On the Mathematical Modeling of Memristors

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数学模型部分词汇翻译

数学模型部分词汇翻译

数学模型:[英文]:mathematical model[解释]:对于现实世界的某一特定对象,为了某个特定的目的,通过一些必要的假设和简化后所作的数学描述。

利用模型,通过数学的分析处理,能够对原型的现实性态给出深层次的解释,或预测原型未来的状况或提供处理原型的控制或优化的决策。

它是数学理论和方法用以解决现实世界实际问题的一个重要途径。

例如牛顿第二定律所描述的力和运动的关系 F = ma = md 2 s / dt 2 给出了受外力 F 作用的物体运动的距离 s ( t )与 F 的关系。

它是一个数学上的二阶微分方程,假设物体为一个质点,不存在阻力,摩擦力等的前提下描述了物体的运动与所受外力的依赖关系。

这就是动力学一个最基本的数学模型。

利用它就可以从理论上探讨大量的动力学的现象。

当代由于数学向各门学科的全面渗透,数学不仅仅是物理学的研究工具,它已成为各门学科的一个重要的研究手段,建立数学模型最重要的步骤是首先要把研究对象通过化简,归结出它的数学结构,以便于使用数学理论和方法。

由于数学模型在科学发展中的重要性,它和数学建模已经逐渐从各门学科中独立出来,成为应用数学的一个重要的方向而进入学校的教学计划。

与数学的演绎推理不同,数学模型是运用数学的语言和工具,对现实世界的信息通过假设、化简加以翻译归纳的产物,因此随着研究目的、简化方式的不同,同一个原型的数学模型可以有不同的表现方式,它可以是确定型的,也可以是随机的;可以是连续型的,也可以是离散的。

因此对于同一个原型,可以使用不同的数学分支,通过相应的模型进行研究。

当然通过数学抽象出来的模型较之原型有更宽的覆盖面,甚至于能够描述不同学科有关对象的变化关系。

由于现实世界的复杂性,科学技术发展到今天,还不能给出普遍适用的建立数学模型的准则和技巧。

在一些使用模型较多的研究领域内,已经开始形成了自己的数学模型及建模体系,例如种群生态学中的数学模型,经济学中的数学模型,天气预报的数学模型,当然也包括理论力学——作为物理中运动和力学的数学模型。

Advanced Mathematical Modeling Techniques

Advanced Mathematical Modeling Techniques

Advanced Mathematical ModelingTechniquesIn the realm of scientific inquiry and problem-solving, the application of advanced mathematical modeling techniques stands as a beacon of innovation and precision. From predicting the behavior of complex systems to optimizing processes in various fields, these techniques serve as invaluable tools for researchers, engineers, and decision-makers alike. In this discourse, we delve into the intricacies of advanced mathematical modeling techniques, exploring their principles, applications, and significance in modern society.At the core of advanced mathematical modeling lies the fusion of mathematical theory with computational algorithms, enabling the representation and analysis of intricate real-world phenomena. One of the fundamental techniques embraced in this domain is differential equations, serving as the mathematical language for describing change and dynamical systems. Whether in physics, engineering, biology, or economics, differential equations offer a powerful framework for understanding the evolution of variables over time. From classical ordinary differential equations (ODEs) to their more complex counterparts, such as partial differential equations (PDEs), researchers leverage these tools to unravel the dynamics of phenomena ranging from population growth to fluid flow.Beyond differential equations, advanced mathematical modeling encompasses a plethora of techniques tailored to specific applications. Among these, optimization theory emerges as a cornerstone, providing methodologies to identify optimal solutions amidst a multitude of possible choices. Whether in logistics, finance, or engineering design, optimization techniques enable the efficient allocation of resources, the maximization of profits, or the minimization of costs. From linear programming to nonlinear optimization and evolutionary algorithms, these methods empower decision-makers to navigate complex decision landscapes and achieve desired outcomes.Furthermore, stochastic processes constitute another vital aspect of advanced mathematical modeling, accounting for randomness and uncertainty in real-world systems. From Markov chains to stochastic differential equations, these techniques capture the probabilistic nature of phenomena, offering insights into risk assessment, financial modeling, and dynamic systems subjected to random fluctuations. By integrating probabilistic elements into mathematical models, researchers gain a deeper understanding of uncertainty's impact on outcomes, facilitating informed decision-making and risk management strategies.The advent of computational power has revolutionized the landscape of advanced mathematical modeling, enabling the simulation and analysis of increasingly complex systems. Numerical methods play a pivotal role in this paradigm, providing algorithms for approximating solutions to mathematical problems that defy analytical treatment. Finite element methods, finite difference methods, and Monte Carlo simulations are but a few examples of numerical techniques employed to tackle problems spanning from structural analysis to option pricing. Through iterative computation and algorithmic refinement, these methods empower researchers to explore phenomena with unprecedented depth and accuracy.Moreover, the interdisciplinary nature of advanced mathematical modeling fosters synergies across diverse fields, catalyzing innovation and breakthroughs. Machine learning and data-driven modeling, for instance, have emerged as formidable allies in deciphering complex patterns and extracting insights from vast datasets. Whether in predictive modeling, pattern recognition, or decision support systems, machine learning algorithms leverage statistical techniques to uncover hidden structures and relationships, driving advancements in fields as diverse as healthcare, finance, and autonomous systems.The application domains of advanced mathematical modeling techniques are as diverse as they are far-reaching. In the realm of healthcare, mathematical models underpin epidemiological studies, aiding in the understanding and mitigation of infectious diseases. From compartmental models like the SIR model to agent-based simulations, these tools inform public health policies and intervention strategies, guiding efforts to combat pandemics and safeguard populations.In the domain of climate science, mathematical models serve as indispensable tools for understanding Earth's complex climate system and projecting future trends. Coupling atmospheric, oceanic, and cryospheric models, researchers simulate the dynamics of climate variables, offering insights into phenomena such as global warming, sea-level rise, and extreme weather events. By integrating observational data and physical principles, these models enhance our understanding of climate dynamics, informing mitigation and adaptation strategies to address the challenges of climate change.Furthermore, in the realm of finance, mathematical modeling techniques underpin the pricing of financial instruments, the management of investment portfolios, and the assessment of risk. From option pricing models rooted in stochastic calculus to portfolio optimization techniques grounded in optimization theory, these tools empower financial institutions to make informed decisions in a volatile and uncertain market environment. By quantifying risk and return profiles, mathematical models facilitate the allocation of capital, the hedging of riskexposures, and the management of investment strategies, thereby contributing to financial stability and resilience.In conclusion, advanced mathematical modeling techniques represent a cornerstone of modern science and engineering, providing powerful tools for understanding, predicting, and optimizing complex systems. From differential equations to optimization theory, from stochastic processes to machine learning, these techniques enable researchers and practitioners to tackle a myriad of challenges across diverse domains. As computational capabilities continue to advance and interdisciplinary collaborations flourish, the potential for innovation and discovery in the realm of mathematical modeling knows no bounds. By harnessing the power of mathematics, computation, and data, we embark on a journey of exploration and insight, unraveling the mysteries of the universe and shaping the world of tomorrow.。

Mathematical Modelling and Numerical Analysis Will be set by the publisher Modelisation Mat

Mathematical Modelling and Numerical Analysis Will be set by the publisher Modelisation Mat
& luskin@
c EDP Sciences, SMAI 1999
2

PAVEL BEL K AND MITCHELL LUSKIN
In general, the analysis of stability is more di cult for transformations with N = 4 such as the tetragonal to monoclinic transformations studied in this paper and N = 6 since the additional wells give the crystal more freedom to deform without the cost of additional energy. In fact, we show here that there are special lattice constants for which the simply laminated microstructure for the tetragonal to monoclinic transformation is not stable. The stability theory can also be used to analyze laminates with varying volume fraction 24 and conforming and nonconforming nite element approximations 25, 27 . We also note that the stability theory was used to analyze the microstructure in ferromagnetic crystals 29 . Related results on the numerical analysis of nonconvex variational problems can be found, for example, in 7 12,14 16,18,19,22,26,30 33 . We give an analysis in this paper of the stability of a laminated microstructure with in nitesimal length scale that oscillates between two compatible variants. We show that for any other deformation satisfying the same boundary conditions as the laminate, we can bound the pertubation of the volume fractions of the variants by the pertubation of the bulk energy. This implies that the volume fractions of the variants for a deformation are close to the volume fractions of the laminate if the bulk energy of the deformation is close to the bulk energy of the laminate. This concept of stability can be applied directly to obtain results on the convergence of nite element approximations and guarantees that any nite element solution with su ciently small bulk energy gives reliable approximations of the stable quantities such as volume fraction. In Section 2, we describe the geometrically nonlinear theory of martensite. We refer the reader to 2,3 and to the introductory article 28 for a more detailed discussion of the geometrically nonlinear theory of martensite. We review the results given in 34, 35 on the transformation strains and possible interfaces for tetragonal to monoclinic transformations corresponding to the shearing of the square and rectangular faces, and we then give the transformation strain and possible interfaces corresponding to the shearing of the plane orthogonal to a diagonal in the square base. In Section 3, we give the main results of this paper which give bounds on the volume fraction of the crystal in which the deformation gradient is in energy wells that are not used in the laminate. These estimates are used in Section 4 to establish a series of error bounds in terms of the elastic energy of deformations for the L2 approximation of the directional derivative of the limiting macroscopic deformation in any direction tangential to the parallel layers of the laminate, for the L2 approximation of the limiting macroscopic deformation, for the approximation of volume fractions of the participating martensitic variants, and for the approximation of nonlinear integrals of deformation gradients. Finally, in Section 5 we give an application of the stability theory to the nite element approximation of the simply laminated microstructure.

