialEquations and the Summability of FormalSolutions11:05---11:50 Ya-Xiang Yuan袁亚湘Subspace Techniques in Optimization第五组主持人: 报告时间报告人报告题目10:20---11:05 Ai-Hua Fan范爱华Random multiplicative chaos and theirapplications11:05---11:50 Geng-Hua Fan范更华Covering Graphs by Paths and Circuits 7月27日下午游览刘公岛7月28日上午8:00 --- 10:00 大会报告主持人:报告人: Jean Pierre Bourguignon报告题目: Finsler Geometry, rejuvenation of a classical domain报告人: Nick Katz报告题目: L-Functions Over Finite Fields10:20---11:50 分组报告第一组主持人: 报告时间报告人报告题目10:20---11:05 Lian-Gang Peng彭联刚Elliptic Lie algebras and tubular algebras11:05---11:50 Lin-Sheng Yin印林生Some arithmetic properties of several Gammafunctions第二组主持人: 报告时间报告人报告题目10:20---11:05 Ngaiming Mok莫毅明Global extensions of local holomorphic isometries with respect to the Bergman metric11:05---11:50 Hai-Bao Duan段海豹Poincare Conjecture and the Foundation ofGeometry第三组主持人: 报告时间报告人报告题目10:20---11:05 Bin Liu柳彬Quasi-perodic Motions in Nonlinear Oscillations11:05---11:50 Yun-Guang Lu陆云光Global Weak Solutions for Gas Dynamics Systemand Related Systems第四组主持人: 报告时间报告人报告题目10:20---11:05 Yong-Chuan Chen陈永川Proving Nonterminating q-Identities by theq-Zeilberger Algorithm11:05---11:50 Fa-Lai Chen陈发来Applications of ComputationalAlgebraicGeometry in Geometric Modelling第五组主持人:10:20---11:05 Hong-Bo Li李洪波On Symbolic Geometric Computation withConformal Geometric Algebra11:05---11:50 Hai-Jun Huang黄海军从用户均衡(UE)到系统最优(SO)—潜力的上界7月28日下午14:00---17:15 分组报告第一组主持人: 报告时间报告人报告题目14:00---14:45 Ji-Ping Zhang张继平Kronecker equivalence of fields and groupcoverings14:45---15:30 Yun Gao郜云Free fields and hermitian representations ofextended affine Lie algebras15:30---15:45 休息15:45---16:30 Guo-Ping Tang唐国平Higher K-theory of group-rings of virtuallyinfinite cyclic groups16:30---17:15 Hou-Rong Qin秦厚荣The Rank of K2 of elliptic curves第二组主持人: 报告时间报告人报告题目14:00---14:45 An-Min Li李安民Relative Gromov-Witten invariants andapplications14:45---15:30 Xiang-Yu Zhou周向宇Some problems arising from the extended futuretube conjecture15:30---15:45 休息15:45---16:30 Sen Hu胡森G-structures and calibrated geometries第三组主持人:14:00---14:45 Lan Wen文兰Generic dynamics away from homoclinicbifurcations14:45---15:30 You-De Wang王友德Schrodinger Flows and Some Related Problems15:30---15:45 休息15:45---16:30 Yi-Min Long龙以明Multiple closed geodesics on Finsler 2-spheres16:30---17:15 Jian-Gong You尤建功Infinite dimensional KAM theory and itsapplications in Hamiltonian PDEs第四组主持人: 报告时间报告人报告题目14:00---14:45 Zhi-Ming Chen陈志明Upscaling of a class of nonlinear parabolicequations for the flow transport in heterogeneousporous media14:45---15:30 Chuan-Ming Zong宗传明The Deep Holes and Free Planesin Lattice Sphere Packings15:30---15:45 休息15:45---16:30 Lei Hu胡磊Cryptography based on Weil and Tate pairingsover elliptic curves第五组主持人: 报告时间报告人报告题目14:00---14:45 Fu-Zhou Gong巩馥洲Exponential Ergodicity, Spectral gap, and TheirApplications14:45---15:30 Li-Ming Wu吴黎明Large deviations and essential spectral radius ofMarkov processes15:30---15:45 休息15:45---16:30 Dong-Ming Wang王东明Doing Mathematics by Computer16:30---17:15 Zeng-Jing Chen陈增敬Risk measures and g-expectations7月29日上午8:00---9:30 分组报告第一组主持人: 报告时间报告人报告题目8:00---8:45 Xiao-Tao Sun孙笑涛Chern classes of moduli spaces of stable bundles8:45---9:30 Jie Xiao肖杰Derived categories and Lie algebras第二组主持人: 报告时间报告人报告题目8:00---8:45 Tao Li李韬Heegaard surfaces and measured laminations8:45---9:30 Jing-Zhong Zhang张景中中学数学课程新思路第三组主持人: 报告时间报告人报告题目8:00---8:45 Jian-Fu Yang杨健夫Spike-layered solutions for an elliptic system withNeumann boundary conditions8:45---9:30 Ke-Wei Zhang张克威One-Dimensional Forward-Backward DiffusionEquations and the Partial Differential InclusionMethod第四组主持人: 报告时间报告人报告题目8:00---8:45 Li Yuan袁礼Numerical simulation of high speed chemicallyreacting gas flow8:45---9:30 Zong-Min Wu吴宗敏Meshfree Scattered dataquasi-interpolation10:00---12:00 15-分钟报告第一组主持人: 报告时间报告人报告题目10:00---10:15 Jian Zhang张健On Nonlinear Schrödinger Equations withHarmonic Potentia l10:15---10:30 Yun-Qing Huang黄云清有限元格式的相容性与超收敛10:30---10:45 Jian-Zhong Zhang张建中A superlinearly convergent algorithm forvariational and quasi-variational inequalities10:45---11:00 Zheng Wang王政高速碰撞问题二维有限元数值模拟研究11:00---11:15 休息11:15---11:30 Zhong-Kui Liu刘仲奎广义幂级数环11:30---11:45 Gang Xu徐刚建设大学数学优质教学资源努力提高大学数学教学质量11:45---12:00 Guo-Zhen Hou候国珍中华内算拓展出“量子数论十八境”第二组主持人: 报告时间报告人报告题目10:00---10:15 Wei Wu吴微Recent Developments on Convergence of OnlineGradient Methods for Neural Network Training10:15---10:30 Xing Li李星Anti-plane moving Yoffe-crack problem in a stripof functionally graded piezoelectric materials10:30---10:45 Hong-Qing Zhang张鸿庆The arithmetic model in mechanization ofmathematical physics10:45---11:00 Zhen-Xiang Zhang张振祥Finding C3 - Strong Pseudo primes11:00---11:15 休息11:15---11:30 Yao-Jun Liu刘耀军Semigroup method in combinatorics on word11:30---11:45 Xian-Bei Liu刘先蓓Finding solutions to the congruence2n-2 = 1 mod n11:45---12:00 Chun-Gang Ji纪春岗Pell Equations, odd graphs and thrie applications第三组主持人: 报告时间报告人报告题目10:00---10:15 Wen-Peng Zhang张文鹏On a Conjecture of the Euler Numbers10:15---10:30 Ji-Bin Li李继斌On the Study of Solutions of Singular NonlinearTravelling Wave Equations: Dynamical SystemApproach10:30---10:45 Yun-Fei Yao姚云飞数学分析的教改与实践10:45---11:00 Yi-Bo Wang王一博高精度欧拉方法数值模拟流体不稳定性11:00---11:15 休息11:15---11:30 Peng-Zhan Qu屈鹏展三元数系11:30---11:45 Ji-Hui Zhang张吉慧Existence results for some fourth-ordernonlinear elliptic problems第四组主持人: 报告时间报告人报告题目10:00---10:15 Zhen-Qi Li黎镇琦Spectrum of the Laplacian on the Cartan'sMinimal Isoparametric Hypersurface in S410:15---10:30 Peng-Cheng Niu钮鹏程A Note on a Geometric Maximum Principle forthe Generalized Greiner Operator10:30---10:45 Jin Liang梁进Higher order degenerate Cauchy problems inlocally convex spaces10:45---11:00 Zhi-Zhong Sun孙志忠无界域上薛定根方程的数值求解及其理论分析11:00---11:15 休息11:15---11:30 Jing-An Cui崔景安Spreading Disease with Transport-RelateInfection11:30---11:45 Gong-Sheng Li李功胜Conditional Stability For An Inverse SourceProblem In A Transport Equation第五组主持人: 报告时间报告人报告题目10:00---10:15 Zhi-Da Huang黄志达地下水在层状多孔介质中渗流的数学问题10:15---10:30 Gui-Yun Chen陈贵云On Thompson’s Conjecture and Related Topcs10:30---10:45 Jia-Zu Zhou周家足Integral geometry and geometric inequalities inhomogeneous space10:45---11:00 Heng Yong勇珩A Rezone Strategy of Adaption to Variationof Flow Variables11:00---11:15 休息11:15---11:30 Jin-Ru Chen陈金如Mortar-type mixed Q rot1/Q0 element method andits multigrid method for the incompressibleStokes problem*11:30---11:45 Yu-Shun Wang王雨顺Local conservation schemes11:45---12:00 Xiu-Rang Qiao乔修让Some proofs of 4-color Theorem7月29日下午14:00---14:45分组报告第一组主持人:报告时间报告人报告题目14:00---14:45 Chong-Ying Dong董崇英On the uniqueness of the mooshine vertexoperator algebra第二组主持人:报告时间报告人报告题目14:00---14:45 Jia-Yu Li李嘉禹The mean curvature flow approach to theexistence of holomorphic curves in K-E manifolds第三组主持人:报告时间报告人报告题目14:00---14:45 Zhou-Ping Xin辛周平Some progress in the Mathematical Theoryof Fluid Dynamics第四组主持人:报告时间报告人报告题目14:00---14:45 Jin-Chao Xu许进超New Numerical Techniques for Non-NewtonianModels15:00---16:00 大会报告主持人:报告人: Noga Alon报告题目: Approximation algorithms and Grothendieck type inequalities16:20 闭幕式主持人:PUBLIC TALKS公众演讲Speaker:Lo Yang (杨乐)Time:9:00 July 26 , 2005Title:改革开放以来的中国数学会Speaker:Zhi-Ming Ma (马志明)Time:9:00 July 27 , 2005Title:Google搜索与Inter网的数学Speaker:Zhong-Ci Shi (石钟慈)Time:9:00 July 28 , 2005Title:中国计算数学五十年1.The 6th Meeting of the 9th CMS Steering Council 中国数学会第九届常务理事会议Time:20:00 July 26, 20052.The 2nd Meeting of the 9th CMS Council中国数学会第九届二次理事会议Time:20:00 July 27, 20053.President Forum of Mathematical Schools in China 院长论坛Time:20:00 July 28, 2005。

光谱层英文版

光谱层英文版

光谱层英文版The Spectral Layer: Unveiling the Invisible RealmThe universe we inhabit is a tapestry of intricately woven elements, each thread contributing to the grand tapestry of existence. Amidst this intricate web, lies a realm that is often overlooked, yet holds the key to unlocking the mysteries of our reality. This realm is the spectral layer – a realm that transcends the boundaries of our visible world and delves into the unseen realms of energy and vibration.At the heart of the spectral layer lies the electromagnetic spectrum –a vast and diverse range of wavelengths and frequencies that encompass the entirety of our physical world. From the low-frequency radio waves to the high-energy gamma rays, the electromagnetic spectrum is the foundation upon which our understanding of the universe is built. It is within this spectrum that we find the familiar visible light, the spectrum of colors that we perceive with our eyes, but it is only a small fraction of the vast and diverse tapestry that makes up the spectral layer.Beyond the visible spectrum, there lies a realm of unseen energies that are integral to the very fabric of our existence. Infrared radiation, for instance, is a form of electromagnetic radiation that is invisible to the human eye but plays a crucial role in the transfer of heat and the functioning of various biological processes. Similarly, ultraviolet radiation, though invisible to us, is essential for the production of vitamin D and the regulation of circadian rhythms.But the spectral layer extends far beyond the confines of the electromagnetic spectrum. It is a realm that encompasses the vibrations and frequencies of all matter and energy, from the subatomic particles that make up the building blocks of our universe to the vast cosmic structures that span the vastness of space. These vibrations and frequencies, though often imperceptible to our senses, are the foundation upon which the entire universe is built.At the quantum level, the spectral layer reveals the true nature of reality. Subatomic particles, such as electrons and quarks, are not merely static entities but rather dynamic oscillations of energy, each with its own unique frequency and vibration. These vibrations, in turn, give rise to the fundamental forces that govern the behavior of matter and energy, from the strong nuclear force that holds the nucleus of an atom together to the mysterious dark energy that drives the expansion of the universe.But the spectral layer is not merely a realm of the infinitely small. It also encompasses the vast and expansive structures of the cosmos, from the intricate patterns of galaxies to the pulsing rhythms of celestial bodies. The stars that dot the night sky, for instance, are not merely points of light but rather vast nuclear furnaces, each emitting a unique spectrum of electromagnetic radiation that can be detected and analyzed by scientists.Through the study of the spectral layer, we have gained unprecedented insights into the nature of our universe. By analyzing the spectra of distant galaxies, for example, we can determine their chemical composition, their age, and even their rate of expansion –information that is crucial for our understanding of the origins and evolution of the cosmos.But the spectral layer is not just a realm of scientific inquiry – it is also a realm of profound spiritual and metaphysical exploration. Many ancient and indigenous cultures have long recognized the importance of the unseen realms of energy and vibration, and have developed sophisticated systems of understanding and interacting with these realms.In the traditions of Hinduism and Buddhism, for instance, the concept of the chakras – the seven energy centers that are believed to govern various aspects of our physical, emotional, and spiritualwell-being – is a manifestation of the spectral layer. These energy centers are believed to be connected to specific frequencies and vibrations, and the practice of chakra meditation and balancing is seen as a way to align oneself with the natural rhythms of the universe.Similarly, in the traditions of shamanism and indigenous healing practices, the concept of the "spirit world" or the "unseen realm" is closely tied to the spectral layer. Shamans and healers are often said to be able to perceive and interact with the unseen energies that permeate our world, using techniques such as drumming, chanting, and plant medicine to access these realms and bring about healing and transformation.In the modern era, the spectral layer has become the subject of intense scientific and technological exploration. From the development of advanced imaging technologies that can reveal the unseen structures of the human body to the creation of sophisticated communication systems that harness the power of the electromagnetic spectrum, the spectral layer has become an essential component of our understanding and manipulation of the physical world.Yet, despite the immense progress we have made in our understanding of the spectral layer, there is still much that remainsunknown and mysterious. The nature of dark matter and dark energy, for instance, remains one of the greatest unsolved puzzles in modern physics, and the true nature of consciousness and the relationship between the physical and the metaphysical realms continues to be a subject of intense debate and exploration.As we continue to delve deeper into the spectral layer, we may uncover even more profound insights into the nature of our reality. Perhaps we will discover new forms of energy and vibration that have yet to be detected, or perhaps we will find that the boundaries between the seen and the unseen are far more permeable than we ever imagined. Whatever the future may hold, one thing is certain: the spectral layer will continue to be a source of fascination, inspiration, and mystery for generations to come.。