Mathematical Modeling

Mathematical Modeling

Mathematical ModelingArnold NeumaierNovember6,2003Institut f¨u r Mathematik,Universit¨a t WienStrudlhofgasse4,A-1090Wien,Austriaemail:Arnold.Neumaier@univie.ac.atWWW:http://www.mat.univie.ac.at/∼neum/1Why mathematical modeling?Mathematical modeling is the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides in-sight,answers,and guidance useful for the originating application.Mathematical modeling•is indispensable in many applications•is successful in many further applications•gives precision and direction for problem solution•enables a thorough understanding of the system modeled•prepares the way for better design or control of a system•allows the efficient use of modern computing capabilitiesLearning about mathematical modeling is an important step from a theoretical mathematical training to an application-oriented mathematical expertise,and makes the studentfit for mastering the challenges of our modern technological culture.2A list of applicationsIn the following,I give a list of applications whose modeling I understand,at least in some detail.All areas mentioned have numerous mathematical challenges.This list is based on my own experience;therefore it is very incomplete as a list of applications of mathematics in general.There are an almost endless number of other areas with interesting mathematical problems.Indeed,mathematics is simply the language for posing problems precisely and unambiguously (so that even a stupid,pedantic computer can understand it).1Anthropology•Modeling,classifying and reconstructing skulls Archeology•Reconstruction of objects from preserved fragments •Classifying ancient artificesArchitecture•Virtual realityArtificial intelligence•Computer vision•Image interpretation•Robotics•Speech recognition•Optical character recognition •Reasoning under uncertaintyArts•Computer animation(Jurassic Park)Astronomy•Detection of planetary systems •Correcting the Hubble telescope•Origin of the universe•Evolution of starsBiology•Protein folding•Humane genome project2•Population dynamics •Morphogenesis•Evolutionary pedigrees •Spreading of infectuous diseases(AIDS)•Animal and plant breeding(genetic variability) Chemical engineering•Chemical equilibrium•Planning of production unitsChemistry•Chemical reaction dynamics •Molecular modeling•Electronic structure calculationsComputer science•Image processing•Realistic computer graphics(ray tracing) Criminalistic science•Finger print recognition•Face recognitionEconomics•Labor data analysisElectrical engineering•Stability of electric curcuits •Microchip analysis•Power supply network optimizationFinance•Risk analysis•Value estimation of options3Fluid mechanics•Wind channel•TurbulenceGeosciences•Prediction of oil or ore deposits•Map production•Earth quake predictionInternet•Web search•Optimal routingLinguistics•Automatic translationMaterials Science•Microchip production•Microstructures•Semiconductor modelingMechanical engineering•Stability of structures(high rise buildings,bridges,air planes)•Structural optimization•Crash simulationMedicine•Radiation therapy planning•Computer-aided tomography•Blood circulation models4Meteorology•Weather prediction•Climate prediction(global warming,what caused the ozone hole?) Music•Analysis and synthesis of soundsNeuroscience•Neural networks•Signal transmission in nervesPharmacology•Docking of molecules to proteins•Screening of new compoundsPhysics•Elementary particle tracking•Quantumfield theory predictions(baryon spectrum)•Laser dynamicsPolitical Sciences•Analysis of electionsPsychology•Formalizing diaries of therapy sessionsSpace Sciences•Trajectory planning•Flight simulation•Shuttle reentryTransport Science•Air traffic scheduling•Taxi for handicapped people•Automatic pilot for cars and airplanes53Basic numerical tasksThe following is a list of categories containing the basic algorithmic toolkit needed for ex-tracting numerical information from mathematical models.Due to the breadth of the subject,this cannot be covered in a single course.For a thorough education one needs to attend courses(or read books)at least on numerical analysis(which usually covers some numerical linear algebra,too),optimization,and numerical methods for partial differential equations.Unfortunately,there appear to be few good courses and books on(higher-dimensional)nu-merical data analysis.Numerical linear algebra•Linear systems of equations•Eigenvalue problems•Linear programming(linear optimization)•Techniques for large,sparse problemsNumerical analysis•Function evaluation•Automatic and numerical differentiation•Interpolation•Approximation(Pad´e,least squares,radial basis functions)•Integration(univariate,multivariate,Fourier transform)•Special functions•Nonlinear systems of equations•Optimization=nonlinear programming•Techniques for large,sparse problems6Numerical data analysis(=numerical statistics)•Visualization(2D and3D computational geometry)•Parameter estimation(least squares,maximum likelihood)•Prediction•Classification•Time series analysis(signal processing,filtering,time correlations,spectral analysis)•Categorical time series(hidden Markov models)•Random numbers and Monte Carlo methods•Techniques for large,sparse problemsNumerical functional analysis•Ordinary differential equations(initial value problems,boundary value problems,eigen-value problems,stability)•Techniques for large problems•Partial differential equations(finite differences,finite elements,boundary elements, mesh generation,adaptive meshes)•Stochastic differential equations•Integral equations(and regularization)Non-numerical algorithms•Symbolic methods(computer algebra)•Sorting•Compression•Cryptography•Error correcting codes74The modeling diagramThe nodes of the following diagram represent information to be collected,sorted,evaluated,and organized.MMathematical ModelSProblem StatementRReportTTheoryPProgramsNNumerical Methods..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................The edges of the diagram represent activities of two-way communication (flow of relevantinformation)between the nodes and the corresponding sources of information.S.Problem Statement •Interests of customer/boss •Often ambiguous/incomplete •Wishes are sometimes incompatibleM.Mathematical Model •Concepts/Variables •Relations •Restrictions •Goals•Priorities/Quality assignments8T.Theory•of Application•of Mathematics•Literature searchN.Numerical Methods•Software libraries•Free software from WWW•Background informationP.Programs•Flow diagrams•Implementation•User interface•DocumentationR.Report•Description•Analysis•Results•Model validation•Visualization•Limitations•RecommendationsUsing the modeling diagram•The modeling diagram breaks the modeling task into16=6+10different processes.•Each of the6nodes and each of the10edges deserve repeated attention,usually at every stage of the modeling process.9•The modeling is complete only if the’traffic’along all edges becomes insignificant.•Generally,working on an edge enriches both participating nodes.•If stuck along one edge,move to another one!Use the general rules below as a check list!•Frequently,the problem changes during modeling,in the light of the understanding gained by the modeling process.At the end,even a vague or contradictory initial problem description should have mutated into a reasonably well-defined description, with an associated precisely defined(though perhaps inaccurate)mathematical model.5General rules•Look at how others model similar situations;adapt their models to the present situa-tion.•Collect/ask for background information needed to understand the problem •Start with simple models;add details as they become known and useful or necessary.•Find all relevant quantities and make them precise.•Find all relevant relationships between quantities([differential]equations,inequalities, case distinctions).•Locate/collect/select the data needed to specify these relationships.•Find all restrictions that the quantities must obey(sign,limits,forbidden overlaps, etc.).Which restrictions are hard,which soft?How soft?•Try to incorporate qualitative constraints that rule out otherwise feasible results(usu-ally from inadequate previous versions).•Find all goals(including conflicting ones)•Play the devil’s advocate tofind out and formulate the weak spots of your model.•Sort available information by the degree of impact expected/hoped for.•Create a hierarchy of models:from coarse,highly simplifying models to models with all known details.Are there useful toy models with simpler data?Are there limiting cases where the model simplifies?Are there interesting extreme cases that help discover difficulties?10•First solve the coarser models(cheap but inaccurate)to get good starting points for thefiner models(expensive to solve but realistic)•Try to have a simple working model(with report)after1/3of the total time planned for the e the remaining time for improving or expanding the model based on your experience,for making the programs more versatile and speeding them up,for polishing documentation,etc.•Good communication is essential for good applied work.•The responsibility for understanding,for asking the questions that lead to it,for recog-nizing misunderstanding(mismatch between answers expected and answers received), and for overcoming them lies with the mathematician.You cannot usually assume your customer to understand your scientific jargon.•Be not discouraged.Failures inform you about important missing details in your understanding of the problem(or the customer/boss)–utilize this information!•There are rarely perfect solutions.Modeling is the art offinding a satisfying compro-mise.Start with the highest standards,and lower them as the deadline approaches.If you have results early,raise your standards again.•Finish your work in time.Lao Tse:”People often fail on the verge of success;take care at the end as at the beginning, so that you may avoid failure.”6Conflicts•fast–slow•cheap–expensive•short term–long term•simplicity–complexity•low quality–high quality•approximate–accurate•superficial–in depth•sketchy–comprehensive11•concise–detailed•short description–long descriptionEinstein:”A good theory”(or model)”should be as simple as possible,but not simpler.”•perfecting a program–need for quick results•collecting the theory–producing a solution•doing research–writing up•quality standards–deadlines•dreams–actual resultsThe conflicts described are creative and constructive,if one does not give in too easily.As a good material can handle more physical stress,so a good scientist can handle more stress created by conflict.”We shall overcome”–a successful motto of the black liberation movement,created by a strong trust in God.This generalizes to other situations where one has to face difficulties, too.Among other qualities it has,university education is not least a long term stress test–if you got your degree,this is a proof that you could overcome significant barriers.The job market pays for the ability to persist.7Attitudes•Do whatever you do with love.Love(even in difficult circumstances)can be learnt;it noticeably improves the quality of your work and the satisfaction you derive from it.•Do whatever you do as a service to others.This will improve your attention,the feedback you’ll get,and the impact you’ll have.•Take responsibility;ask if in doubt;read to confirm your understanding.This will remove many impasses that otherwise would delay your work.Jesus:”Ask,and you will receive.Search,and you willfind.Knock,and the door will be opened for you.”8ReferencesSee my home page,quoted on page1.12。