一个椭球在玻璃中的动力学 Dynamics of a rod in a homogeneous inhomogeneous frozen disordered medium

一个椭球在玻璃中的动力学 Dynamics of a rod in a homogeneous inhomogeneous frozen disordered medium

a r X i v :c o n d -m a t /0605227v 1 [c o n d -m a t .s o f t ] 9 M a y 2006Dynamics of a rod in a homogeneous/inhomogeneous frozen disordered medium:Correlation functions and non-Gaussian effectsAngel J.Moreno 1,2and Walter Kob 31Laboratoire des Verres.Universit´e Montpellier 2.Place 069.F-34095Montpellier,France.2Present address:Donostia International Physics Center,Paseo Manuel de Lardizabal 4,E-20018San Sebasti´a n,Spain.3Laboratoire des Collo ¨ides,Verres et Nanomat´e riaux,Universit´e Montpellier 2,F-34095Montpellier,FranceWe present molecular dynamics simulations of the motion of a single rigid rod in a disorderedstatic 2d-array of disk-like obstacles.Two different configurations have been used for the latter:A completely random one,and which thus has an inhomogeneous structure,and an homogeneous “glassy”one,obtained from freezing a liquid of soft disks in equilibrium.Small differences are observed between both structures for the translational dynamics of the rod center-of-mass.In contrast to this,the rotational dynamics in the glassy host medium is strongly slowed down in comparison with the random one.We calculate angular correlation functions for a wide range of rod length L and density of obstacles ρas control parameters.A two-step decay is observed for large values of L and ρ,in analogy with supercooled liquids at temperature close to the glass transition.In agreement with the prediction of the Mode Coupling Theory,a time-length and time-density scaling is obtained.In order to get insight on the relation between the heterogeneity of the dynamics and the structure of the host medium,we determine the deviations from Gaussianity at different length scales.Strong deviations are obtained even at spatial scales much larger than the rod length.The magnitude of these deviations is independent of the nature of the host medium.This result suggests that the large scale translational dynamics of the rod is affected only weakly by the presence of inhomogeneities in the host medium.Ref.:AIP Conference Proceedings 708(2004)576-582I.INTRODUCTIONSince it was initially introduced by Lorentz as a model for the electrical conductivity in metals [1],the prob-lem of the Lorentz gas has given rise to a substan-tial theoretical effort aimed to understand its proper-ties [2,3,4,5,6,7,8].In this model,a single classical particle moves through a disordered array of static ob-stacles.It can thus be used as a simplified picture of the motion of a light atom in a disordered environment of heavy particles having a much slower dynamics.In the simplest case,where the diffusing particle and the obsta-cles are modeled as hard spheres,an exact solution for the diffusion constant exists in the limit of low densities of obstacles [9].However,the problem becomes highly non-trivial with increasing density,where dynamic cor-relations and memory effects start to become important for the motion of the diffusing particle,and the system shows the typical features of the dynamics of supercooled liquids or dense colloidal systems,such as a transition to a non-ergodic phase of zero diffusivity [2,3,4,6].In par-ticular,diffusion constants and correlation functions are non-analytical functions of the density [2,4,7,8].More-over,correlation functions show non-exponential long-time decays.Diffusing particles and obstacles are generally modeled as disks or spheres in two and three dimensions,respec-tively.Much less attention has been paid to systems that have orientational degrees of freedom.Motivated by this latter question,we have recently started an investigation,at low and moderate densities of obstacles,on a general-ization of the Lorentz gas,namely a model in which the diffusing particle is a rigid rod [10].An array of randomlydistributed disks has been used for the host medium.For simplicity simulations have been done in two dimensions,reducing the degrees of freedom to the center-of-mass po-sition and the orientation of the rod axis.As in the case of supercooled liquids [11,12,13,14,15]or dense col-loidal systems [16,17,18],one observes at intermediate times a caging regime for the rod center-of-mass motion,which is due to the steric hindrance produced by the presence of the neighboring obstacles.More interestingly,strong deviations from Gaussian-ity have been obtained for the incoherent intermediate scattering function at wavelengths much longer than the rod length,giving evidence for a strongly heterogeneous character of the long-time dynamics at such length scales.The inhomogeneous structure of the model used for the host medium has been pointed out as a possible origin of such non-Gaussian effects since a random configuration features large holes on one side but on the other side also dense clusters of obstacles.The presence of holes might lead to a finite probability of jumps that are much longer than the average “jump length”and thus to a heteroge-neous dynamics.In order to shed new light on this question,we have re-peated the simulations by taking a disordered but homo-geneous configuration of the obstacles instead of a ran-dom one.We will see,however,that large scale non-Gaussian effects are not significantly affected by the par-ticular choice of the configuration of the obstacles.Some information on angular correlation functions is also pre-sented.The article is organized as follows:In Section II we present the model and give details of the simulation.Translational and angular mean-squared displacements are presented in Section III.The behavior of angular cor-2relation functions is shown and discussed in Section IV, in terms of the Mode Coupling Theory.Non-Gaussian ef-fects in the random and glassy models of the host medium are investigated in Section V.Conclusions are given in Section VI.II.MODEL AND DETAILS OF THESIMULATIONThe rigid rod,of mass M,was modeled as N aligned point particles of equal mass m=M/N,with a bond length2σ.The rod length is therefore given by L= (2N−1)σ.The positions of the obstacles in the ran-dom configuration were generated by a standard Pois-son process.In order to obtain the glassy host medium, we equilibrated at a reduced particle densityρ=0.77 and at temperature T=ǫ/k B a two-dimensional ar-ray of point particles interacting via a soft-disk potential V(r)=ǫ(σ/r)12.This procedure produced an homoge-neous liquid-like configuration.The latter was then per-manently frozen and was expanded or shrunk to obtain the desired density of obstacles,defined asρ=n obs/l2box, with n obs the number of obstacles and l box the length of the square simulation box used for periodic boundary conditions.The same soft-disk potential V(r)was used for the interaction between the particles forming the rod and the obstacles.For computational efficiency,V(r)was truncated and shifted at a cutoffdistance of2.5σ.In the following,space and time will be measured in the reduced unitsσand(σ2m/ǫ)1/2,respectively.Typically 600-1000realizations of the ensemble rod-obstacles were considered.The set of rods was equilibrated at T=ǫ/k B.After the equilibration,a production run was done at constant energy and results were averaged over the different realizations.These runs covered106time units, corresponding to(1−5)·108time steps,depending on the step size used for the different rod lengths and densities. Run times were significantly longer than the relaxation times of the system.III.MEAN-SQUARED DISPLACEMENTS Figs.1a and1b show respectively forρ=6·10−3 a comparison between the random and the glassy con-figuration for the mean-squared displacement of the rod center-of-mass (∆r(t))2 and the mean-squared angular displacement (∆Φ(t))2 .Brackets denote ensemble av-erage.In order to facilitate the observation of the differ-ent dynamic regimes,data have been divided by the time t.The comparison covers a wide range of rod lengths from L∼0.1d nn to L∼10d nn,where d nn=ρ−1/2≈13, is the average distance between obstacles for the men-tioned density.At short times the rod does not feel the presence of the neighboring obstacles and (∆r(t))2 and (∆Φ(t))2 show the quadratic time-dependence charac-teristic of a ballistic motion.For the shortest rods a sharp transition to the long-time linear regime is observed.In contrast to this,for long rods a crossover regime between both limits,showing a weaker time dependence that theFIG.1:Mean-squared displacement of the center-of-mass(a) and mean-squared angular displacement(b),both divided by the time t,forρ=6·10−3and different rod lengths,for the random(solid lines)and glassy(dashed lines)configuration of the obstacles.ballistic motion,is present over1-2decades of interme-diate times.Such a crossover corresponds to the well-known caging regime[11,12,13,14,15,16,17,18]ob-served in supercooled liquids or dense colloidal systems. Due to the presence of the neighboring obstacles,the par-ticle is trapped within an“effective cage”for some time until it escapes from it and begins to show a diffusive behavior.No significant differences are observed between the val-ues of (∆r(t))2 for a same rod length in the two dif-ferent,random and glassy,configurations.This is more clearly seen by calculating for each L the ratio D ranCM/D glaCM between the center-of-mass translational diffusion con-stant D CM in both configurations.The latter is calcu-lated as the long-time limit of (∆r(t))2 /4t.As shown in Fig.2,differences in D CM between the random and the glassy host medium are less than a factor1.5.The values of D CM are systematically lower for the glassy host medium,evidencing a stronger backscattering for the diffusion,as intuitively expected from the homoge-neous character of this latter configuration in contrast to the random one(see Fig.3),where the presence of holes facilitates diffusion.It is noteworthy that the maximum difference between the values of D CM in both configu-rations takes place for L∼10−20,i.e.,when the rod becomes longer than d nn.Thus,while in the homoge-neous glassy host medium the rod will not be able to pass transversally between two neighboring obstacles,in the inhomogeneous random configuration this diffusion channel will still be present due to the presence of holesFIG.2:Ratio between the rotational and center-of-masstranslational diffusion constants for the random and theglassy host medium atρ=6·10−3and different rod lengths.sityρ=6·10−3.Left:A random configuration.Right:A homogeneous glassy configuration.Short and long arrowscorrespond,respectively,to distances of40and100.and will only be suppressed for very long rods.Strong differences are observed between both mod-els of the host medium in the case of the rotationaldynamics,as shown in Fig.1b for (∆Φ(t))2 and inFig.2for the ratio of the rotational diffusion constants,D ranR/D gla R.(D R is calculated as the long-time limit of(∆Φ(t))2 /2t.)For rod lengths smaller than d nn the ro-tational dynamics is only weakly sensitive to the config-uration of the medium and the ratio D ranR/D gla R remainsclose to unity.However,for rods longer than d nn the lat-ter ratio strongly increases up to a maximum of∼4.5forL∼40.The position of the maximum can be again ratio-nalized from the inhomogeneous structure of the randomhost medium.Short arrows in the configurations of Fig.3forρ=6·10−3indicate distances of40.From the com-parison between both configurations it is clear that whilein the glassy medium rods of L∼40can perform onlysmall rotations within the tubes formed by the neighbor-ing obstacles,in the random medium they can go into theholes and thus rotate freely over a larger angle.There-fore,the long-time angular displacement will grow upmore quickly than for the motion between narrow tubesin the glassy configuration.It must also be mentionedFIG.4:Correlation function cos m∆Φ(t) for L=99andρ=6·10−3,with m=1,2,3...12(from top to bottom).Datacorrespond to the random host medium.that even for the largest investigated rod length,L=99,where motion between tubes also dominates the diffusionin the random medium,the ratio D ranR/D glaRis still sig-nificantly different from unity.Thus,while in the glassymedium the walls of the tube are formed by uniformlydistributed obstacles,in the random configuration longdistances are allowed between some of the neighboringobstacles forming the tube,leading to additional escap-ing channels(see long arrows in Fig.3).In principle,only for extremely long rods the ratio D ranR/D glaRis ex-pected to approach unity.IV.CORRELATION FUNCTIONSThe Mode Coupling Theory(MCT)[19,20,21,22]makes precise quantitative predictions on the dynamicsof supercooled liquids or dense colloidal systems.In itsidealized version,it predicts a dynamic transition froman ergodic to a non-ergodic phase at some critical valueof the control parameters.These are usually the tem-perature or the density,though in principle the MCTformalism can be generalized to other control parame-ters.Moreover,it has been recently tested in a kind ofsystems very different from liquids or colloids,such asplastic crystals[23],suggesting a more universal charac-ter for this theory.Motivated by this possibilities,we testsome predictions of MCT for the rotational dynamics ofthe rod with L andρas control parameters.According to MCT,upon approaching the criticalpoint,the correlation function shows a two-step decay.The initial decay corresponds to the dynamic transitionfrom the ballistic to the caging regime.As a consequenceof the temporary trapping in the cage formed by theneighboring obstacles,the particle does not lose the mem-ory of its initial position and the correlation functions de-cay very slowly,giving rise to a plateau at intermediatetimes between the ballistic and the diffusive regime.Thedynamics close to this plateau is usually referred as theβ-relaxation.The closer the control parameters are tothe critical values,the slower is the mobility of the par-ticles and the longer is the lifetime of the cage,leadingFIG.5:(a)Correlation function cos 3∆Φ(t ) for ρ=6·10−3and different rod lengths;(b)The same function for L =99and different densities.Solid and dashed lines correspond,respectively,to the random and the glassy configurations of the host medium.to a longer plateau in the correlation function.Finally,at much longer times,the particle escapes from the cage and loses memory of its initial environment,leading to a second long-time decay of the correlation function to zero,known as the α-relaxation.Fig.4shows the behavior of the angular correlators cos m ∆Φ(t ) for m =1,2,3,...12for a density of obsta-cles ρ=6·10−3and a rod length L =99much longer than d nn .Data are shown for the random host medium,though for the glassy one they show the same qualitative behavior.Though it is difficult to see it for the small-est values of m ,the plateau is clearly visible for m ≥4.Thus,long rods will only perform small rotations be-fore hitting the walls of the tube.As a consequence,the correlators for small m will decrease only very weakly during the ballistic regime,and hence it will be diffi-cult to see the subsequent plateau.In contrast to this,for higher-order correlators,the angle rotated during the ballistic regime will be amplified by a factor m ,leading to a stronger decay before the caging regime and facili-tating the observation of the plateau.The latter begins to develop around t ∼30.As can be seen in Fig.1b for the curve for L =99,this time corresponds to the begin-ning of the caging regime for the mean-squared angular displacement,in agreement with the MCT prediction.Fig.5shows for m =3,and for the two models of host medium,how the plateau develops with increasing rod length and density of obstacles.While for values of L below d nn ,the correlators show a simple decay and within the error bar show no difference between both models of the host medium,a clear difference is observed for longer FIG.6:(a)Time-length superposition for cos ∆Φ(t ) at den-sity ρ=6·10−3;(b)Time-density scaling for the same func-tion for length L =99.For the sake of clarity the lowest values of ρand L have been represented with points.Dashed lines are fits to KWW functions.The stretching exponents are indicated in the figure.Data correspond to the random host medium.rods.Thus,for the glassy medium rotational relaxation times become about a decade larger and the plateau is significantly higher than in the random configuration,in agreement with the results presented in Section III for the mean-squared angular displacement.Another important prediction of the MCT is the so called “second universality”:given a correlator g (t,ζ),with ζa control parameter,then one has that,in the time scale of the α-regime,the correlator follows a scaling law g (t,ζ)=˜g (t/τ(ζ)),where τ(ζ)is the ζ-dependence of the relaxation time τof the α-regime for such a corre-lator,and ˜g is a master function.The relaxation time is in practice defined as the time where the correlator de-cays to an arbitrary but small fraction of its initial value,or is obtained from fitting the α-decay of the correla-tor to a Kohlrausch-Williams-Watts (KWW)function,exp(−(t/τ)β),widely used in the analysis of relaxations in complex systems [24].In most of the experimental sit-uations the relevant control parameter is the temperature and for that reason,the second universality is often re-ferred as the “time-temperature superposition principle”.Now we test the existence of a time-length and a time-density superposition principle for the angular correlators of the rod.Fig.6shows the correlators cos ∆Φ(t ) as a function of the scaled times t/τ(L )for constant density ρ=6·10−3,and t/τ(ρ)for constant rod length L =99.Data are shown for the random host medium.The re-laxation times τhave been defined as cos ∆Φ(τ) =1/e .Apart from the trivial scaling in the limit of short rods and low densities,a good superposition to a master curve is obtained for larger values of L and ρ,confirming thesecond universality of the MCT for this system.The master curve can be well reproduced by a KWW func-tion with a stretching exponentβ=0.8,as shown in Fig.6.Analogous results,with a very similar stretching exponent,are obtained for the glassy host medium.V.NON-GAUSSIAN EFFECTSIn Einstein’s random walk model for diffusion,par-ticles move under the effect of collisions with the oth-ers.When a particle undergoes a collision,it changes its direction randomly,and completely loses the mem-ory of its previous history.When the time and spatialobservational scales are much larger than the charac-teristic time and the mean free path between collisions, the van Hove self-correlation function,i.e.,the time and spatial probability distribution G s(r,t)of a particle ini-tially located at the origin,is given by a Gaussian func-tion[25].This functional form is exact for an ideal gas and for an harmonic crystal.It is also valid in the limit of short times,where atoms behave as free par-ticles.When the system is observed at time and length scales comparable to those characteristic of the collisions, the possible intrinsic dynamic heterogeneities of the sys-tem will be reflected by strong deviations from Gaus-sianity in G s(r,t).Such deviations are usually quan-tified by the so-called second-order non-Gaussian pa-rameterα2(t),which in two-dimensions is defined as α2(t)=[ (∆r(t))4 /2 (∆r(t))2 2]−1.For a Gaussian function in two dimensions,α2(t)=0,while deviations from Gaussianity result infinite values ofα2(t).Our previous investigation[10]on the non-Gaussian parameter in the random host medium revealed some fea-tures similar to those observed in supercooled liquids or dense colloids,such as the development of a peak(see also Fig.7),which grows with increasing rod length-in analogy to decreasing temperature in supercooled liq-uids or increasing density in colloids.As also observed in these latter systems[11,12,13,14,15,16,17,18],the re-gion around the maximum of the peak corresponds to the time interval corresponding to the end of the caging and the beginning of the long-time diffusive regime.Thus, the breaking of the cage leads to afinite probability of jumps much longer that the size of the cage,resulting in a strongly heterogeneous dynamics at that intermediate time and spatial scale,which is reflected by a peak in the non-Gaussian parameter.Another interesting result was the observation that,in particular for long rods,in the time span of the simula-tion,i.e.for time scales that are much longer than the typical relaxation time of the system,α2(t)did not de-cay to zero but remainedfinite[10].Thus,the long-time dynamics is still significantly non-Gaussian,i.e.hetero-geneous,at the spatial scale-much larger than the rod length-covered by the simulation.In order to investi-gate the possibility of a relation of this large-scale non-Gaussian dynamics with the presence of holes in the ran-dom structure of obstacles,that might lead to afinite probability of jumps much longer than the average,as pointed out in Ref.[10],we have calculatedα2(t)for the FIG.7:Time-dependence of the non-Gaussian parameter α2(t)for different values of L and for the random and the glassy host medium at densityρ=6·10−3.dynamics of the rod in the glassy host medium.Fig.7 shows a comparison between the non-Gaussian parame-ter in both structures for several rod lengths and den-sityρ=6·10−3.While in the limit of short and long rodsα2(t)takes similar values for both configurations in all the time window investigated,a much more pro-nounced peak is observed at intermediate rod lengths for the glassy configuration.This result can be rationalized by the absence of holes in the homogeneous glassy host medium.Thus in the latter,jumps that lead to an escape from the cage formed by the neighboring obstacles will be necessarily long,since long rods will be confined in tubes which they will be able to leave only by making a long longitudinal motion.In contrast to this,the above mentioned inhomogeneous nature of the tube walls in the random structure will allow the rod to escape the cage also by shorter jumps,resulting in a less heterogenous caging regime.Only rods much longer than the hole size will need long jumps to escape from the tubes in the ran-dom host medium and the caging regime will become as heterogeneous as in the glassy host medium,as evidenced by the similar peak heights in Fig.7for L=59and99. Concerning the long-time dynamics,the non-Gaussian parameter also remainsfinite for the glassy host medium, and no important differences with the random configura-tion are observed.Therefore,contrary to what was pre-viously pointed out[10],the non-Gaussianity observed at large length scales for the long-time dynamics in the in-homogeneous random host medium is also present in the homogeneous glassy one,and is not related to the pres-ence of holes in the configuration of obstacles,the latter having effects only in the intermediate caging regime. Another way of visualize the non-Gaussian effects at large length scales is obtained by representing the intermediate incoherent scattering function F s(q,t)= exp(−i q·∆r(t)) ,at very long wavelengthsλ=2π/q. Brackets denote ensemble and angular average.Fig.8 shows this latter function in the random host medium for L=99andρ=6·10−3,at different(long)wave-lengths.(Note that the simulations have been extended one order of magnitude respect to those presented in Ref.[10].)The presence of non-Gaussian effects are made6FIG.8:Intermediate incoherent scattering function F s(q= 2π/λ,t)at different wavelengthsλforρ=6·10−3and L=99.Points correspond to simulation data in the ran-dom host medium.Lines are the curves in Gaussian approx-imation as calculated from the center-of-mass mean-squared displacement obtained from the simulation(see text).clear by comparing the different curves with those cor-responding to the Gaussian case in two-dimensions[25], exp[− (∆r(t))2 q2/4],where we use the center-of-mass mean-squared displacement (∆r(t))2 calculated from the simulations.From this comparison it is clear that significant non-Gaussian effects are still present at length scales of at least∼9000,i.e.two orders of magnitude larger than the rod length.Whether this result indicates that the diffusion of a rod in a disordered array of obsta-cles is actually a Poisson process at any length scale is an open question.VI.CONCLUSIONSBy means of molecular dynamics simulations,we have compared the dynamics of a rigid rod in two models of a 2d-disordered static host medium:a random configura-tion of soft disks and another“glassy”one obtained from freezing a liquid of soft disks in equilibrium.While the former is characterized by the presence of big holes and clusters of close obstacles,the latter presents an homo-geneous structure.No significant differences have been observed in the translational dynamics of the rod center-of-mass.However,rotations are much more hindered in the glassy host medium.Angular correlation functions have been calculated for a wide range of rod length and density of obstacles.In agreement with the predictions of the Mode Coupling Theory,these functions show a plateau at the time scale of the caging regime,and follow a time-length and a time-density scaling for the long-time dynamics.Strong non-Gaussian behavior has been observed at large length scales,though no significant differences are evidenced between the random and the glassy configu-ration of the obstacles.This result suggests that the long-time translational dynamics is not controlled by the presence of inhomogeneities in the host medium.We thank E.Frey for useful discussions. 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Static and dynamical properties of heavy water at ambient conditions from first-principles