Mathematical modeling of drying of pretreated

Mathematical modeling of drying of pretreated

Mathematical modeling of drying of pretreated and untreated pumpkinT.Y.Tunde-Akintunde &G.O.OgunlakinRevised:13February 2011/Accepted:26April 2011#Association of Food Scientists &Technologists (India)2011Abstract In this study,drying characteristics of pretreated and untreated pumpkin were examined in a hot-air dryer at air temperatures within a range of 40–80°C and a constant air velocity of 1.5m/s.The drying was observed to be in the falling-rate drying period and thus liquid diffusion is the main mechanism of moisture movement from the internal regions to the product surface.The experimental drying data for the pumpkin fruits were used to fit Exponential,General exponential,Logarithmic,Page,Midilli-Kucuk and Parabolic model and the statistical validity of models tested were determined by non-linear regression analysis.The Parabolic model had the highest R 2and lowest χ2and RMSE values.This indicates that the Parabolic model is appropriate to describe the dehydration behavior for the pumpkin.Keywords Pumpkin fruits .Hot air drying .Effective diffusivity .Mathematical modellingNomenclature a Drying constant b Drying constant c Drying constant DR Drying rate (g water/g dry matter*h)k Drying constant,1/min M e Equilibrium moisture content(kg water/kg dry matter)M i Initial moisture content (kg water/kg dry matter)M R Dimensionless moisture ratioMR exp,i Experimental dimensionless moisture ratio MR pre,i Predicted dimensionless moisture ratio M t Moisture content at any time of drying (kg water/kg dry matter)M t +dtMoisture content at t +dt (kg water/kg dry matter)N Number of observationsn Drying constant,positive integer R 2Coefficient of determination t Time (min)W Amount of evaporated water (g)W 0Initial weight of sample (g)W 1Sample dry matter mass (g)z Number of constants χ2Reduced chi-squareIntroductionPumpkin (cucurbita mixta )is a fruit rich in Vitamin A,potassium,fiber and carbohydrates.It is a versatile fruit that can be used for either animal feed or for human consumption as a snack,or made into soups,pies and bread.The high moisture content of the fruit makes it susceptible to deterioration after harvest.The most common form of preservation being done locally is drying.It is a means of improving storability by increasing shelf-life of the food product.Dried products can be stored for months or even years without appreciable loss of nutrients.Drying also assist in reducing post harvest losses of fruits and vegetables especially which can be as high as 70%(Tunde-Akintunde and Akintunde 1996).Sun drying is the common method of drying in the tropical region.However the process is weather depen-T.Y .Tunde-Akintunde (*):G.O.Ogunlakin Department of Food Science and Engineering,Ladoke Akintola University of Technology,PMB 4000,Ogbomoso,Oyo State,Nigeria e-mail:toyositunde@J Food Sci TechnolDOI 10.1007/s13197-011-0392-2dent Also,the drying of food products with the use of sun-drying usually takes a long time thus resulting in products of low quality.There is a need for suitable alternatives in order to improve product quality.Hot air dryers give far more hygienic products and provide uniform and rapid drying which is more suitable for the food drying processes(Kingsly et al.2007a;Doymaz 2004a).Many types of hot-air dryers are being used for drying agricultural products.However the design of such driers for high-moisture foods constitutes a very complex problem owing to the characteristics of vegetable tissues.One of the most important factor that needs to be considered in the design of driers is the proper prediction of drying rate for shrinking particles and hence the appropriate drying time in the dryer needs to be investigated.This can be achieved by determination of the drying characteristics of the food material.Therefore the thin layer drying studies which normally form the basis of understanding the drying characteristics of food materials has to be carried out for each food material.Studies have been carried out on thin layer drying of some food materials(Gaston et al.2004), fruits(Doymaz2004a,c;Simal et al.2005),leaves and grasses(Demir et al.2004).Though there has been some literature on drying of pumpkin(Alibas2007;Doymaz2007b;Sacilik2007),the selection of appropriate model to describe the drying process for the variety common in the country and within the experimental conditions considered in this study is yet to be done.Thin layer drying models used in the analysis of drying characteristics are usually theoretical,semi-theoretical or purely empirical.A number of semi-theoretical drying models have been widely used by various researchers(Sharma and Prasad2004;Simal et al.2005;Sogi et al.2003;Togrul and Pehlivan2004).A number of pre-treatments can be applied depending on the food to be dried,its end use,and availability(Doymaz 2010).Pretreatment of food materials which includes; blanching,osmotic dehydration,soaking in ascorbic acid before or on drying have been investigated to prevent the loss of colour by inactivating enzymes and relaxing tissue structure.This improves the effect of drying by reducing the drying time and gives the eventual dried products of good nutritional quality(Kingsly et al.2007b;Doymaz 2010).Various commercially used pretreatments include potassium and sodium hydroxide,potassium meta bisul-phate,potassium carbonate,methyl and ethyl ester emul-sions,ascorbic and citric acids,(Kingsly et al.2007a,b; Doymaz2004a,b;El-Beltagy et al.2007).However non-chemical forms of pretreatment are generally preferred especially among small-scale processors in the tropics. Blanching as a pre-treatment is used to arrest some physiological processes before drying vegetables and fruits.It is a heat pre-treatment that inactivates the enzymes responsible for commercially unacceptable darkening and off-flavours.Blanching of fruits and vegetables is generally carried out by heating them with steam or hot water(Tembo et al.2008).However other forms of blanching i.e.oil-water blanching have been used by Akanbi et al.(2006)in a previous study for pretreating chilli.These forms of blanching were observed to have an effect on the drying rate and quality of the dried chili. However oil-water blanching pretreatments have not been studied for pumpkin.The aim of this study was:(a)to study the effect of the different blanching pre-treatments on the drying times and rate,and(b)to fit the experimental data to seven mathematical models available in the literature.Materials and methodsExperimental procedureFresh pumpkin fruit were purchased from Arada,a local market in Ogbomoso,Nigeria.The initial average moisture content of fresh pumpkin samples was deter-mined by oven drying method(AOAC1990),and it was found to be91.7%(wet basis)or10.90(g water/g dry matter).The samples were washed and peeled after which the seeds were removed.The pumpkin was then sliced into pieces of5mm×5mm dimensions.The blanching pre-treatments for inactivation of enzymes are as indicated below while untreated samples(UT)were used as the control.i)Samples submerged in boiling water for3minutes andcooling immediately in tap water-(WB)ii)Samples steamed over boiling water in a water bath (WBH14/F2,England)for3minutes and cooled immediately in tap water-(SB)iii)Samples dipped for3minutes in a homogenized mixture of oil and water of ratio1:20(v/v)with0.1g of butylated hydroxyl anisole(BHA)heated to95°C-(O/W B)In all the pretreatments the excess water was removed by blotting the pretreated pumpkin samples with tissue paper.The moisture contents after pretreatment were12.69(g water/g dry matter)for water and oil-water blanching and11.5(g water/g dry matter)for steam blanching.Drying procedureThe drying experiments of pumpkin samples were carried out in a hot-air dryer(Gallenkamp,UK)having three tiers of trays. Perforated trays having an area of approximately0.2m2wereJ Food Sci Technolplaced on each tier and the trays were filled with a single layer of the pumpkin samples.The air passes from the heating unit and is heated to the desired temperature and channeled to the drying chamber.The hot air passes from across the surface and perforated bottom of the drying material and the direction of air flow was parallel to the samples.The samples utilised for each experimental condition weighed 200±2g.Drying of the pumpkin was carried out at drying temperatures of 40to 800C with 20°C increment,and a constant air velocity of 1.5m/s for all circumstances.The dryer was adjusted to be selected temperature for about half an hour before the start of experiment to achieve the steady state conditions.Weight loss of samples was measured at various time intervals,ranging from 30min at the beginning of the drying to 120min during the last stages of the drying process by means of a digital balance (PH Mettler)with an accuracy of ±0.01g.The samples were taken out of the dryer and weighed during each of the time intervals.Drying was stopped when constant weight was reached with three consecutive readings.The experiments were repeated twice and the average of the moisture ratio at each value was used for drawing the drying curves.Model nameModelReferencesExponential modelMR ¼exp Àkt ðÞEl-Beltagy et al.(2007)Generalized exponential model MR ¼Aexp Àkt ðÞShittu and Raji (2008)Logarithmic model MR ¼aexp ðÀkt Þþc Akpinar and Bicer (2008)(Page ’s model)MR ¼exp Àkt n ðÞSingh et al.