Static and dynamical properties of heavy water at ambient conditions from first-principles

a r X i v :c o n d -m a t /0504146v 2 [c o n d -m a t .s o f t ] 8 A p r 2005Static and dynamical properties of heavy water at ambient conditions fromfirst-principles molecular dynamicsP.H-L.SitDepartment of Physics,Massachusetts Institute of Technology,Cambridge MA 02139Nicola MarzariDepartment of Materials Science and Engineering,Massachusetts Institute of Technology,Cambridge MA 02139(Dated:February 2,2008)The static and dynamical properties of heavy water have been studied at ambient conditions with extensive Car-Parrinello molecular-dynamics simulations in the canonical ensemble,with tem-peratures ranging between 325K and 400K.Density-functional theory,paired with a modern exchange-correlation functional (PBE),provides an excellent agreement for the structural proper-ties and binding energy of the water monomer and dimer.On the other hand,the structural and dynamical properties of the bulk liquid show a clear enhancement of the local structure compared to experimental results;a distinctive transition to liquid-like diffusion occurs in the simulations only at the elevated temperature of 400K.Extensive runs of up to 50picoseconds are needed to obtain well-converged thermal averages;the use of ultrasoft or norm-conserving pseudopotentials and the larger plane-wave sets associated with the latter choice had,as expected,only negligible effects on the final result.Finite-size effects in the liquid state are found to be mostly negligible for systems as small as 32molecules per unit cell.I.INTRODUCTIONWater,due to its abundance on the planet and its role in many of the organic and inorganic chemical processes,has been studied extensively and for decades both at the theoretical and at the experimental level 1−17.The pecu-liar interplay of hydrogen bonding,glassy behavior,and of quantum-mechanical effects on the dynamics of the atomic nuclei make computer simulations challenging,and a great effort has been expended to build a com-prehensive and consistent microscopic picture,and a link with observed macroscopic properties 4−17.Additionally,it is only recently that close agreement for fundamen-tal structural information such as the radial distribution function has been obtained between different experimen-tal techniques,such as X-ray 2and neutron diffraction 3measurements.Computational studies based on molecular dynamics simulations have also a rich history in the field.Simu-lations using force-fields models 4−9have been successful at reproducing many structural and dynamical proper-ties of liquid water.However,empirical models rely on parameters which are determined by fits to known exper-imental data,or occasionally to ab-initio results.Their transferability to different environments,or the ability to reproduce faithfully the microscopic characteristics of hydrogen bonding,are often in question.Due to develop-ment of novel techniques 18,19and the ever-increasing im-provement in computational power,extensive molecular-dynamics simulations from first-principles are now possi-ble.The increased accuracy and predictive power of these simulations comes at a significant price,and careful con-siderations has to be given to the length scales and time scales that can be afforded in a first-principle simulation,and the trade-offs in statistical errors when comparedwith classical simulations.Numerous ab-initio simula-tions on water have appeared 10−17,showing good agree-ment with experiments for the structural and dynamical data.Recent results have also reported 20,21that careful equilibration for ab-initio water at ambient conditions leads to radial distribution functions over-structured in comparison with experiments 2.After equilibration,the numerical estimates for the diffusion coefficient become at least one order of magnitude smaller than the mea-sured ones.Prompted by these results and by our own observations on the structure of water around iron aqua ions 22,we have undertaken an extensive investigation of the static and dynamical properties of water,to ascertain its phase stability around ambient conditions as predicted by first-principles molecular dynamics.Particular care has been given to the statistical accuracy of the results,assuring that the time scales and length scales of the simulations were chosen appropriately for the given conditions.The paper is organized as follows:In section II,we detail all the technical aspects of our simulations.Section III sur-veys the static and vibrational properties of the water molecule and the water dimer in vacuum,at the GGA-PBE 23density-functional level.In section IV,we discuss our extensive liquid water simulations,performed with Car-Parrinello molecular dynamics,in the temperature range between 325K and 400K.Section V discusses the limitations of this approach,and some of the possible reasons to explain the remaining discrepancies with ex-perimental results.2 II.CAR-PARRINELLO MOLECULARDYNAMICSOurfirst-principles calculations are based on density-functional theory,periodic-boundary conditions,plane-wave basis sets,and norm-conserving24or ultrasoft pseudopotentials25to represent the ion-electron interac-tions,as implemented in the public domain codes CP and PWSCF in theν-ESPRESSO package26.In Car-Parrinello molecular dynamics,an extended La-grangian is introduced to include explicitly the wavefunc-tion degrees of freedom,that are evolved“on-the-fly”si-multaneously with the ionic degrees of freedom:L CP=µ i d r ˙Ψi(r) 2+1PBE US PBE NC BLYP33(Thiswork)HOH104.20104.500.980.970.97173017401730d OO(˚A) 2.88 2.98-23.2-21.4-18(kJ/mol)3TABLE II:Vibrational frequencies of water monomer:ν1,ν2 andν3are the symmetric stretching,bending and asymmetric modes,respectively.PBE(NC)BLYP33 378136571573159539083756PBE(US)Expt34,39ν1(A)(cm−1)36953577ν2(A)(cm−1)15961593ν3(A)(cm−1)38043675ν1(D)(cm−1)35323446ν2(D)(cm−1)16161616ν3(D)(cm−1)37813647ν(Hb)(cm−1)644600ν(Hb)(cm−1)378333ν(O−O)(cm−1)196214to this point in later section.IV.LIQUID W ATER SIMULATIONSA.Liquid water simulation at325K1.Simulation DetailsIn thisfirst simulation,we used a body-centered-cubic supercell with32heavy water molecules,periodic bound-ary conditions,and the volume corresponding to the experimental40density of1.0957g/cm3at325K.A body-centered-cubic supercell strikes the optimal bal-ance,for a given volume,in the distance between a molecule and its periodic neighbors,and the number of these periodic neighbors.Ultrasoft pseudopotentials werefirst used,as detailed in the previous section,with plane-wave kinetic energy cutoffs of25Ry(wavefunc-tions)and200Ry(charge densities).The deuterium mass was used in place of hydrogen to allow for a larger time step of integration.It should be noted that for clas-sical ions this choice does not affect thermodynamic prop-erties such as the melting temperature(the momentum integrals for the kinetic energy factor out in the Boltz-mann averages).Of course,dynamical properties such as the diffusion coefficient will be affected by our choice of heavier ions.Extensive experimental data for deuterated (heavy)water are in any case widely available.The wavefunctionfictitious mass(µ)is chosen to be 700a.u.;this results in a factor of∼14between the aver-age kinetic energy of the ions and that of the electrons.A time step of10a.u.was used to integrate the electron and ionic equations of motions.This combined choice of parameters allows for roughly25ps of simulation time without a significant drift in the kinetic energy of wave-functionsand theconstant of motion for the Lagrangian (1).Our choice offictitious mass is consistent with the ratioµ/M≤1dt |r i(t)−r i(0)|2.(2)The structural and dynamical properties before and after this10ps mark are summarized in Table IV;experimen-tal values at298K are included for comparison.All these observation conjure to a picture in which the sys-tem takes at least10ps to reach a reasonably thermalizedFIG.1:Mean square displacement and potential energy as a function of time for ourfirst32-water molecules simulations at 325K.A:Diffusive region in thefirst stage of the simulation. B:After about10ps diffusivity drops abruptly.TABLE IV:Structural and dynamical parameters before and after the10ps mark,compared with the experimental results at298K.D(cm2/s)2.823.212.75FIG.4:Potential energy and constant of motion in a produc-tion run at 400K.FIG.5:Kinetic energy of the ions and the electrons in a production run at 400K.lation.25ps is approximately the maximum time allowed for a simulation with these parameters before the drift in the kinetic energy of the wavefunctions becomes appar-ent.We thus used these simulations as efficient “ther-malization”runs,to be followed by production runs that will be described below.It is interesting to monitor dur-ing these thermalization runs the evolution of the MSDs;these are shown for all four temperatures in Fig.3.An abrupt drop in diffusivity is observed for all cases but one (predictably,the one at the highest temperature of 400K).The onset of this drop in diffusivity varies,but broadly speaking is again of the order of 10ps.With these trajectories,now well thermalized in con-figuration space,we started our four production runs atFIG.6:O-O radial distribution functions calculated from the first and the next 12ps of the simulation at 400K.325K,350K,375K and 400K,and each of them starting from the last ionic configurations of the previous simu-lation at the corresponding temperature,but with zero ionic velocities.These four runs,lasting between 20ps and 37ps,were performed using µ=450a.u.and δt =7a.u.This choice of mass and timestep allows for an ex-cellent conservation of the constant of motion,and negli-gible drift in the fictitious kinetic energy of the electrons,for the simulation times considered.The ratio between the kinetic energy of the ions and that of the electrons was ≈22for the whole production time.Although a small enough fictitious mass decouples the electronic and ionic degrees of freedom,Tangney et.al.41pointed out that there is a fictitious mass dependent error that is not averaged in the time scale of ionic motions.Schwe-gler et.al.28studied this effect comparing closely Car-Parrinello and Born-Oppenheimer simulations finding a larger self-diffusion coefficient in the Car-Parrinello sim-ulation.However,the structural and thermodynamical properties were not affected.We show in Figs.4and 5the case of the 400K simu-lation;we stress that no periodic quenching of the elec-trons was needed,and the simulations were single un-interrupted runs.Since the initial configurations were already at equilibrium at their respective temperature,and thermalization in momentum space is fast,we found that “production time”can start early in the simulations.We discarded from each trajectory the initial 1.2ps that were needed to allow the ions to reach their target ki-netic energy.As a measure of the good thermalization reached in the simulations,we show in Fig.6the O-O radial distribution function obtained from the first 12ps of our 400K trajectory,and the following 12ps.To rule out any spurious effect in our simulations com-ing from the use of pseudopotentials,or an extended La-6TABLE V:Details of the production runs.Pseudo-Densityδt(a.u.)time(ps)32545037.6 NC 1.0957535045022.9 US 1.0635740045032.5 NC 1.05545D self R[g max]10−5cm2/s325K(US) 3.38 3.86325K(NC) 3.25 3.79350K(US) 3.25 3.77375K(US) 3.10 3.72400K(US) 2.50 3.45400K(NC) 2.55 3.37 Expt40,46at300K 2.75 3.58 The structural and dynamical results are summarized in Table VI.As seen in this table,there is an eight-fold increase in D self when increasing the temperature from 375K to400K.Price et.al.43reported the experimen-tal self-diffusion coefficient of supercooled heavy water at different of temperatures.At276.4K,which is just be-low the freezing temperature of heavy water(277.0K), the experimental value for D self is0.902×10−5cm2/s.D self for our simulations at325K,350K and375K is0.16,0.25and0.26×10−5cm2/s,respectively.These numbers are significantly smaller then the experimental value below the freezing point.On the other hand,D self at400K in our simulations is comparable to the exper-imental value at300K.These observations suggest that the theoretical freezing point for water at the DFT-PBE level is between375K and400K,and water below375 K is in a glassy/supercooled state.The hydrogen-bond structure can be studied calculat-ing the number of hydrogen bonds per molecule:we iden-tify a hydrogen bond is identified when two oxygen atoms are closer than3.5˚A and theFIG.7:O-O radial distribution functions calculated from the production runs at325K,350K,375K and400K for ul-trasoft and norm-conserving pseudopotentials.Experimental result is taken from Ref3.FIG.8:O-D radial distribution functions calculated from simulations at325K,350K,375K and400K for ultrasoft and norm-conserving pseudopotentials.Experimental result is taken from Ref3.V.OVERESTIMATION OF THE FREEZING TEMPERATURE OF W ATERThere are several possible reasons for the overestima-tion of the freezing temperature of water,and several of them could play a significant role.We discuss here some of the possibilities.FIG.9:Mean square displacements calculated from simula-tions at325K,350K,375K and400K for ultrasoft and norm-conserving pseudopotentials.A.Finite-size effectsSince our simulation cell contains only32water molecules,finite-size effects could obviously play a role even if periodic-boundary conditions are used.The in-teractions of water molecules with their periodic images could be considerable due to the long-range hydrogen-bond network.On the other hand,when zero correla-tions are found between a molecule and its periodic image we can safely assume that the unit cell is for all practi-cal purposes large enough,and every molecule feels the same environment that it would have in an infinite sys-tem.In our case,the distance between a molecule and its eight periodic images is∼11˚A and at this distance all radial distribution functions look veryflat and unstruc-tured.In any case,to study thefinite-size effects we car-ried out another extensive simulation(40ps total,with 15ps of production time following25ps of thermaliza-tion)for a system composed of64heavy water molecules at400K.We used the same parameters for this simu-lation as in the32-molecule,400K ultrasoft simulation. The oxygen-oxygen radial distribution function g OO(r)is shown in Fig.10for both the32-and64-molecule sys-tems.As mentioned previously,the radial distribution functions was calculated by repeating the unit cell in all directions.The molecules up to5.5˚A in the32-molecule cell,and6.9˚A in the64-molecule cell are inequivalent. We indicate in the graphs with two arrows,the radii of the spheres completely inscribed by in BCC simulation cells.Differences between32-and64-molecule systems are negligible,and within the variance for simulations of the order of10-20ps(as estimated from uncorrelated clas-sical simulation data20);the64-water simulation showsFIG.10:O-O radial distribution function for a Car-Parrinello simulations with 32or 64molecules.The arrows indicate the radius of the sphere that is completely inscribed by the BCC simulation cells with 32or 64molecules.FIG.11:O-O radial distribution function for a classical (SPC)simulation with 64or 1000water molecules.a marginally more structured g (r )where the first peak height is at about 2.6(compared to 2.5for the 32-water case).Larger ab-initio simulations would be too demand-ing;for this reason,we performed two classical simu-lations at 300K using the SPC force field 47for water,and comparing the case of 64and 1000water molecules (each simulation lasting 1000ps).Theg OO (r)calculated from these two runs are shown in Fig.11,and again we do not find any significant differences between these two curves.These results help ruling out finite-size effects as the major cause of the discrepancy observed with the experimental numbers.FIG.12:Power spectrum of deuterium atoms calculated from the velocity-velocity correlation function.For comparison,we also show the power spectrum as obtained with a larger fictitious mass of 700a.u (dotted line)instead of 450a.u.B.Exchange-correlation functional effectsWhile density-functional theory is in principle exact,any practical application requires an approximated guess to the true exchange-correlation functional.In this work,we have used the GGA-PBE approach 23.As it was ob-served in Sec.III,the structural properties for the water molecule and dimer are in excellent agreement with ex-periments,as is the binding energy for the dimer.On the other hand,the vibrational properties show larger discrepancies with experiments than usually expected,in particular for some of the libration modes in the dimer.This result certainly points to the need for improved func-tionals to describe hydrogen bonding.The dependence of the melting point on the exchange-correlation func-tional chosen is more subtle;below 400K,PBE water dis-plays solid-like oxygen-oxygen radial distribution func-tions that are only slightly affected by the temperature,and that are similar to those obtained with a fairly differ-ent functional such as BLYP 20.The similarity between these radial distribution functions is just a reminder that the structural property and the geometry of the inter-molecular bonds are well described by different function-als;once water is “frozen”,all radial distribution func-tions will look similar.The temperature at which this transition takes place could be affected by the use of dif-ferent functionals 48and the magnitude of the contribu-tion of any one of them to the melting point temperature is still open to investigation.C.Quantum effectsInfirst-principles Car-Parrinello or Born-Oppenheimer molecular dynamics simulations the ions are most of-ten treated as classical particles,which is a good ap-proximation for heavy ions(path-integral simulations can describe the quantum nature of the ions49−51,but their computational costs,when paired with afirst-principles DFT descriptions of the electrons,preclude at this moment simulations with the statistical accuracy needed).However,for light ions like hydrogen or deu-terium,the effects of a proper quantum statistics can be very significant;tunneling of the nuclei can also affect the dynamics52,53.In the case of water,all the intramolec-ular vibrational modes and some of the intermolecular modes are much higher in energy compared to room tem-perature.We show in Fig.12the power spectrum for the deuterium atoms as calculated from the velocity-velocity correlation function of heavy water molecules for our sim-ulations at400K(ultrasoft,32molecules).The distinc-tive peaks of the intramolecular stretching and bending modes are centered around2400cm−1and1200cm−1 respectively,much higher than the room temperature of k B T room≈200cm−1.The peak at500cm−1corresponds to the intermolecular vibrational modes,also larger than k B T room.When ions are treated as classical particles, as in our ab-initio molecular dynamics simulations,all vibrational modes obey Boltzmann statistics.In reality, modes with frequency higher than k B T room are frozen in their zero-point motion state,and their exchange of energy with the lower-frequency modes(“the environ-ment”)is suppressed-in other words their contribution to the specific heat is zero,in full analogy with the low-temperature discrepancies from the Dulong-Petit law in solids.This effect could significantly affect the dynamics of water-water interactions,and it has long been argued that treating each water molecule as a rigid body could actually provide a closer match with experimental con-ditions.In fact,recent ab-initio simulations54in which the water molecules are constrained to maintain their equilibrium intramolecular bond lengths and bond angle result in a more diffusive and less structured description of water,that remains liquid at a temperature of326K. Path-integral simulation55for water described with clas-sical potentials didfind as well a significant difference due to the quantum effects(i.e.the freezing of the high-energy vibrational excitation),of the order of50K.VI.CONCLUSIONWe performed extensivefirst-principles molecular dy-namics simulations of heavy water at the DFT-PBE level. Equilibration times are found to be comparatively long, and easily in excess of≈10ps at ambient temperature. 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Dynamics with a non-standard inertia-acceleration relation an alternative to dark matter

Dynamics with a non-standard inertia-acceleration relation an alternative to dark matter

a rXiv:as tr o-ph/93312v123Mar1993WIS-92/93/Nov-Ph DYNAMICS WITH A NON-STANDARD INERTIA-ACCELERATION RELATION:AN ALTERNATIVE TO DARK MATTER IN GALACTIC SYSTEMS Mordehai Milgrom Department of Physics Weizmann Institute of Science 76100Rehovot,Israel ABSTRACT We begin to investigate particle dynamics that is gov-erned by a nonstandard kinetic action of a special form.We are guided by a phenomenological scheme–the modified dynamics (MOND)–that imputes the mass discrepancy,observed in galactic systems,not to the presence of dark matter,but to a departure from Newtonian dynamics below a certain scale of accelerations,a o .The particle’s equation of motion in a potential φis derived from an action,S,of the form S ∝S k [ r (t ),a o ]− φdt.The limit a o →0cor-responds to Newtonian dynamics,and there the kinetic action S k must take the standard form.In the opposite limit,a o →∞,we require S k →0–and more specifically,for circular orbits S k ∝a −1o –in order to attain the phe-nomenological success of MOND.If,to boot,S k is Galilei invariant it must be time-non-local;indeed,it is non-local in the strong sense that it cannot even be a limit of a sequence of local,higher-derivative theories,with increasingorder.This is a blessing,as such theories need not suffer from the illnesses that are endemic to higher-derivative theories.We comment on the possibility that such a modified law of motion is an effective theory resulting from the elimination of degrees of freedom pertaining to the universe at large (the near equality a o≈cH o being a trace of that connection).We derive a general virial relation for bounded trajectories.Exact solutions are obtained for circular or-bits,which pertain to rotation curves of disk galaxies:The orbital speed,v ,and radius,r ,are related by (the rotation curve)µ(a/a o )a =dφvariations,would be different in the two classes of theories.The various points we make are demonstrated on examples of theories of this class,but we cannot yet offer a specific theory that satisfies us on all accounts,even at the non-relativistic level that we consider here.We also explore,in passing,theories that depart from the conventional Newtonian dynamics for very low frequen-cies.Such theories may be written that are linear,and hence more amenable to solution and analysis than the MOND theories;but,they do not fare as well in explaining the observations.I.INTRODUCTION:THE GALACTIC MASS DISCREP-ANCY AND THE MODIFIED DYNAMICSIt has been posited that the observed mass discrepancy,evinced by galac-tic systems,stems from the breakdown of the laws that are used to analyze their dynamics.This contrasts with the prevalent dark-matter doctrine that imputes the discrepancy to the presence of large quantities of,yet unobserved, matter.In particular,the problem has been attributed[1-3]to a departure from Newton laws that occurs in the limit of small accelerations,such as are found in galactic systems:There exists a certain acceleration parameter,a o, much above which Newton laws apply with great accuracy.Much below a o the dynamics(of gravitational systems,at least)departs drastically from these laws,and may be epitomized by a relation of the forma2/a o=MG/r2,(1) which gives the gravitational acceleration,a,of a test particle,at a distance r from a(point)mass M(instead of the usual a=MG/r2).There are different theories that might spring from this seed relation.In fact,our purpose here is to point a new direction for searching such a theory.Arguably,the performance of this modified nonrelativistic dynamics (MOND)has been quite successful in reproducing the observations of galac-tic systems[4-7](if not altogether without dispute–see ke[8],and the rebuttal by Milgrom[9]).The following predictions are particularly clear-cut and well-nigh oblivi-ous to the details of the underlying theory.Most of them pertain to circular motion in axisymmetric disk galaxies.1.The speed on a circular orbit,supported gravitationally by a central body, becomes independent of the orbital radius for large radii[2].This implies universallyflat asymptotic rotation curves of isolated disk galaxies,in accord with high quality observations of extended rotation curves[4,5,7].2.The asymptotic rotational speed,V∞,depends only on the total mass,=MGa o.This leads to a relation of the form M,of the central body via V4∞V4∞∝L,where L is any luminosity parameter that is proportional to the total mass.This is to be compared with the observed strong V∞−L correlation, known as the Tully-Fisher relation[10].3.If one approximates elliptical galaxies by isothermal spheres,MOND pre-dicts[11]a strong correlation between the velocity dispersion in a galaxy,σ,and its total mass M:σ4∼MGa o,reproducing the observed Faber-Jackson[12]relation betweenσand the luminosity.4.MOND predicts[2]that galaxies with very low surface density–which correspond to low mean accelerations–will evince a large mass discrepancy. This has been forcibly born out by many recent observations of low-surface-brightness disks[13],and,with less confidence,for dwarf-spheroidal satellites of our galaxy[14].5.According to MOND,elliptical galaxies–as modeled by isothermal spheres–cannot have a mean acceleration much exceeding a o(or a mean surface density much exceeding the critical surface density,Σo≡a o G−1).This explains the observed cutoffin the distribution of mean surface brightnesses of elliptical galaxies,known as the Fish law[15-16]6.It is known that bare,thin disks,supported by rotation,are unstable to bar formation,and eventual breakup.MOND endows such disks with added stability that enables them to survive,provided they fall in the MOND regime,i.e.their mean acceleration is at a o or below[17].This can explain the observed marked paucity of galactic disks with mean surface density larger than the aboveΣo,known as the Freeman law(see ref.[16])for a discussion of this issue).7.Above all,detailed analysis of rotation curves,for galaxies with high-quality data,show a very close match of the predictions of MOND with the observed rotation curves of these galaxies[4,5,7].Generally,the dynamics of systems of galaxies–such as small groups, galaxy clusters,and the Virgo infall–which are not as well understood,can also be explained by MOND,with no need to invoke dark matter[3].In the framework of MOND,all these tell us that a modification is called for when dealing with bound orbits.There is no observational information on unbound(scattering)trajectories,for massive particles.However,we may gain insight from observations of gravitational lensing of light from distant galaxies,by foreground clusters of galaxies[18].These however concern rel-ativistic trajectories,which require a relativistic extension of MOND for a proper treatment(see below and Sec.IV).Henceforth we assume MOND for all types of nonrelativistic trajectories.The value of a o has been determined,with concurrent results,by several independent methods;these rest on the twofold role of a o as the borderline acceleration(based on predictions5and6above),and as the scale of accel-eration in the deep MOND limit(based on predictions2and3).The best determination comes from the detailed studies of rotation curves[4,5,7],and isa o∼(1−1.5)10−8cm s−2,(2) assuming the value of75km/s/Mpc for the Hubble constant.Values of a/a o as small as0.1have been probed in studies of rotation curves;for the dynamics of the infall into the Virgo cluster one has typically a/a o≈0.01.MOND,which at the moment,is but a phenomenological theory,may be interpreted as either a modification of inertia,or as a modification of gravitythat leaves the law of motion intact[1].True,General Relativity has taught us that gravity and inertia are not quite separable on the fundamental level. For instance,the Einsteinfield equations for the gravitationalfield imply the geodesic law of motion.However,at the level at which we work–i.e.a non-relativistic effective theory–it does make sense to distinguish between the interpretations.By the former we mean,foremost,a modification the must be implemented for whatever combination of forces determines the motion.In default of a full-fledged theory Milgrom[1,2,3]had initially used a work-ing extrapolating equation of motion of the formµ(a/a o) a= a N≡− ∇φ,(3) to calculate the acceleration a from the conventional potentialφ( a N is the Newtonian acceleration).The extrapolating function,µ(x),satisfies µ(x)x→∞−−−−→1,so that Newtonian dynamics is attained in the limit of large accelerations.In the deep MOND limit we haveµ(x)∝x,for x≪1(and a o is normalized such thatµ(x)≈x),so that eq.(1)results,and all the above-mentioned predictions follow.Relation(3)is noncommittal on the matter of interpretation(see ref.[1]).As it is written it may be viewed as a modification of the law of motion for arbitrary potentialφ,or,inverted to read a=−ν(| ∇φ|/a o) ∇φ,and assumed to apply only for gravitational potential, it may be viewed,instead,as a modification of gravity.Bekenstein and Milgrom[19]have then devised a nonrelativistic formu-lation of MOND that is expressly a modification of gravity:It replaces the Poisson equation( ∇· ∇φ=4πG̺)by∇·[µ(| ∇φ|/a o) ∇φ]=4πG̺,(4) which determines the gravitational potential,φ,generated by the mass-density distribution̺.The acceleration of a test particle is then given by− ∇φ.This formulation is derivable from an action principle,enjoys the usual conservation laws,and has been shown to be satisfactory on other important accounts[19]. It reduces to eq.(3)in cases of one-dimensional symmetry,and,in fact,even for less symmetric systems such as disk galaxies,has been shown to predict very similar rotation curves as eq.(3).Notwithstanding the superior standing of eq.(4),it is eq.(3)that has been used in all the rotation-curve tests of MOND[4,5,7]–being much wieldier.There are several relativistic theories with MOND as their non-relativistic limit[19][20];none is without problems.They are all based on modification of gravity,in that they can be formulated as a modification of the Einstein field equations for the metric,with particles moving on geodesics.Obviously, this entails an indirect effect on other forces when gravity is important;but, inasmuch as gravity can be neglected,they do not entail a modification of the motion of particles subjected to other forces.In this paper we begin to discuss formulations of MOND that,in contrast, may be viewd as a modifications of the law of motion of a particle in a givenpotentialfield.The main implication here is that particle dynamics is affected for whichever combination of forces is at play.We derive the dynamics from an action principle,with an action S= S p+S k,where the potential part,S p,takes the conventional form,while thekinetic action,S k,carries the modification.The latter is functional of the trajectory of the particle,and is constructed using the acceleration constanta o as the only extra parameter.The theory must approach the Newtonian dynamics in the limit a o→0.When a o is not much smaller then all the quantities of the dimensions of acceleration,MOND takes its effect.The deep MOND limit corresponds to a o→∞.To obtain the behavior epitomized by eq.(1)–or,to have asymptoticflat rotation curves for isolated galaxies–weexpect S k∝a−1o in this limit,at least for circular trajectories.Thus–in departure from the pristine statement of the MOND hypothesis–deviations from Newtonian dynamics appear,here,not only when the momen-tary acceleration on the trajectory is much smaller than a o.The special role of the acceleration is highlighted only through the introduction of the con-stant a o of these dimensions.On a circular,constant-speed trajectories,all the quantities with the dimensions of acceleration that can be built equal the acceleration itself.For such trajectories the acceleration only has relevancy for MOND(as is the case for rotation curves of disk galaxies).The main purposes of this paper are:(i)To adumbrate a possible ra-tionale for MOND,which we do in Sec.II.(ii)To demonstrate that it is possible to formulate MOND as a modification of the law of motion–in con-trast to a modification of gravity–and to expound some general properties of theories based on such a modification(Sec.III).(iii)To point out significant differences between the two interpretations,and to highlight the observational implications of these differences(Sec.VI).(iv)To present general,exact re-sults pertaining to circular motion,and point their relevance to the dynamics of disk galaxies.In particular,we give a general expression for the rotation curve of an axisymmetric disk galaxy,and derive simple expressions for the energy and angular momentum of circular orbits in Lagrangian theories;this we do in Sec.IV.Some of the points we want to explicate become more transparent inthe context of a theory where the dynamics is modified in the limit of low frequencies,instead of the limit of small accelerations.Theories of this class may be constructed that are linear,and thus more amenable to analysis and solution;alas,as potential alternatives to dark matter in galactic systems, they seem to be at some odds with the observations.We discuss these,in some detail,in Sec.V,both for their own possible merit,and as heuristic auxiliaries.Finally,we list some of the desiderata that we have not been able to implement yet,and some questions that remain open(Sec.VI).We cannot pinpoint a specific theory.There is a large variety of theoriesthat,as far as we have checked,are consistent with the available,relevant data(especially RCs).Also,we can offer no single model that possesses all the features we would ultimately require from a theory(see Sec.VI).This approach opens a new course for searching for a sorely needed,rel-ativistic extension of MOND.II.PREAMBLE:THE POSSIBLE PROVENANCE OF THE MODIFIED DYNAMICSVarious considerations lead us to suspect that MOND is an effective ap-proximation of a theory at a deeper stratum.Here,a most germane observa-tion[1]is that the deduced value of a o[eq.(2)]is of the same order as cH o,where H o is the present expansion rate of the universe(the Hubble constant), and c the speed of light.(For H o=75km/s/Mpc,cH o∼7×10−8cm s−2.) This near equality may betoken an effect of the universe as a whole on localdynamics,on systems that are small on the cosmological scale.There are,in fact,several quantities with the dimensions of an acceler-ation,that can be constructed from cosmological parameters[21],beside the above expansion parametera ex≡cH o.(5) Another isa cu≡c2/R c,(6) where R c is the radius of spatial curvature of the universe;yet another isaλ≡c2|λ|1/2,(7) withλthe cosmological constant(λ≡Λ/3c2,whereΛis sometimes used).Only a ex is observationally determined with some accuracy,as the Hubble constant has been measured to lie between about50and100km/s/Mpc(see Huchra[22]for a review).On R c we have only a lower limit of the order of c/H o,and on|λ|we have only an upper limit[23-24]of order(H o/c)2.So, only upper limits exist on a cu and aλ,and these are of the order of a ex.In a Friedmannian universe,with vanishing cosmological constant,one hasa cu/a ex=|1−Ω|1/2,(8) whereΩis the ratio of the mean density in the universe to the closure density. (We use these relations,derived from conventional cosmology,only heuristi-cally,as MOND may entail a modification of the cosmological equations.)So, if todayΩis not very near1,a cu and a ex are comparable.If the mean mass density falls much shorter of closure–as would be in keeping with the basic premise of MOND–but a non-zero cosmological constant renders the universe flat–as would conform with inflationary models[25](see also ref.[24])–we have todayaλ≈a ex.(9) We see then that the near equality a o∼a ex may be fortuitous,with a o being really a proxy for a cu,or for aλ(or for another cosmic parameter).Theidentification of the parameter behind a o would be an important step toward constructing an underlying theory for MOND;it is also of great impact in connection with formation of structure in the universe,and the subsequent evolution of galaxies,and galactic systems[21]:Whereas aλis a veritable con-stant,a ex,and a cu vary with cosmic time(in different ways).The matter has also antropic bearings.The Newtonian limit,which corresponds to a o=0,would be realized, according to the above identifications of a o,when the universe is static(a ex= 0),flat(a cu=0),or characterized by a vanishing cosmological constant(aλ= 0),respectively.It is clear that to some degree the various cosmological parameters must appear in local dynamics.Afinite curvature radius of the universe would en-ter,at least through the boundary conditions for the Einsteinfield equations, and will modify the local gravitationalfield.Likewise,a non-zero cosmological constant modifies the distance dependence of Newtonian gravity.The expan-sion of the universe also enters,for example by slowing down otherwise-free massive particles(or red-shifting free photons).All these are not,however, what we need for MOND.These effects are only appreciable over distances or times of cosmological scales–not relevant for non-relativistic galactic systems and phenomena.We thus do not expect H o,R c,andλto enter local dynamics as a time or length scale.On the other hand,a o is of the the same order as the galactic parameter of the same dimensions.So,if a o does remain as a vestige of the underlying mechanism,it will be the only such parameter important for non-relativistic galactic phenomena.In contrast,relativistic phenomena characterized by accelerations of order a o or less,also involve scale length of cosmological magnitude.For example,a photon trajectory passing a distance r from a mass M,such that MG/r2≤a o,has a curvature radius of cosmo-logical scale(see more in Sec.IV).So,the fact that only a o appears in MOND may be peculiar to the non-relativistic nature of this theory.The following analogy may help illuminate the situation we have in mind: Had all our knowledge come from experiments in a small,closed laboratory on the earth’s surface,our dynamics would have involved a“universal”constant g–the free-fall acceleration.Unaware of the action of the earth,we wouldfind a relation F=m( a− g)between the applied force F,and the acceleration a of a particle.Actually, g encapsules the effects of the mass,M⊕,and radius, R⊕,of the earth.R⊕,by itself,appears conspicuously in the dynamics only on scales comparable with itself; g alone enters the results of very-small-scale experiments characterized by accelerations not much larger then g.(The es-cape speed here plays the part of the speed of light in connection with MOND:g=v2es /2R⊕.)The effective departure here is of a gravitational origin,but wemay describe it as a modification of inertia because it is in effect for whatever combination of forces F represents,even when there are no“local”sources of gravity(i.e.sources in addition to the earth).We would thus have no reason to consider it a modification of gravity.It may well be that the departure in-herent in MOND is also of a gravitational origin(and will then automaticallypreserve the weak equivalence principle in the effective theory).But why,at all,should H o,R c,orλenter local dynamics as an acceler-ation parameter?We can only offer a vague possible rationale:Even within the purview of conventional physics we expect a difference of sorts in the dy-namics of particles with accelerations above and below a o,if a o stands for one of the above cosmological parameters.Consider,for example,a electric charge accelerated with acceleration a;the lower radius of its radiation zone is r rad=c2/a.To say that a≪a cu,for example,is to say that r rad is much larger than the radius of curvature of universe.In a closed universe,for exam-ple,this would mean that there in not even room for a radiation zone.Clearly, the radiation pattern must differ from the case where a≫a cu for which the curvature is hardly felt within the nearfield.More generally,an accelerated particle carries a causal structure that includes an event horizon[26],whose scale isℓ=c2/a:This is the distance to the horizon;this is the space-time distance within which a freely falling frame can be erected;this is the typical wavelength of the Unruh black-body radiation,etc..For accelerations that are low in the context of MOND(a≪a o),the quality of the universe that is behind a o is felt withinℓ.Conversly,we surmise thatℓis imprinted on the conjectured inertiafield(e.g.by modifying the spectrum of the vacuumfields) in a way that is then sensitive to the acceleration of a test particle interacting with thisfield.All the above considerations may also usher MOND in as an effective theory of gravity.We thus envisage inertia as resulting from the interaction of the accel-erated body with some agentfield,perhaps having to do with the vacuum fields,perhaps with an“inertiafield”whose source is matter in the“rest of the universe”–in the spirit of Mach’s principle.If thisfield bears the imprint of at least one of the cosmic parameters,an effective description of dynamics in which this inertiafield is eliminated,may result in a scheme like MOND.We have not been able to put these vague ideas into a concrete use,and will have to make do,in the rest of this paper,with the study of effective theories.Nonetheless,we keep this conjectured origin before our eyes as a guide of sorts on what MOND may entail,beside the implications for the structure of galactic systems,which have been the phenomenological anchor for MOND.For example,such considerations would lead us reckon with the effects of an a o that varies with cosmic time,as alluded to above.We may also suspect that if MOND,as we now formulate it,is only an effective theory delimited by cosmological factors,then it cannot be used to describe cosmology itself(as we would not describe the motion of earth satellites with a constant-g dynamics in the above analogy).More generally,we noted above that MOND as described above–with a o as the only relevant parameter–may breakdown for any relativistic phenomenon with acceleration below a o(more in Sec.VI).There are attributes of the conventional laws of motion that we cannot take for granted in light of what we said above on the possible origin of the modified dynamics.For example,it is not obvious that the effective theorydescribing MOND is derivable from an action principle.The question of which symmetries(conservation laws)the theory enjoys also becomes open.We shall certainly want to preserve the six cherished symmetries of space;to wit,the translations and rotations(or more generally the six isometries of a maximally symmetric3-D space);what we said above raises no question with regard to these:the underpinnings of the effective theory are not expected to define a preferred position or direction.The same is true of parity.Regarding time reversal symmetry,if the expansion of the universe is in the heart of MOND,the fact that the universe has a well defined time arrow might well result in a strong TR asymmetry in the MOND regime,just as physics in an external magneticfield is TR non-symmetric.It would be very difficult to ascertain a breakdown of TR symmetry in the province of the galaxies.Many potential manifestations of this are forbidden by other symmetries,and there are other balking factors.The same is true of the time-translation symmetry. Here our guess would be that time translation might be affected through the introduction of the scale of time(H−1o),and this is not interesting,in the context of MOND,as it would lead to deviations only on very long time scales.Similarly,we are at sea regarding full Galilei invariance.While the ulti-mate underlying theory may well be Galilei(Poincare)invariant,or enjoy even a higher symmetry,this is not necessarily the case for the effective theory that results from the elimination of some degrees of freedom:the surmised“iner-tiafield”,which comprises the hidden degrees of freedom,defines a preferred frame.Perhaps the effective theory does enjoy some generalization of Galilei invariance that involves a o as a parameter,and that reduces to Galilei invari-ance in the limit a o=0.Imagine,for example,an accelerated observer in a curved space.As long as its acceleration is much larger than a cu of eq.(6),so that its Unruh wavelength,or the size of the region affecting the dynamics,is much smaller than the radius of curvature,he will not detect departure from Lorentz invariance due to the curvature;for a≪a cu the non-zero curvature distorts the Unruh spectrum.As phenomenology does not yet require that we relinquish any of the above symmetries,and as we do not have any concrete theoretical impetus to do so,we shall retain them in the following discussion.We shall concentrate on effective theories of motion that are derived from an action principle,and conform to the MOND hypotheses.Such effective theories,which are obtained by eliminating some degrees of freedom from a more fundamental theory,are,in many cases,non-local;i.e.,they are not derivable from an action that is an integral over a simple Lagrangian that is a function of only afinite number of time derivatives of the trajectory.A well known case in point is the Wheeler-Feynman formulation of electrodynam-ics[27],which results from the elimination of the electromagneticfield,and is non-local when only the particle coordinates remain as degrees of freedom.We shall show–without bringing to bear the origin of MOND–that an acceptable theory of MOND must,in fact,be strongly non-local.III.GENERAL MOND ACTIONSOne starts by assuming that the motion of a non-relativistic test particle, in a static potentialfieldφ,is governed by an equation of motion of the formm A=− ∇φ,(10) where m is the mass of the particle,and A(replacing the acceleration a inNewtonian dynamics)depends only on the trajectory r(t)(not on the po-tential);its value is,in general,a functional of the whole trajectory,and afunction of the momentary position on the trajectory,parameterized by the time t.Of A we further require the following:(i)That it be independent ofm,so as to retain the weak equivalence principle.In what follows we take a unit mass for the particle.(ii)According to the MOND assumption, A maybe constructed with the aid of the acceleration constant a o,as the only dimen-sional constant.This is assumed to be the case in the non-relativistic limit.(iii) A has to approach the Newtonian expression, a,in the limit a o→0,for all trajectories,to achieve correspondence with Newtonian dynamics.(iv)The deep MOND limit corresponds to a o→∞;phenomenology points to A∝a−1o, in this limit.We do not know that this shoul hold universally(i.e.for all sortof trajectories etc.),we shall assume this behavior for circular orbits,and for general trajectories assume only A→0.We also do not have observational information on the very limit a o→∞;reliable information exists only down to a/a o∼0.1.Here we shall assume this everywhere for large a o,but most of what we say is independent of this assumption.(v)We assume a o to be time independent,and so we are free to assume that A does not depend explicitly on time.If a o actually varies with cosmic time,our results may be considered the lowest order in an adiabatic approximation.Explicit time dependence could also enter A if the particle has variable mass,which is simple to include (but which we bar).(vi) A is invariant under translations,and transforms as a vector under rotations of the trajectory.(vii)We do insist on full Galilei invariance.Indication that we need require some invariance connected with a veloc-ity boost could have come from the study of unbound trajectories.When a fast,unbound particle of velocity v scatters gravitationally offsome mass, with pericenter distance r,the characteristic acceleration around pericenter isa≈v2es /r,where v es is the escape speed from r.We can,however,with theaid of v,construct other quantities with the dimensions of acceleration,e.g.a n=(v n d n。