(2008)Midilli –Kucuk model MR ¼aexp Àkt n ðÞþbt Midilli and Kucuk (2003)Parabolic modelMR ¼a þbt þct 2Sharma and Prasad (2004),Doymaz (2010)Table 1Mathematical models fitted to pretreated pumpkin drying curves100200300400Drying Time (min)WB SBO/W B UT0.10.20.30.40.50.60.70.80.910100200300Drying Time (min)M o i s t u r e R a t i o00.10.20.30.40.50.60.70.80.91M o i s t u r e R a t i oWB SB O/W B UT00.10.20.30.40.50.60.70.80.91M o i s t u r e R a t i o100200300Drying Time (min)WB SB O/W B UTbacFig.1Drying curve of pump-kin slices dried at (a )40,(b )60and (c )80°C (WB-water blanched,SB-steam blanched,O/W B-oil water blanched,UT-untreated).Each observation is a mean of two replicate experiments (n =2)J Food Sci TechnolMathematical modelingThe moisture content at any time of drying (kg water/kg dry matter),M t was calculated as follows:M t ¼W o ÀW ðÞÀW 1W 1ð1ÞWhere W 0is the initial weight of sample,W is the amount of evaporated water and W 1is the sample dry matter mass.The reduction of moisture ratio with drying time was used to analyse the experimental drying data.The moisture ratio,M R,was calculated as follows:M R ¼M t ÀM eM i ÀM e¼exp ÀKt ðÞð2ÞWhere M t ,M i and M e are moisture content at any time of drying (kg water/kg dry matter),initial moisture content (kg water/kg dry matter)and equilibrium moisture content (kg water/kg dry matter),respectively.The equilibrium moisture contents (EMCs)were deter-mined by drying until no further change in weight wasobserved for the pumpkin samples in each treatment and drying condition (Hii et al.2009)The drying constant,K ,is determined by plotting experimental drying data for each pretreatment and drying temperature in terms of Ln M R against time (t)where M R is moisture ratio.The slope,k,is obtained from the straight line graphs above.The drying rate for the pumpkin slices was calculated as follows:DR ¼M t þdt ÀM tdtð3ÞWhere DR is drying rate,M t +dt is moisture content at t +dt (kg water/kg dry matter),t is time (min).The moisture ratio curves obtained were fitted with six semi-theoretical thin layer-drying models,Exponential,Generalized exponential,Page,Logarithmic,Midilli -Kucuk and Parabolic models (Table 1)in order to describe the drying characteristics of pretreated pumpkin.These linear forms of these models were fitted in the experimental data using regression technique.To evaluate the models,a nonlinear regression procedure was per-formed for six models using SPSS (Statistical Package for00.020.040.060.080.10.12Drying Time (h)D r y i n g r a t e (g w a t e r /g D M .h )WB SB O/W B UT00.020.040.060.080.10.120.140.160.180246Drying time (h)D r y i n g r a t e (g w a t e r /g D M .h )WB SB O/W B UT00.010.020.030.040.050.060246Drying Time (h)D r y i n g R a t e (g w a t e r /g D M .h )WB SB O/W B UTbacFig.2Drying rate curve of pretreated pumpkin dried at (a )40,(b )60and (c )80°C (WB-water blanched,SB-steam blanched,O/W B-oil water blanched,UT-untreated).Each observation is a mean of two replicate experiments (n =2)J Food Sci Technolsocial scientists)11.5.1software package.The correlation coefficient (R 2)was one of the primary criteria to select the best equation to account for variation in the drying curves of dried samples.In addition to R 2,other statistical parameters such as reduced mean square of the deviation (χ2)and root mean square error (RMSE)were used to determine the quality of the fit.The higher the value of R 2and the lower the values of χ2and RMSE were chosen as the criteria for goodness of fit.(Togrul and Pehlivan 2002;Demir et al.2004;Doymaz 2004b ;Goyal et al.2006).The above parameters can be calculated as follows:#2¼PN i ¼1MR ðexp ;i ÞÀMR ðpred ;i ÞÀÁN Àz2ð4ÞRMSE ¼1N XN i ¼1MR pred ;i ÀMR exp ;i ÀÁ2"#12ð5ÞWhere MR exp,i and MR pre,i are experimental and pre-dicted dimensionless moisture ratios,respectively;N is number of observations;z is number of constantsResults and discussion Drying characteristicsThe characteristics drying curves showing the changes in moisture ratio of pretreated pumpkin with time at drying temperatures of 40,60and 80°C are given in Fig.1.Figure 2show the changes in drying rate as a function of drying time at the same temperatures.It is apparent that moisture ratio decreases continuously with drying time.According to the results in Fig.1,the drying air temperature and pre-treatments had a significant effect on the moisture ratio of the pumpkin samples as expected.This is in agreement with the observations of Doymaz (2010)for apple slices.The figures show that the drying time decreased with increase in drying air temperature.Similar results were also reported for food products by earlierTable 2Drying constant (K)values for pretreatment methods and drying temperaturesDrying Temperature (°C)Pretreatment method Steam Blanching Water Blanching Oil-Water Blanching Untreated 400.45290.44580.42260.4029600.62230.61630.60150.5637800.78840.78710.78370.7803Model nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.95180.0058240.071384Exponential Model 0.96550.0051960.062424Logarithmic Model 0.96840.0056210.059272Page Model0.973750.0032790.048397Midilli –Kucuk model 0.863980.0277520.109059Parabolic model 0.99630.0004730.0164460°CGeneralized Exponential Model 0.98740.0013820.035049Exponential Model 0.98820.0013460.032351Logarithmic Model 0.99420.0007850.022878Page Model0.980390.001110.028847Midilli –Kucuk model 0.818570.0145140.085189Parabolic model 0.99410.0004480.01672680°CGeneralized Exponential Model 0.95640.0038620.058588Exponential Model 0.96220.0038610.054796Logarithmic Model 0.97250.0107950.082138Page Model0.95950.0086160.080386Midilli –Kucuk model 0.937020.022370.105759Parabolic model0.98650.0032920.04685Table 3Curve fitting criteria forthe various mathematical models and parameters for pumpkin pre-treated with water blanching and dried at temperatures of 40,60and 80°CJ Food Sci TechnolModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.95930.0048430.065095Exponential Model 0.96890.0042740.056615Logarithmic Model 0.968960.0050650.056262Page Model0.95990.0045870.057237Midilli –Kucuk model 0.818470.0342380.121134Parabolic model 0.99490.0006490.01925260°CGeneralized Exponential Model 0.987370.0013540.034688Exponential Model 0.98760.0014480.033563Logarithmic Model 0.990810.0008640.023994Page Model0.97490.0012870.031073Midilli –Kucuk model 0.982270.0169220.091982Parabolic model 0.99410.0005980.01933680°CGeneralized Exponential Model 0.94360.0054840.069818Exponential Model 0.951870.0108440.060609Logarithmic Model 0.958190.0052010.082325Page Model0.922370.0065670.070179Midilli –Kucuk model 0.955020.0210930.102696Parabolic model0.979590.0047230.058882Table 4Curve fitting criteria for the various mathematical models and parameters for pumpkin pre-treated with steam blanching and dried at temperatures of 40,60and 80°CModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.94540.0067290.076734Exponential Model 0.96030.0059530.066819Logarithmic Model 0.961750.0069530.065922Page Model0.96450.0045960.057295Midilli –Kucuk model 0.838010.0328130.118587Parabolic model 0.99560.0008220.02167260°CGeneralized Exponential Model 0.976650.0017910.039903Exponential Model 0.982710.002020.039642Logarithmic Model 0.986950.0016620.033287Page Model0.974690.0012580.03072Midilli –Kucuk model 0.88840.0083740.064708Parabolic model 0.99280.0004530.01682280°CGeneralized Exponential Model 0.947850.0047940.065279Exponential Model 0.95540.00480.061101Logarithmic Model 0.96250.0120260.086696Page Model0.932230.0106440.089346Midilli –Kucuk model 0.94940.0200940.100234Parabolic model0.983570.0043540.053874Table 5Curve fitting criteria for the various mathematical models and parameters for pumpkin pre-treated with oil-water blanching and dried at temperatures of 40,60and 80°CJ Food Sci TechnolModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.948760.0058960.071829Exponential Model 0.95850.0056790.065264Logarithmic Model 0.959110.0067350.06488Page Model0.94120.0064750.068007Midilli –Kucuk model 0.78980.0399250.130808Parabolic model 0.985950.0018690.03267860°CGeneralized Exponential Model 0.97350.0024360.046534Exponential Model 0.97490.0028840.047363Logarithmic Model 0.981070.0011850.028035Page Model0.95840.0021470.040127Midilli –Kucuk model 0.76690.0163070.090296Parabolic model 0.990390.0011790.0272280°CGeneralized Exponential Model 0.949980.0045840.063836Exponential Model 0.95720.0047680.060896Logarithmic Model 0.959850.0050460.057999Page Model0.920160.0077380.069542Midilli –Kucuk model 0.95070.0179590.09476Parabolic model0.97750.0042850.056692Table 6Curve fitting criteria for the various mathematical models and parameters for untreated pumpkin and dried at temper-atures of 40,60and 80°CDrying Time (h)M o i s t u r e R a t i oDrying Time (h)M o i s t u r e R a t i oDrying Time (h)M o i s t u r e R a t i obacFig.3Comparison of experi-mental and predicted moisture ratio values using Parabolic model for pretreated pumpkin dried at (a )40,(b )60and (c )80°C (WBE-water blanched experimental,SBE-steam blanched experimental,O/W BE-oil water blanchedexperimental,UTE-untreated experimental,WBP-waterblanched predicted,SBP-steam blanched predicted,O/W BP-oil water blanched predicted,UTP-untreated predicted).Each observation is a mean of two replicate experiments (n =2)J Food Sci Technolresearchers(Sacilik and Elicin2006;Lee and Kim2009; Kumar et al.2010).The drying time required to lower the moisture ratio of water blanched samples to0.034when using an air temperature of40°C(5h)was approximately twice that required at a drying air temperature of80°C (2.5h).This same trend occurred for both untreated and other pretreatment methods.The drying time required to reach moisture ratio of0.018for drying temperature of60°C for samples pretreated with steam blanching was3h while the corresponding values for water blanched,oil-water blanched and control samples were3.5,3.9and4h respectively.The difference in drying times of pre-treated samples with steam blanching was16.7%,30%and33.3%shorter than water blanched,oil-water blanched and control samples,re-spectively.Similar trends were observed at drying temper-atures of40and80°C.All the pretreated samples had higher drying curves than the untreated samples generally(Fig.1).This is an indication of the fact that various forms of blanching pretreatments increase the drying rate for pumpkin samples. This is similar to the observations of Goyal et al.