计量经济学中英文词汇对照

计量经济学中英文词汇对照

Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covaห้องสมุดไป่ตู้iance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Asymmetric distribution Asymptotic bias Asymptotic efficiency Asymptotic variance Attributable risk Attribute data Attribution Autocorrelation Autocorrelation of residuals Average Average confidence interval length Average growth rate BBB Bar chart Bar graph Base period Bayes' theorem Bell-shaped curve Bernoulli distribution Best-trim estimator Bias Binary logistic regression Binomial distribution Bisquare Bivariate Correlate Bivariate normal distribution Bivariate normal population Biweight interval Biweight M-estimator Block BMDP(Biomedical computer programs) Boxplots Breakdown bound CCC Canonical correlation Caption Case-control study Categorical variable Catenary Cauchy distribution Cause-and-effect relationship Cell Censoring

Constraints on the decay of dark matter to dark energy from weak lensing bispectrum tomogra

Constraints on the decay of dark matter to dark energy from weak lensing bispectrum tomogra

2
B.M. Sch¨ afer, G.A. Caldera-Cabral and R. Maartens
model ΛCDM ΛΓ CDM φCDM φΓ CDM Ωm 0.25 0.25 0.25 0.25 σ8 0.8 0.8 0.8 0.8 ns 1 1 1 1 w0 -1 -1 −2 3 −2 3 wa 0 0 1 −3 1 −3 Γ 0 0
1 3 1 3
a very powerful observational probe for investigating the influence of dark energy on structure formation and the geometry of the universe (Schneider et al. 1992; Mellier 1999; Bartelmann & Schneider 2001; Refregier 2003), even in the nonlinear regime of structure formation (Jain & Seljak 1997; Bernardeau et al. 1997; Benabed & Bernardeau 2001). Lensing data is best used in tomographic measurements for constraining dark energy equation of state properties (Hu 1999, 2002; Heavens 2003; Jain & Taylor 2003), where one either measures the power spectrum or the bispectrum of a weak lensing quantity (Kilbinger & Schneider 2005; Schneider & Bartelmann 1997; Bernstein & Jain 2004; Dodelson & Zhang 2005). Supplementing the recent paper by La Vacca & Colombo (2008), who derived lensing bounds on interacting models from weak lensing power spectra, we focus on bispectrum tomography, and we use a more general, albeit phenomenological cosmological model. Bispectra have the advantage that the perturbative treatment is easier to carry out and that they are sensitive on the transition from linear to nonlinear dynamics in structure formation. After introducing the cosmological model and the peculiarities of gravitational lensing in the decaying CDM models in Sect. 2, we compute the weak lensing bispectrum and tomographic measurements in Sect. 3. Fisher-constraints on cosmological parameters are derived in Sect. 4 and the main results are summarised in Sect. 5. The parameter accuracies are forecast for the weak lensing survey proposed for the Dark UNiverse Explorer1 (DUNE). For the fiducial model, we take a spatially flat ΛCDM cosmology with w = −1, adiabatic initial conditions and stable CDM (Γ = 0). Specific parameter choices are H0 = 100h km/ s/Mpc with h = 0.72, Ωm = 0.25, σ8 = 0.8 and ns = 1.

The Sun's hydrostatic structure from LOWL data

The Sun's hydrostatic structure from LOWL data

Sarbani Basu, J. Christensen-Dalsgaard J. Schou
M. J. Thompson
and
High Altitude Observatory, NCAR, P.O. Box 3000, Boulder, CO 80307
S. Tomczyk
Recent observations with the LOWL instrument have for the rst time provided a uniform set of frequencies that allow detailed inversion for the structure of much of the Sun's deep interior. We present the results of inverting the LOWL data and compare them with the corresponding results obtained using inhomogeneous data sets from more than one instrument. Furthermore, improvements in the description of the required physics motivates the calculation of new solar models. Thus we present results of inversions of the LOWL data against several reference models using up-to-date physics. In models including the gravitational settling of helium, the sound speed and density agree with the Sun to within substantially better than 1%. We test various modi cations to the physics of the models, to see if the remaining small (but signi cant) discrepancy between the Sun and the models can be removed. We nd that none of the modi cations tested can adequately account for the remaining discrepancy, though a small increase in helium di usion in the core gives a modest improvement over the standard di usion model. Finally, we nd that the seismic data support theoretical calculations which indicate that settling of heavier elements has reduced the present surface value of Z by about 8% relative to its mean interior value.

低结构材料评比标准

低结构材料评比标准

低结构材料评比标准## Low Structure Materials Assessment Criteria.1. Composition and Purity:Material composition should be well-defined and consistent.Impurities and defects should be minimized to ensure material performance and reliability.2. Structural Order and Defects:Low structure materials should exhibit minimal structural order, resulting in amorphous or disordered arrangements.The presence and distribution of defects can influence material properties and performance.3. Mechanical Properties:Stiffness and strength should be appropriate for the intended application.Elastic modulus, yield strength, and fracture toughness should be characterized.4. Thermal Properties:Thermal conductivity and thermal expansion coefficient should be considered for applications involving heat transfer or thermal stability.Resistance to thermal aging and environmental degradation is important.5. Optical Properties:Optical transmittance, reflectance, and absorption should be tailored to specific applications such as light guiding, light manipulation, or energy harvesting.6. Electrical Properties:Electrical conductivity, dielectric constant, and impedance should be characterized for electrical applications.Insulation resistance and electrostatic discharge tolerance are important considerations.7. Chemical Properties:Chemical compatibility and stability are crucial for various environments and applications.Resistance to oxidation, corrosion, and chemical degradation should be evaluated.8. Processability:Materials should be easily synthesized, processed, and formed into desired shapes and configurations.Scalability and cost-effectiveness of production methods are important factors.9. Environmental Impact:The environmental impact of material synthesis, use, and disposal should be considered.Biodegradability, recyclability, and sustainability are becoming increasingly important.10. Safety:Potential hazards associated with handling, storage, and disposal should be thoroughly assessed.Toxicity, flammability, and chemical reactivity should be considered.## 低结构材料评比标准。