(2008), Doymaz(2007a),Kingsly et al.(2007a),Doymaz(2004b) for apples,tomato,peach slices and mulberry fruits.The difference in drying is more pronounced at the initial stages of drying when the major quantity of water is evaporated while at the latter stages of drying the difference in the amount of water evaporated is not as pronounced as the early drying stages.The difference in the drying of the different pretreatments experienced in the initial stages becomes less pronounced with increase in drying temper-ature.This may be because at higher temperatures the driving force due to diffusion from the internal regions to the surface is higher thus overcoming hindrances to drying more effectively resulting in more uniform drying.The K values for pretreated samples were higher than that of untreated samples for all the drying temperatures(Table2). This confirms the fact that the various forms of blanching pretreatments increased the rate at which drying took place.Analysis of the drying rate curves(Fig.2)showed no constant-rate period indicating that drying occurred during the falling-rate period.Therefore,it can be considered a diffusion-controlled process in which the rate of moisture removal is limited by diffusion of moisture from inside to surface of the product.This is similar to the results reported for various agricultural products such Amasya red apples (Doymaz2010),peach(Kingsly et al.2007a),yam(Sobukola et al.2008),and tumeric(Singh et al.2010).Fitting of drying curveSPSS statistical software package for non-linear regression analysis was used to fit moisture ratio against drying time to determine the constants of the four selected drying models.The R2,χ2and RMSE used to determine the goodness of fit of the models are shown in Tables3,4,5 and6.The Parabolic model gave the highest R2value which varied from0.9963to0.9775for experimental conditions considered in this study.The values ofχ2and RMSE for the Parabolic model which varied from0.000448 to0.004723and0.01644to0.058882respectively were the lowest for all the models considered.From the tables,it is obvious that the Parabolic model therefore represents the drying characteristics of pretreated pumpkin(for individual drying runs)better than the other models(Generalized exponential,Exponential,Logarithmic,Page or Midilli–Kucuk)considered in this study.The comparison between experimental moisture ratios and predicted moisture ratios obtained from the Parabolic model at drying air temperature of40°C,60°C and80°C for pumpkin samples are shown in Fig.3.The suitability of the Parabolic model for describing the pumpkin drying behaviour is further shown by a good conformity between experimental and predicted moisture ratios as seen in Fig.3.This is similar to the observations of Doymaz(2010)for drying of red apple slices at55,65and75°C.ConclusionThe effect of temperature and pre-treatments on thin layer drying of pumpkin in a hot-air dryer was investigated. Increase in drying temperature from40to80°C decreased the drying time from5hours to4hours for all the samples considered.The pretreated samples dried faster than the untreated samples.Samples pretreated with steam blanch-ing had shorter drying times(hence higher drying rates) compared to water blanched,oil-water blanched and control samples The entire drying process occurred in falling rate period and constant rate period was not observed.The suitability of four thin-layer equations to describe the drying behaviour of pumpkin was investigated.The model that had the best fit with highest values of R2and lowest values ofχ2,MBE and RMSE was the Parabolic model. Thus this model was selected as being suitable to describe the pumpkin drying process for the experimental conditions considered.ReferencesAkanbi CT,Adeyemi RS,Ojo A(2006)Drying characteristics and sorption isotherm of tomato slices.J Food Eng73:141–146 Akpinar AK,Bicer Y(2008)Mathematical modelling of thin layer drying process of long green pepper in solar dryer and under open sun.Energy Conver Manag49:1367–1375Aliba I(2007)Microwave,air and combined microwave–air-drying parameters of pumpkin slices.LWT40:1445–1451J Food Sci TechnolAOAC(1990)Official methods of analysis,15th edn.Association of Official Analytical Chemists,ArlingtonDemir V,Gunhan T,Yagcioglu AK,Degirmencioglu A(2004) Mathematical modeling and the determination of some quality parameters of air-dried bay leaves.Biosys Eng88(3):325–335 Doymaz I(2004a)Drying kinetics of white mulberry.J Food Eng61(3):341–346Doymaz I(2004b)Pretreatment effect on sun drying of mulberry fruit (Morus alba L.).J Food Eng65(2):205–209Doymaz I(2004c)Convective air drying characteristics of thin layer carrots.J Food Eng61(3):359–364Doymaz I(2007a)Air-drying characteristics of tomatoes.J Food Eng 78:1291–1297Doymaz I(2007b)The kinetics of forced convective air-drying of pumpkin slices.J Food Eng79:243–248Doymaz I(2010)Effect of citric acid and blanching pre-treatments on drying and rehydration of Amasya red apples.Food Bioprod Proc 88(2–3):124–132El-Beltagy A,Gamea GR,Amer Essa AH(2007)Solar drying characteristics of strawberry.J Food Eng78:456–464Gaston AL,Abalone RM,Giner SA,Bruce DM(2004)Effect of modelling assumptions on the effective water diffusivity in wheat.Biosys Eng88(2):175–185Goyal RK,Kingsly ARP,Manikantan MR,Ilyas SM(2006)Thin layer drying kinetics of raw mango slices.Biosys Eng95(1):43–49 Goyal RK,Mujjeb O,Bhargava VK(2008)Mathematical modeling of thin layer drying kinetics of apple in tunnel dryer.Int J Food Eng 4(8):Article8Hii CL,Law CL,Cloke M,Suzannah S(2009)Thin layer drying kinetics of cocoa and dried product quality.Biosys Eng102:153–161 Kingsly RP,Goyal RK,Manikantan MR,Ilyas SM(2007a)Effects of pretreatments and drying air temperature on drying behaviour of peach slice.Int J Food Sci Technol42:65–69Kingsly ARP,Singh R,Goyal RK,Singh DB(2007b)Thin-layer drying behaviour of organically produced tomato.Am J Food Technol 2:71–78Kumar R,Jain S,Garg MK(2010)Drying behaviour of rapeseed under thin layer conditions.J Food Sci Technol47(3):335–338 Lee JH,Kim HJ(2009)Vacuum drying kinetics of Asian white radish (Raphanus sativus L.)slices.LWT-Food Sci Technol42:180–186Midilli A,Kucuk H(2003)Mathematical modeling of thin layer drying of pistachio by using solar energy.Energy Conver Manag 44(7):1111–1122Sacilik K(2007)Effect of drying methods on thin-layer drying characteristics of hull-less seed pumpkin(Cucurbita pepo L.).J Food Eng79:23–30Sacilik K,Elicin AK(2006)The thin layer drying characteristics of organic apple slices.J Food Eng73:281–289Sharma GP,Prasad S(2004)Effective moisture diffusivity of garlic cloves undergoing microwave convective drying.J Food Eng65(4):609–617Shittu TA,Raji AO(2008)Thin layer drying of African Breadfruit (Treculia africana)seeds:modeling and rehydration capacity.Food Bioprocess Technol:1–8.doi:10.1007/s11947-008-0161-zSimal S,Femenia A,Garau MC,Rossello C(2005)Use of exponential,page and diffusion models to simulate the drying kinetics of kiwi fruit.J Food Eng66(3):323–328Singh S,Sharma R,Bawa AS,Saxena DC(2008)Drying and rehydration characteristics of water chestnut(Trapa natans)as a function of drying air temperature.J Food Eng87:213–221Singh G,Arora S,Kumar S(2010)Effect of mechanical drying air conditions on quality of turmeric powder.J Food Sci Technol47(3):347–350Sobukola OP,Dairo OU,Odunewu A V(2008)Convective hot air drying of blanched yam slices.Int J Food Sci Technol43:1233–1238 Sogi DS,Shivhare US,Garg SK,Bawa SA(2003)Water sorption isotherms and drying characteristics of tomato seeds.Biosys Eng 84(3):297–301Tembo L,Chiteka ZA,Kadzere I,Akinnifesi FK,Tagwira F(2008) Blanching and drying period affect moisture loss and vitamin C content in Ziziphus mauritiana(Lamk.).Afric J Biotech7:3100–3106Togrul IT,Pehlivan D(2002)Mathematical modeling of solar drying of apricots in thin layers.J Food Eng55:209–216Togrul IT,Pehlivan D(2004)Modeling of thin layer drying kinetics of some fruits under open air sun drying process.J Food Eng65(3):413–425Tunde-Akintunde TY,Akintunde BO(1996)Post-harvest losses of food crops:sources and solutions.Proceedings of the Annual Conference of the Nigerian Society of Agricultural Engineers, Ile-Ife,Nigeria from November19–22,1996.V ol18:258–261J Food Sci Technol。