0404039.pdf

0404039.pdf
Hydrodynamic afterburner for the Color Glass Condensate and the parton energy loss
Tetsufumi Hirano1 and Yasushi Nara2
1
RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973, USA 2 Department of Physics, University of Arizona, Tucson, Arizona 85721, USA (Dated: May 30, 2006)
PACS numbers:源自I.INTRODUCTION
Experiments of high energy pp, dA, and AA collisions are currently performed at the Relativistic Heavy Ion Collider (RHIC) in Brookhaven National Laboratory for comprehensive understanding of the highly hot/dense matter, the quark gluon plasma (QGP). At collider energies, collisions of two relativistic nuclei involve many aspects of physics according to the relevant energy or time scale. There already exist many theoretical approaches to understand numerous RHIC data. We consider in this work, particularly, the physics of the gluon saturation in a colliding nucleus, hydrodynamic evolution of produced matter, and the energy loss of hard partons in the medium. Our goals are to combine them and to take a first step toward a unified understanding of the dynamical aspect of high energy heavy ion collisions. First data reported by the STAR Collaboration at RHIC [1] revealed that the observed large magnitude of elliptic flow for hadrons is naturally explained by hydrodynamics. This suggests that large pressure possibly in the partonic phase is built at the very early stage (τ ∼ 0.6 fm/c) in Au+Au collisions at RHIC. This is one of the strongest indications of early thermalization of the QGP at RHIC [2, 3]. These hydrodynamic predictions also give good agreements with mass dependences of the second harmonic coefficient of azimuthal distribution v2 (pT ) in the low pT region [4]. This is in contrast to the case in the lower energy collisions where the hydrodynamics always overpredicts the data [5, 6]. Hydrodynamics also predicts that the scaled elliptic flow, which is defined as the second harmonics v2 divided by initial spatial eccentricity ε, becomes almost constant around 0.2 [7]. The experimental data reaches the hydrodynamic limit for the first time in central and semicentral collisions at RHIC energies [8]. Moreover, v2 as a function of pseudorapidity [9] can be reproduced by hydrodynamics only in | η |< 1 [10]. All these analyses indicate that a high dense matter created
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a r X i v :c o n d -m a t /9705032v 2 [c o n d -m a t .s t r -e l ] 28 J u l 1997Dynamical properties of low dimensional CuGeO 3and NaV 2O 5spin-Peierls systemsDavid Augier and Didier PoilblancLaboratoire de Physique Quantique &Unit´e Mixte de Recherche CNRS 5626Universit´e Paul Sabatier,31062Toulouse,France.(July 97)Properties of low-dimensional spin-Peierls systems are described by using a one dimensional S =1trated Heisenberg chain and,at low temperatures,the small interchain frustration alone cannot be responsible for the opening of a spin gap.The coupling to the lat-tice is therefore expected to play a dominant role in the transition at least for NaV2O5.In order to study their in-terplay,the frustration and the spin-lattice coupling have to be treated on equal footings.This is the purpose of this paper.It is well known that a1D system shows no phase tran-sition atfinite temperature because of quantumfluctu-ations.Interchain couplings are necessary to obtain a finite transition temperature.However,they are thought to be small and will be neglected hereafter in the study of zero temperature properties.So far,there have been various attempts to treat the coupling to the lattice by considering a static dimeriza-tionδof the exchange integral(so called adiabatic ap-proximation or frozen phonon approximation).The value ofδis determined in order to obtain the experimental value of the zero temperature spin gap∆.Dimeriza-tions such asδ=0.014[7]andδ=0.048[17]were proposed for CuGeO3and for NaV2O5,respectively,in order to reproduce the measured spin gaps(assuming ∆≃0.151J and∆≃0.193J for CuGeO3and NaV2O5, respectively).Calculations using this approach have been performed in order to makefirst comparisons with exper-iments[9,18–20].In this paper we use a modification of the previous static model to describe the physical properties of one-dimensional spin-Peierls compounds below the transition temperature.For convenience,the previous ad hoc static dimerization discussed above is replaced here by a sin-gle dynamical optical phonon mode(Section II).As far as thermodynamic properties are concerned,this model should be,in fact,equivalent in the thermodynamic limit to a model where the lattice is treated at a meanfield level[21].However,this new approach has some advan-tages:(i)it incorporates automatically the elastic en-ergy and avoids the lengthy iterative procedure needed in a mean-field treatment to converge to the equilibrium static lattice dimerization;(ii)it enables to study the mechanism of the lattice symmetry breaking and,hence, provides a basis for future studies including a macro-scopic number of phonon modes(i.e.proportional to the system length L)[22]in spin-Peierls chains.Within this single mode approximation,we truncate the Hilbert space of the phonons and show in details that this approximation is well controlled(Section III).Us-ing afinite size scaling analysis(discussed in detail in Section IV)the dimerization and the spin gap resulting from a spontaneous discrete symmetry breaking of the lattice periodicity are calculated(Section V).Focussing primarily on CuGeO3and NaV2O5materials,we then establish a simple relation between the parameters of the model in such a way to enforce the constraint that the numerically calculated spin gap is equal to the experi-mental gap.The role of the parameters is discussed.In the last part(Section VI),we study the spin dynamics.In particular,we investigate the role of the lattice dynam-ics on the low energy magnon branch and low energy structures in the dynamical spin structure factor.Our results are compared to the ones obtained in the static model[17,19,23,24].II.MODELSOur starting point is the1D frustrated AF Heisen-berg chain.For practical applications,the previous val-ues of J andαwill be used:J=160K,α=0.36 for CuGeO3[7]and J=440K,α=0for NaV2O5[5]. In addition,a coupling between spins and dispersionless optical phonons(magneto-elastic coupling)is considered. For sake of simplicity we assume a linear dependence of the exchange integrals on the relative atomic displace-ments{u i}[25,26],H=J i((1+λu i) S i. S i+1+α S i. S i+2)+H0ph,(1)whereλis the coupling constant.H0ph is the phononic Hamiltonian of identical independent quantum oscilla-tors,H0ph= i(p2i2K u2i)(p i is the conjugate mo-mentum associated to the atomic displacement u i).The atomic displacements u i and their conjugate variables can easily be expressed in term of the canonical phonon creation and annihilation operators b†k and b k.Since the spin susceptibility diverges(forα<0.5[27,28])at mo-mentum k=πwe expect that the coupling to the lattice will be dominant at k=πwhich corresponds,in fact,to the modulation of the spin-Peierls ground state.There-fore,from now on,we shall only keep a single k=πphonon mode[29].In this case,using,u i≃(−1)i2mLΩ(bπ+b†π)(Ω2=K/m and L is the number of sites),thefinal Hamiltonian becomes,H=J i 1+g(−1)i L(bπ+b†π) S i. S i+1+α S i. S i+2+H0ph,(2) where g=λ 2mΩis the dimensionless coupling con-stant.Within this approximation H0ph can be rewrit-ten as H0ph=Ω(b†πbπ+1gL b π+b †π MF and omitting a constant part,the MFHamiltonian takes the form,H MF =Ji((1+δ(−1)i ) S i . S i +1+α S i . S i +2)+1λ2δ2,(3)where the last term is the elastic energy loss.This is ex-actly the well known model describing a static dimeriza-tion below the transition temperature in spin-Peierls sys-tems [7,8].Interestingly enough,a similar effective model has also been used to describe conjugated hydrocarbons with bond alternation such as polyacetylene [30].In this new form,the breaking of the lattice periodicity is ex-plicit.As a consequence the ground state becomes doubly degenerate (the order parameter δcan take a positive or a negative value)and a spin gap appears.The spin-Peierls ground state is characterized by a “···A −B −A −B ···”pattern with a succession of strong singlet A bonds and weak singlet B bonds (so called Valence Bond or dimer state).Note that δin model (3)is a variational param-eter to be determined in order to minimize the ground state energy by an iterative procedure.In contrast,the dimerization in Hamiltonian (2)arises from a dynamical symmetry breaking.However,it is interesting to notice that models (3)and (2)should be in fact equivalent [31]in the thermodynamic limit ,at least as far as their ther-modynamic properties are concerned [21,32].Static and dynamical quantities are given by exact di-agonalizations of small ing a finite size scaling analysis,results in the thermodynamic limit are deduced.The parameters δon one hand and g and Ω/J on the other hand are determined from a fit to the experimental spin gap.III.TRUNCATION PROCEDURELet us now deal first with the numerical treatment of (2).The total Hilbert space can be written as the ten-sorial product of the space of the spin configurations (to which the symmetry group of the problem is applied)times the phononic space.However,strictly speaking,the Hilbert space associated to the phonons is infiniteeven for a chain of finite length.Indeed,the natural basis {|n }is simply defined by the unlimited occupation num-ber n of the k =πphonon mode,|n =1/√L (b π+b †π)+Ωb †πb π(A is an undetermined constant).In this approximation,b †πb π =A 2g 2L/Ω2grows linearly with the length of the chain.In addition,this simple argument also suggests that the occupation number of the πmode scales like the square of the dimensionless coupling g and like the inverse square of the phonon frequency.These intuitive behaviors are indeed well followed as can be seen in Fig.3in a large range of parameters.0510152025L0.000.050.100.150.20<b +b >FIG.2.Dependence of the mean occupation number on the length of the chain L for g =0.109and Ω=0.3J .0.00.10.2g20.01.02.03.0<b +b >(a)(b)0306090J 2/Ω2FIG.3.Mean occupation number calculated on a L =12site chain versus g 2for Ω/J =0.3(a)and versus (J/Ω)2for g =0.109(b).One also observes in Fig.1that the singlet ground state energy is almost converged for L =20(the values of the energies for L =12and L =20at large N max are indis-tinguishable)while finite size effects are still large for the triplet energy because of the existence of a continuum of states above the first triplet excitation.In the next Section,we investigate carefully the convergence of var-ious physical quantities with respect to the system size.We show that an accurate finite size analysis can be per-formed to obtain extrapolations to the thermodynamic limit.IV.FINITE SIZE SCALING ANALYSISFirstly,we focus on the size dependence of the en-ergy per site of the singlet ground state and of the low-est triplet state which are expected to converge to the same value in the thermodynamic limit.Typically,we use chains of length L =8,12,16,20and 24sites.Data are shown in Fig.4for α=0.15,g =0.45and Ω=0.3J .The ground state energy per site varies roughly like 1/L 2.This behavior is predicted for gapless 1D chains obeying conformal invariance [34]but seems to be still valid here in spite of the presence of a spin gap (see later).This already suggests that,for such param-eters,the system sizes are still comparable to the spin correlation length but not much larger.The behavior of the triplet energy is more involved.An approximate 1in order to accurately determine the spin gap as a func-tion of g andΩ;(iii)a determination of the relation to be followed by the parameters g andΩin order that the calculated ratio∆/J equals the actual experimental ratio (see Section V).Wefirst consider the scaling behavior of the spin gap. We have found that it scales accurately according to the law,[35,10]∆(L)=∆+AL0),(4)where L0is a typical length scale.In general L0is of the order of the magnetic correlation length character-izing the decay of the equal time spin-spin correlation in real space.As seen later,values of L0are typically 20lattice units(l.u.)for parameters corresponding to CuGeO3and30l.u.for NaV2O5.Therefore,with chains lengths up to24sites,finite size effects are still impor-tant and an accurate extrapolation is necessary.This scaling is illustrated forα=0.15<αc,g=0.22,Ω=0.3J(◦),forα=0.36>αc(CuGeO3-like case), g=0.089,Ω=0.3J( )and forα=0(NaV2O5-likecase),g=0.40,Ω=0.5J(♦)in Fig.5(a).A spin gapopens for allαif g>0.This is similar to the mean-field treatment where the order parameterδ=0leads tothe symmetry breaking and thus to the opening of a spin gap.In Fig.5(b)we compare,in the case of NaV2O5(i.e.α=0),the scaling of the spin gaps calculated using thedynamical model(2)with g=0.275,Ω=0.3J(◦)on one hand and the static model(3)withδ=0.05( )onthe other hand[36].These values of the parameters have been chosen in order to obtain the same extrapolated spingap.Although the spin gaps are equal,the two models exhibit slightly different scaling behaviors(L0≃30for the dynamical model and L0≃18for the static one[17]). At this stage,it is interesting to better understand howin the the dynamical model(2)the opening of the spin gap is connected to the discrete symmetry breaking(as can be seen e.g.in X-rays scattering).Thefirst signature of this phenomenon is the degeneracy of the ground state which is expected in the thermodynamic limit.We have therefore studied the behavior with system size of the en-ergies E p(S=0),p=0,1,2,of the three lowest singlet states.The energy differences E1(S=0)−E0(S=0) (circles)and E2(S=0)−E0(S=0)(squares)are plot-ted in Fig.6,in the caseΩ=0.3J,as a function of the inverse length of the chain1/L forα=0.36(open sym-bols)and forα=0(filled symbols).The values of the coupling g are chosen here in such a way to reproduce the experimental spin gaps of the CuGeO3(open sym-bols)and NaV2O5(filled symbols)materials(see Section V).The results show very convincingly that the singlet ground state is indeed two-fold degenerate in the thermo-dynamic limit while afinite gap for singlet excitations appears above[37].It is important to notice that the quantum numbers associated to the translation symme-try are different for the two lowest singlet states which0.000.050.100.151/L0.00.20.40.6∆/J(a)(b)0.00.20.40.6∆/JFIG.5.