数学建模竞赛h奖英文

数学建模竞赛h奖英文

数学建模竞赛h奖英文Mathematical Modeling Competition H Award1. Mathematical:数学的2. Modeling:建模3. Competition:竞赛4. H: H奖5. Award:奖项1. The mathematical modeling competition requires participants to apply mathematical principles to solve real-world problems.数学建模竞赛要求参赛者将数学原理应用于解决现实世界的问题。

2. In order to excel in the competition, students must demonstrate strong analytical and problem-solving skills.为了在竞赛中取得优异的成绩,学生们必须展示出强大的分析和问题解决能力。

3. The H award is a prestigious recognition given to those who demonstrate exceptional mathematical modeling abilities.H奖是对那些展示出卓越数学建模能力的人的一个有声望的认可。

4. Winning the H award is a testament to the recipient's dedication to the field of mathematical modeling.赢得H奖是对获奖者在数学建模领域专注的证明。

5. Participants in the competition are evaluated based on the clarity of their mathematical models, the accuracy of their solutions, and the creativity in their approaches.竞赛的参赛者将根据数学模型的清晰度、解决方案的准确性和方法的创造力进行评估。

Introduction to Mathematical Modeling in Mathematica

Introduction to Mathematical Modeling in Mathematica
Introduction to Mathematical Modeling in Mathematica
Bruce E. Shapiro
Department of Biomathematics UCLA School of Medicine and Jet Propulsion Laboratory California Institute of Technology bshapiro@ 3 June 1998 Presented as part of Medical Informatics: A Course for Health Professionals Sponsored by the National Library of Medicine and Marine Biology Laboratory Water Street Woods Hole, MA 02543
Starting Mathematica............................12
An Example Population Biology...........23 Mathematica Reference Outline............24
Numerical Computation.........................24 Numerical Evaluation.........................24 Equation Solving..............................24 Sums and Products............................24 Integration.......................................25 Optimization....................................25 Data Manipulation.............................25 Curve Fitting...............................25 Fourier Transform.........................26 Selection of special elements...........26 Set Manipulation..........................26 Matrices and Vectors..........................27 Matrix Operations.........................27 Systems of Linear Equations...........27 Matrix Decompositions..................27 Complex Numbers............................28 Number Representation......................28 Heads..........................................28 Digits, Exponents, and Mantissas.....28 Change of Representation...............29 Infinity........................................29 Numerical Precision...........................29 Evaluation Accuracy and Precision....29 Intervals......................................29 Machine Accuracy and Precision.......30 Options...........................................30 Algebraic Computation..........................30 Basic Algebra...................................30 Formula Manipulation.......................31 Simplification..............................31 Expansion....................................31 Rearrangement..............................31 Parts of an Expression....................32 Numerators and Denominators.........32 Trigonometric Function Manipulation ..................................................32 Root and Radical Manipulation........32 Other Manipulations......................32 Bruce E. Shapiro Woods Hole, 1998

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题1. In the process of mathematical modeling, "parameter" means _____.A. a fixed valueB. a variable valueC. a constant valueD. a random value答案:A。

解析:“parameter”常见释义为“参数”,通常指固定的值,选项 A 符合;选项B“variable value”意为“变量值”;选项C“constant value”指“常数值”;选项D“random value”是“随机值”,在数学建模中“parameter”通常指固定的值。

2. When building a mathematical model, "function" is often used to describe _____.A. a relationship between inputs and outputsB. a set of random numbersC. a single valueD. a group of constants答案:A。

解析:“function”在数学建模中常被用来描述输入和输出之间的关系,选项 A 正确;选项B“a set of random numbers”表示“一组随机数”;选项C“a single value”是“单个值”;选项D“a group of constants”指“一组常数”。

3. In the context of mathematical modeling, "optimization" refers to _____.A. finding the best solutionB. creating a new modelC. changing the parameters randomlyD. ignoring the constraints答案:A。

07-Mathematical Modeling of Incompressible MHD Flows with Free Surface

07-Mathematical Modeling of Incompressible MHD Flows with Free Surface

ISIJ International, Vol. 47 (2007), No. 4, pp. 545–551545©2007ISIJFig.1.Solution procedure of mathematical model.©2007ISIJ546numerical diffusion caused by VOF convection step is ex-amined, and coalescence of two gas bubbles in the liquid and formation of the milk crown are examined in order to verify the validity of this mathematical model. As examples of MHD flow analysis, effect of the static magnetic field on pouring of the liquid metal to a square container and coa-lescence of two gas bubbles in the electroconductive liquid are investigated.3.1.Effect of Anti-diffusionComputational system shown in Fig. 2is analyzed in order to confirm the effect of the anti-diffusion putational domain is 2D, and divided into 100ϫ40grids. The time changes of calculation result are shown in Fig. 3. As it is clear from this figure, the gas–liquid inter-face is preserved sharply with anti-diffusion operation. On the other hand, the gas–liquid interface becomes blurred without anti-diffusion operation. It is recognized that the anti-diffusion operation is very effective in order to sup-press the numerical diffusion caused by convective term in fluid flows with complicated deformation of free surface. In general, the blurred gas–liquid interface can not be returned to the sharp interface, but it can be return to the sharp inter-face by the anti-diffusion step easily.3.2.Coalescence of Two Gas BubblesAnalysis of coalescence of two gas bubbles analyzed by the level set method 14)in order to evaluate the validity of the present mathematical model, is carried out, and compu-tational results are compared. Computational domain is 0.5ϫ1.0ϫ0.5m in size and is divided into 40ϫ80ϫ40grids. Small bubble which is located under the large bubble is incorporated in the large bubble as shown in Fig. putational result is shown in Fig. 5in comparison with the result by the level set method, and both results agree well. In this simulation, mass conservation error is less than 0.01%.k CrownIn the analysis of the free surface flow, milk crown prob-547©2007ISIJFig.2.Computational condition.Fig.3.Time changes of computational results with and withoutanti-diffusion putational condition for coalescence of two gas bub-bles.Fig.5.Numerical results compared with results of revel setmethod.lem is one of the most challenging theme, and this problem was analyzed by VOF, MARS, CIP and Level Set method, but it has not been perfectly solved. Here, this milk crown problem is solved as the verification of the present method. Computational condition is very simple as shown in Fig.6. Impact of a liquid droplet to the liquid thin film is ana-lyzed with changing the liquid film thickness. By assuming the symmetrical condition, 1/4 of the whole region that is 24ϫ24ϫ15mm in size, is divided into 80ϫ80ϫ80 grids. Courant number in time progress is made to be 0.1.The forming condition of the milk crown has been clari-fied experimentally,15)and computational condition was agree with that experimental condition. Splash is higher as the liquid film thickness is thinner. It is also known that the origin of the milk crown is immediately formed as soon as the droplet collides with the liquid film, and the photograph is shown in Fig. 7. The protrusion shown in this photographis an origin of the milk crown, and it is the most important point to reproduce the formation of the milk crown by the computational simulation. It seems that this origin can not have been reproduced in the conventional analyses. Computational results are shown in Fig. 8, and the origin of the milk crown and effect of liquid film thickness on the splash are reproduced well. In conventional analysis14) using level set method, the origin of the milk crown can not be represented in spite of with the higher resolution grids than this work. There seem to be some reasons. In case of the level set method, calculated interface seems to be too smoothed by the reinitialization step, and in CIP and MARS method, calculated interface is blurred by the nu-merical diffusion. In this mathematical model, milk crown formation can be represented by suppressing the numerical diffusion with anti-diffusion step.In the free surface analysis, it is very difficult that the in-stability of the actual phenomenon can be reproduced sta-bly by the computational analysis. The instability of the ac-tual phenomenon can not be reproduced by using the stable numerical method. Though the present model is not perfect, it seems to keep an appropriate balance of actual instability and computational stability.3.4.Pouring of Liquid MetalApplication example of MHD flows with free surface is shown here. DC magnetic field is applied to the electrocon-ductive liquid metal, and fluid flow control by electromag-netic brake (EMBr) is analyzed. Rectangle insulated con-tainer shown in Fig. 9is supplied with the liquid metal, and the effect of imposed direction of magnetic field on the ef-fect of the flow field is investigated. Computational domain is divided into 40ϫ40ϫ40 grids. The magnetic field of the uniform intensity of 5000 gauss is applied in the direction of x, y, and z individually. Computational results are shown in Fig. 10that shows the time changes of the flow field. In case of the x direction imposing of the magnetic field, it seems that the magnetic field does not contribute to the 548©2007ISIJFig.6.Computational condition for milk crown problem.Fig.7.Photograph of the origin of the milk crown.Fig.8.Computational results of the milk crown formation.putational condition.flow control. In case of the y direction imposing, the falling liquid metal flow is not much affected, but the wave motion of free surface is effectively suppressed. In case of the z di-rection imposing, the Lorentz force works in order to tighten the falling liquid metal flow, but the wave motion of free surface can not be suppressed like in case of the y di-rection imposing. Distributions of eddy current loop and Lorentz force are shown in Fig. 11, when the liquid metal flow reaches the bottom of the container. It is clear that in case of the z direction imposing, the eddy current is easy to form the loop and the Lorentz force effectively works in the falling liquid metal.It is well-known that the wave motion of free surface is suppressed effectively by imposing of the static magnetic field that is normal to the free surface in previous research for simple system, and the similar control effect is con-firmed in the complicated flow condition.3.5.Coale sce nce of Two Gas Bubble s in Ele ctrocon-ductive LiquidFinally, the effect of the static magnetic field on the coa-lescence of two gas bubbles in electroconductive liquid is analyzed as an interesting problem of MHD flows. Analysis condition is shown in Fig. 12and computational results are shown in Fig. 13. Computational domain is divided into 40ϫ40ϫ100 grids. In the case which magnetic field is per-pendicularly applied for the gravity direction, the shape of the bubble becomes to be flat, and rising velocity of the bubble is slower than without magnetic field. On the other hand, in the case which magnetic field is applied in the di-rection of gravity force, the bubble is transformed to the shape like the bullet in order to reduce the resistance from the liquid, and rising velocity is almost same compared with without magnetic field. Distribution of Lorentz force which works on bubbles and current density distribution around the bubbles are shown in Fig. 14. The above-men-tioned phenomena are very clear from this figure. In the 549©2007ISIJ putational results.case which magnetic field is perpendicularly applied for the gravity direction, eddy current is symmetrically distributed in the plane which is vertical to the direction of applied magnetic field, and Lorentz force works in order to sup-press the rising of bubbles. In the case which magnetic field is applied in the direction of gravity force, eddy current flows around the bubbles, and flow direction of eddy cur-rent is inverted in top and bottom of the bubble. Lorentz force works in order to tighten the bubble and not almost works in the gravity direction.4.ConclusionMathematical model for incompressible MHD flows with complex free surface has been developed. In this mathemat-ical model, SMAC method is used for solving the Navier–Stokes equations, the dynamic conditions at free surface are implemented through the CSF model with the VOF method, CICSAM scheme is used for VOF convection term, and anti-diffusion procedure is adopted in order to suppress the numerical diffusion caused by VOF convection step. Following results are obtained after some simulations.(1)It is found that the anti-diffusion procedure is very effective in order to suppress the numerical diffusion in the fluid flow problems with complex free surface.(2)It is possible that the coalescence of two gas bab-bles can be solved exactly same as level set method.(3)Milk crown formation that is challenging theme in free surface problems can be considerably solved well by the present mathematical model.(4)The wave motion of electoroconductive liquid sur-face is suppressed effectively by imposing of the static magnetic field that is normal to the free surface after pour-ing of liquid metal into the rectangle insulated container.The similar control effect has been confirmed in previous research for the simpler system.(5)Coalescence of two gas bubbles in electroconduc-tive liquid is investigated. In the case which magnetic field is perpendicularly applied for the gravity direction, the shape of the bubble becomes to be flat, and rising velocity550©2007ISIJFig.11.Distributions of eddy current and Lorenz force at free surface.of the bubble is slower than without magnetic field. On the other hand, in the case which magnetic field is applied in the direction of gravity force, the bubble is transformed to the shape like the bullet in order to reduce the resistancefrom the liquid, and rising velocity is almost same com-pared with without magnetic field.REFERENCES1)F . H. Harlow and E. Welch: Phys. Fluids , 8(1965), 2182.2) C. W . Hirt and B. D. Nichols: J. Comput. Phys., 39(1981), 201.3)M. Sussman, P . Smereka and S. Osher: J. Comput. Phys., 100(1994),146.4)T. Y abe and P . Y . Wang: J. Phys. Soc. Jpn., 60(1991), 2105.5)T. Kunugi: Proc. of ISAC’97 High Performance Computing on Mul-tiphase Flows, JSME, Tokyo, (1997), 25.6)W . F . Noh and P . Woodward: SLIC (Simple Line Interface Calcula-tion), Lecture Notes in Physics, ed. by A. I. van der V ooren and P . J.Zandbergen, Springer-Verlag, Berlin and New Y ork, (1976), 330.7) A. Sou: Proc. of ASME FEDSM’03 4th ASME_JSME Joint FluidsEngineering Conf., JSME, Tokyo, CD-ROM FEDSM2003-45164.8)J. U. Brackbill, D. B. Kothe and C. Zemach: J. Comput. Phys., 100(1992), 335.9)O. Ubbink and R. Issa: J. Comput. Phys., 100(1992), 25.10)T. Y abe, T. Utsumi and Y . Ogata: CIP Method, Morikita, Tokyo,(2003), 198.11) A. A. Amsden and F . H. Harlow: J. Comput. Phys., 6(1970), 322.12)T. Kobayashi ed.: Hand Book of Computational Fluid Dynamics,Maruzen, Tokyo, (2003), 78.13)K. Takatani: 173–174th Memorial Seminar, ISIJ, Tokyo, (2000), 6.14)R. Croce: Diplomarbeit, Rheinischen Friedrich-Wilhelms-UniversitätBonn, (2002).15)H. Gunji, H. Ishii, A. Saito and T. Sakai: Research on the MilkCrown, Nagare Multi Media 2003; URL: http://www.nagare.or.jp/mm/2003/index_ja.htm (accessed February 25, 2004)551©2007ISIJputational condition for coalescence of two gasbubbles in static magnetic field.putational results of coalescence of two gas bubblesin static magnetic field.Fig.14.Distributions of eddy current and Lorentz force aroundgas bubbles at free surface.。