(a)Spin gap∆in units of J as a function of the inverse of the length of the chain1/L forα=0.15,g=0.22,Ω=0.3J(◦),α=0.36,g=0.089,Ω=0.3J( )andα=0, g=0.40,Ω=0.5J(♦).(b)Comparison between the behav-iors∆/J vs1/L obtained within the dynamical model(2)for α=0,g=0.275,andΩ=0.3J(◦)and within the static model(3)forα=0,δ=0.05( ).correspond to momenta k=0and k=π.Hence,mixing of these two states leads to a doubling of the unit cell.The lattice dimerization can be quantitatively mea-sured by the order parameterδ∗=gL (bπ+b†π)2 1/2 (the expectation value bπ+b†π vanishes because small tunnelling between the two degenerate dimer states al-ways exists in afinite chain).δ∗as a function of the inverse length of the chain is plotted in Fig.7for various pairs of parameters(Ω,g)(see caption)chosen in such a way that the spin gap is constant(in fact adjusted to the actual spin gap of CuGeO3as described in Section V).Extrapolated values of the dimerizationδ∗for differ-ent phonon frequencies are in fact quite close,at least in the range0.1≤Ω≤0.5.The dimerizationδ∗seems then to be only determined by the magnitude of the spin gap.The fact thatδ∗,atfixed extrapolated spin gap, is independent of the frequencyΩis consistent with the proof by Brandt and Leschke[21]that the thermody-namic properties of the dynamical model(2)and of the static model(3)are identical.However,it is interesting to notice that the value obtained here(∼0.022)is sig-nificantly larger than the value(∼0.014)needed in the MF approximation to produce the same gap.The differ-1/L0.00.10.20.30.4(E 1-E 0)/J , (E 2-E 0)/JFIG.6.Energy differences (E 1(S =0)−E 0(S =0))/J (◦,•)and (E 2(S =0)−E 0(S =0))/J ( , )as a function of 1/L .Open and filled symbols correspond to α=0.36,g =0.109and Ω=0.3J and to α=0,g =0.270and Ω=0.3J ,respectively.ence between these two values can be simply attributed to the zero point motion of the harmonic mode which is included only in (2).0.000.050.101/L0.0150.0180.0210.024δ∗FIG.7.Order parameter δ∗as a function of the inverse of the length of the chain 1/L for Ω=0.1J and g =0.062(◦),Ω=0.3J and g =0.109( )and Ω=0.5J and g =0.141(♦)(see text regarding the choice of parameters).PARISON WITH EXPERIMENTThe systematic finite size scaling described above has been performed for a large set of parameters Ω/J and g .For simplicity,let us first assume Ω/J =0.3.The behav-ior of ∆(Ω/J =0.3,g )/J versus g is plotted in Fig.8for a large frustration α=0.36corresponding to the case ofCuGeO 3(◦)and for a non frustrated chain correspond-ing to the case of NaV 2O 5( ).Quite generally,the spin gap grows with the coupling constant g as expected.In-deed,a larger coupling to the lattice produces a larger dimerization and then,indirectly,a larger spin gap.The actual physical value of the ratio Ω/J is,to the best of our knowledge,difficult to obtain from exper-iment.Therefore,we shall not here restrict to any specific value of Ω/J but rather consider a wide range 0.1≤Ω/J ≤0.5.However,for each value of Ω,the di-mensionless coupling constant g (Ω)can be determined by enforcing that the extrapolated spin gap ratio ∆(Ω,g )/J equals the experimentally observed gap.The procedure is shown in Fig.8for Ω=0.3J and α=0(NaV 2O 5)and α=0.36(CuGeO 3).The small horizontal marks corre-spond to the actual experimental gaps,i.e.∆/J ≃0.151and ∆/J ≃0.193for CuGeO 3and NaV 2O 5,respectively.We then obtain g (Ω=0.3)≃0.109for CuGeO 3and g (Ω=0.3)=0.270for NaV 2O 5.The same method was performed for two other values of the frequency,Ω=0.1J and Ω=0.5J .A relation is then obtained between Ωand g for the two values of the frustration parameter α=0and α=0.36.This is illustrated in Fig.9.We find that Ωhas to vary roughly like g 2in order that the spin gap is constant.Naively,one indeed expects that softer (i.e.with smaller Ω)phonon modes are more effective to break the lattice symmetry.So,if one requires the spin gap to be constant,this effect has to be compensated by a smaller coupling g .0.070.120.170.220.27g0.100.140.180.22∆/JFIG.8.Spin gap ∆/J (in units of J )as a function of the magneto-elastic coupling g for α=0.36,Ω=0.3J (◦)and α=0,Ω=0.3J ( ).Horizontal marks indicate actual ex-perimental spin gap values.In Fig.9we observe that the coupling constant g (Ω)is roughly 2.5−3times smaller for CuGeO 3than for NaV 2O 5although the ratio of their spin gaps is only 1.5.This is an interesting consequence of the large frustration in CuGeO 3.Indeed,a large αopens alone a (quite small)spin gap and,more importantly,amplifies the effect of thespin-phonon coupling.This effect is evenmoredrasticinthestaticmodel(3)wherethedimerizationsδ=0.014(CuGeO3)andδ=0.048(NaV2O5)have a ratio of about4[17].0.00.10.20.30.4g0.00.10.20.30.40.5Ω/JFIG.9.FrequencyΩin units of J as a function of themagneto-elastic coupling g insuring a constant spin gap(seetext)forα=0.36(◦)andα=0( ).The model(2)seems to describe accurately the spin-Peierls transition.Theoretical parameters have been de-duced from experiment and the ground state propertiesof the spin-Peierls phase have been established.We haveprovided evidences in favour of the dynamical breakingof the lattice periodicity with the simultaneous openingof the spin gap.Next,we shall study the dynamical prop-erties of this model.VI.DYNAMICAL PROPERTIESINS is a powerful experiment probing the momentum-dependence of the spin dynamics.INS has been per-formed on CuGeO3single crystals[13,12]and on NaV2O5powders[3].It provides a direct measure of the dynami-cal spin-spin structure factor,S zz(q,ω)= n| Ψn|S z(q)|Ψ0 |2δ(ω−E n+E0),(5)where|Ψ0 is the(singlet)ground state of energy E0andthe sum is performed on all triplet excited states|Ψn (ofenergy E n).S z(q)is normalised as1/√10,n=0,···,10;(a)CuGeO3parame-ters,α=0.36,g=0.109,Ω=0.3J;(b)NaV2O5parameters,α=0,g=0.270,Ω=0.3J.A broadening of theδ-functionsε=0.04J was used.The dispersion relations of the magnon branch(◦),thesecond excitation( )and the upper limit of the con-tinuum(♦)in the dynamical model(2)are plotted inFig.11(a)for CuGeO3.The‘∗’symbols correspond to ex-perimental results from Ref.[13]andfilled symbols corre-spond to infinite size extrapolations at momenta q=π/2and q=π.Similar dispersion relations are shown inFig.11(b)for NaV 2O 5and the position of the experimen-tal q =πspin gap [3]is indicated by an arrow.Note that we have explicitly checked that the magnon branch is well separated from the continuum.A finite size scaling anal-ysis of the energies of the two lowest triplet states (•, )is indeed possible at momentum q =π/2.Figs.11(a-b)clearly show that there is a finite gap between the first branch and the continuum as in the static model.It is consistent with the fact that the continuum corresponds to solitonic spin-1/2excitations (or spinons)and that solitons and antisolitons can bind in pairs with momenta close to q =π/2[23].Such a dougle gap feature was indeed observed experimentally [39].It is important to notice that the dispersion relation is not symmetric with respect to q =π/2in contrast to the case of a static dimerization.In fact,such a symmetry in the energy spectrum is due to the Bragg scattering resulting from the doubling of the unit cell.Since the dimerization appears only as a true phase transition in model (2),we expect that the symmetry of the spectrum with respect to π/2will only become exact in the thermo-dynamic limit.In the case of CuGeO 3,our results are in very good agreement with INS experiments although fi-nite size effects are still important.In fact the agreement improves with increasing system size since the calculated magnon branch for q >π/2shifts slightly to lower en-ergy when L grows (in order to be symmetric with the q <π/2part).Note also that energy scales are four times larger for NaV 2O 5than for CuGeO 3which could restrict INS experiments on NaV 2O 5to low energy regions of the spectrum in the vicinity of q =π.It is interesting to compare results for the spin dynam-ics obtained within the dynamical model to the ones ob-tained within the static model.Fig.12shows the lowest triplet magnon branches and the next triplet excitations (in fact lower limits of the S =1continuum)for parame-ters suitable for CuGeO 3.We do not explicitly show the comparison of the upper limits of the continua since the two curves obtained within the two models are almost indistinguishable.This is not surprising because higher energy excitations are only determined by the magnitude of the frustration and the coupling to the lattice plays a minor role here.At lower energy,the magnon branches of the two models look also very similar for q <π/2but some differences appear for q >π/2since,as explained before,the dispersion is not symmetric with respect to π/2in the dynamical model.This is simply due to larger finite size effects [40]occurring in model (2)related to the fact that the lattice periodicity is only spontaneously bro-ken.Once such finite size effects are taken into account we can safely conclude that the dispersions of the magnon branches of the two models in the thermodynamic limit are very close.Similarly,the discrepancies seen between the positions of the lower limits of the continua of triplet excitations are not relevant.Indeed,a detailed finite size scaling analysis at e.g.q =π/2reveals that the position of the two lower limits are in fact quite close (1.117J for (3)to be compared to 1.118J for (2)).An exactly similarq/π0123ω/Jω/JFIG.11.Momentum dependence of the first (◦),second excitation ( )and upper limit of the continuum (♦)on a 20site chain for the dynamical model (2).Filled symbols rep-resent extrapolations to infinite size (first (•)and second ( )excitations).(a)CuGeO 3parameters (α=0.36,g =0.109,Ω=0.3J ).Experimental values (∗)are taken from from Ref.[13].Units on the right are in meV assuming J =160K (13.8meV).(b)NaV 2O 5parameters (α=0,g =0.270,Ω=0.3J ).Units on the right are in meV assuming J =440K (37.9meV).The arrow indicates the experimental value of the q =πspin gap.comparison can be done for NaV 2O 5(not shown).The spin static structure factor,S zz (q )=dωS zz (q,ω),which can be obtained in INS by integrating the spectrum over energy is plotted in Fig.13for CuGeO 3(α=0.36,g =0.109,Ω=0.3J )(◦)and NaV 2O 5(•)(α=0,g =0.270,Ω=0.3J )for a 20site chain.It is peaked near q =πas a result of strong short range AF correlations.Indeed the width of the peak at q =πis directly related to the inverse magnetic correlation length.Note however that S zz (π)is slightly suppressed in CuGeO 3compared to NaV 2O 5because of the interchain frustration.In any case,the results are very similar to those obtained with the static dimerized model.The relative weights of the magnon peak in S zz (q,ω)are also shown for CuGeO 3( )and NaV 2O 5( ).Their behaviors versus q suggest that working in a range of momenta around q =0.8πmight be0.00.20.40.60.8 1.0q/π0.00.51.01.5ω/J5101520[meV]FIG.12.Momentum dependence of the two lowest triplet excitation energies in CuGeO 3calculated on a 20site chain for (i)the dynamical model (2)(α=0.36,g =0.109,Ω=0.3J )(◦)and (ii)the static model (3)(α=0.36,δ=0.014)( ).Units on the right are meV assuming that J =160K (13.8meV).more appropriate experimentally in order to have clearer evidences for the continuum.VII.CONCLUSIONSIn order to describe one dimensional spin-Peierls com-pounds,a magneto-elastic (i.e.spin-phonon)coupling has been considered and is shown to be responsible for a dynamical and spontaneous breaking of the lattice peri-odicity followed simultaneously by the opening of a spin gap.The resulting symmetry-broken ground state is con-sistent with the existence of a frozen dimerization such as the one obtained in a mean-field treatment of the cou-pling to the lattice.We have used exact diagonalization techniques to calculate static and dynamical properties of this model.Controlled truncation procedures have been applied to the bosonic Hilbert space of the Hamil-tonian.By using a finite size scaling analysis,we have compared various physical quantities to the experimental ones (in the case of CuGeO 3and NaV 2O 5)and we have determined a range of suitable parameters for the model.We predict that the spin-phonon coupling is 2or 3times larger in NaV 2O 5than in CuGeO 3.The INS spectrum calculated within this model is found to be qualitatively similar to the one obtained in the static model with a finite gap separating the magnon branch from the con-tinuum of triplet excitations above.D.A.acknowledges useful discussions with M.Albrecht and S.Capponi.We thank L.P.Regnault for communi-cating to us the data of Ref.[13],J.Riera for valuableq/πS z z (q )FIG.13.Static factor structure for CuGeO 3(α=0.36,g =0.109,Ω=0.3J )(◦)and NaV 2O 5(α=0,g =0.270,Ω=0.3J )(•)calculated on a 20site chain.Squares corre-spond to the weight of the lowest peak for CuGeO 3( )and for NaV 2O 5( ).comments and IDRIS (Orsay)for allocation of CPU time on the C94and C98CRAY supercomputers.After completion of this work,we learnt of a related work by A.W.Sandvik et al.(cond-mat/9706046)using a Quantum Monte Carlo approach.Rev.B53,11606(1996).[15]F.D.M.Haldane,Phys.Rev.B25,4925(1982).[16]K.Okamoto and K.Nomura,Phys.Lett.A169,433(1992).[17]D.Augier, D.Poilblanc,S.Haas, A.Delia andE.Dagotto,Phys.Rev.B56,Rxxx(1997).[18]S.Haas and E.Dagotto,Phys.Rev.B52,R14396(1995).[19]D.Poilblanc,J.Riera,C.A.Hayward,C.Berthier andM.Hortavi´c,Phys.Rev.B55,Rxxx(May,1997).[20]V.N.Muthukumar et al.,Phys.Rev.B54,R9635(1996).[21]U.Brandt and H.Leschke,Z.Physik271,295(1974);see also K.Hepp and E.H.Lieb,Phys.Rev.A8,2517 (1973).[22]Multiple phonons have been considered in related mod-els of correlated electrons by e.g.R.Fehrenbacher,Phys.Rev.B49,12230(1994)and 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