Geometric Modeling

Geometric Modeling

Geometric ModelingGeometric modeling is a fundamental concept in the field of computer graphics and design. It involves the creation and manipulation of digital representations of objects and environments using geometric shapes and mathematical equations. This process is essential for various applications, including animation, virtual reality, architectural design, and manufacturing. Geometric modeling plays a crucial role in bringing creative ideas to life and enabling the visualization of complex concepts. In this article, we will explore the significance of geometric modeling from multiple perspectives, including its technical aspects, creative potential, and real-world applications. From a technical standpoint, geometric modeling relies on mathematical principles to define and represent shapes, surfaces, and volumes in a digital environment. This involves the use of algorithms to generate and manipulate geometric data, enabling the creation of intricate and realistic 3D models. The precision and accuracy of geometric modeling are essential for engineering, scientific simulations, and industrial design. Engineers and designers utilize geometric modeling software to develop prototypes, analyze structural integrity, and simulate real-world scenarios. The ability to accurately model physical objects and phenomena in a virtual space is invaluable for testing and refining concepts before they are realized in the physical world. Beyond its technical applications, geometric modeling also offers immense creative potential. Artists and animators use geometric modeling tools to sculpt, texture, and animate characters and environments for films, video games, and virtual experiences. The ability to manipulate geometric primitives and sculpt organic forms empowers creatives to bring their imaginations to life in stunning detail. Geometric modeling software provides a canvas for artistic expression, enabling artists to explore new dimensions of creativity and visual storytelling. Whether it's crafting fantastical creatures or architecting futuristic cityscapes, geometric modeling serves as a medium for boundless creativity and artistic innovation. In the realm of real-world applications, geometric modeling has a profound impact on various industries and disciplines. In architecture and urban planning, geometric modeling software is used to design and visualize buildings, landscapes, and urban developments. This enables architects and urban designers toconceptualize and communicate their ideas effectively, leading to the creation of functional and aesthetically pleasing spaces. Furthermore, geometric modelingplays a critical role in medical imaging and scientific visualization, allowing researchers and practitioners to study complex anatomical structures and visualize scientific data in meaningful ways. The ability to create accurate and detailed representations of biological and physical phenomena contributes to advancementsin healthcare, research, and education. Moreover, geometric modeling is integral to the manufacturing process, where it is used for product design, prototyping,and production. By creating digital models of components and assemblies, engineers can assess the functionality and manufacturability of their designs, leading tothe development of high-quality and efficient products. Geometric modeling also facilitates the implementation of additive manufacturing technologies, such as 3D printing, by providing the digital blueprints for creating physical objects layer by layer. This convergence of digital modeling and manufacturing technologies is revolutionizing the production landscape and enabling rapid innovation across various industries. In conclusion, geometric modeling is a multifaceteddiscipline that intersects technology, creativity, and practicality. Its technical foundations in mathematics and algorithms underpin its applications in engineering, design, and scientific research. Simultaneously, it serves as a creative platform for artists and animators to realize their visions in virtual spaces. Moreover,its real-world applications extend to diverse fields such as architecture, medicine, and manufacturing, where it contributes to innovation and progress. The significance of geometric modeling lies in its ability to bridge the digital and physical worlds, facilitating the exploration, creation, and realization of ideas and concepts. As technology continues to advance, geometric modeling will undoubtedly play an increasingly pivotal role in shaping the future of design, visualization, and manufacturing.。

基于Mathematica和Workingmodel的机构运动分析与设计

基于Mathematica和Workingmodel的机构运动分析与设计

基于Mathematica和Workingmodel的机构运动分析与设计金文涛,谢萌,吴开龙,杨岳(北京交通大学机械与电子控制工程学院,北京市海淀区100044)Mechanism motion analysis and design based on Mathematica and WorkingmodelJin Wentao,Xie Meng,,Wu Kailong,Yang Yue(School of Mechanical & Electronic Control Engineering, Beijing Jiaotong University,Beijing 100044, China)摘要:本文主要研究的是通过利用数学软件Mathematica编程对机构进行设计与建模,获得机构运动的三维图形,分析机构运动的特点,然后将设计的机构导入到运动仿真软件Workingmodel中进行仿真,通过比较在两个软件中机构运动的特点来获得预期的结论。

对于机构设计,可以先在Workingmodel中画出的机构运动2D图,进行仿真,然后将可行的方案在Mathematica中编程得到精确的三维机构运动图。

我们通过学习使用Mathematica和Workingmodel,一共设计并仿真了七个机构,有曲柄滑块与正弦机构的运动比较,有四杆机构的衍生机构,有行星轮系与差动轮系的设计,以及基于Workingmodel与excel的凸轮设计等,通过对设计这些机构并且对它们进行仿真,不仅学会使用两款实用的机构设计分析软件,而且学会一些机械设计的方法。

关键字:Mathematica,Workingmodel,仿真,机构运动ABSTRACT: This paper mainly studies are by using Mathematical software programming on the designing and modeling of the mechanism motion, obtain mechanism motion 3d graphics, analysis of mechanism motion characteristics, then will design institution into motion simulation software Workingmodel for simulation, by comparing the two software institutions movement characteristics of to achieve the expected results. For mechanism design, it can draw Workingmodel in first in the 2D figure, then take the feasible scheme into Mathematica in programming to get accurate 3d mechanism motion figure. By studied the Mathematica and Workingmodel ,we have designed 7 mechanisms, include the comparing of simple crank slider and scotch yoke mechanisms, four-bar derivatives of institutions, the design of planetary gear train and differential gear train, and the design of cam based on Workingmodel and excel ect. Through the design and simulated these mechanism,we not only learn to use two kinds of practical mechanism design and analysis software, and learn some mechanical design method. KEYS: Mathematica,Workingmodel,Simulation,Mechanism motion目录0 引言 (3)1 软件简介 (3)1.1Mathematica 简介 (3)1.2Workingmodel简介 (4)2机械结构分析 (4)2.1 曲柄滑块机构与正弦机构的运动比较及仿真 (4)2.2 油矿泵的结构分析及运动仿真 (5)2.3理想化凸轮跟随机构的设计与仿真 (6)2.4 画封闭曲线四杆机构的分析与仿真 (7)2.5 基于Workingmodel 2D的轮系分析 (8)2.6 基于Workingmodel 2D的齿轮-连杆组合机构仿真分析 (9)2.7 基于Workingmodel 2D和Excel的凸轮建模 (10)3 机构运动分析及程序设计 (11)3.1 曲柄滑块机构与正弦机构的运动分析及程序 (11)3.2 油矿泵的运动分析及程序 (14)3.3理想化凸轮跟随机构的运动分析及程序 (15)3.4画封闭曲线四杆机构的运动分析 (15)3.5基于Workingmodel 2D的轮系的运动分析 (17)3.6基于Workingmodel 2D的齿轮-连杆组合机构运动分析及程序 (19)3.7基于Workingmodel 2D和Excel的凸轮建模 (19)4 机构运动分析结果 (22)4.1 机构运动分析结果 (22)4.2 分析仿真结果比较 (23)5 结论 (24)6 参考文献 (24)7 研究感想 (24)0 引言数学软件在数学建模与机构分析中占有非常重要的地位,随着计算机技术不断发展,传统行业如机械设计已经不再单纯的是手工画图了,计算机技术已经深入到机械设计的每一个部分,并且大大的提高了机械设计的效率与质量,所以学会一些重要的机械设计与仿真软件对于从事机械行业的人来说非常重要。

大语言模型中的数学问题英语

大语言模型中的数学问题英语

大语言模型中的数学问题英语Mathematical Problems in Large Language ModelsIntroductionLarge language models are powerful artificial intelligence systems that have gained significant attention and application in various fields, such as natural language processing, machine translation, and text generation. These models, typically based on deep learning techniques, are trained on vast amounts of data to learn patterns and generate text that resembles human language. However, despite their remarkable capabilities, these models also face certain mathematical problems that need to be addressed. In this article, we will delve into some of these challenges and explore potential solutions.1. Numerical StabilityLarge language models often encounter numerical instability when dealing with extremely large or small values. This problem arises due to the limited precision of floating-point numbers used in computations. Situations where calculations involve extremely large or small values can lead to underflow or overflow, resulting in inaccurate or unreliable results. One approach to mitigate this issue is to normalize the inputs and outputs to a suitable range, ensuring that the values are within a manageable scale for the model to handle.2. Curse of DimensionalityThe curse of dimensionality refers to the exponential growth of computational resources required as the dimensionality of the problemincreases. Large language models often rely on high-dimensional representations of words or sentences, which can be computationally intensive. This challenge becomes more pronounced when dealing with large-scale datasets. To address this, dimensionality reduction techniques, such as principal component analysis or word embeddings, can be employed to reduce the computational complexity without sacrificing too much information.3. OptimizationTraining large language models involves optimizing an objective function through a process known as gradient descent. However, optimizing such models can be challenging due to the presence of numerous parameters and complex architectures. The optimization process may encounter issues such as vanishing or exploding gradients, where the gradients become too small or too large, impeding learning. Techniques like gradient clipping or adaptive learning rates can be utilized to alleviate these problems and ensure more stable and efficient optimization.4. InterpretabilityThe size and complexity of large language models can make it difficult to interpret their inner workings. Understanding how these models arrive at certain predictions or generate specific text can be crucial, particularly in applications where transparency and explainability are required. Various methods, such as attention mechanisms or visualization techniques, can be employed to gain insights into the model's decision-making process and enhance interpretability.5. Bias and FairnessLarge language models learn from vast amounts of data, making them prone to inheriting biases present in the training corpus. Biased language generation or biased predictions can have significant societal implications, perpetuating stereotypes or misinformation. Addressing bias and ensuring fairness in large language models require careful design choices, including robust data preprocessing, balanced training data, and post-training mitigation techniques such as bias debiasing or adversarial training.ConclusionLarge language models have revolutionized various domains by demonstrating remarkable language generation capabilities. However, these models also face mathematical challenges that need to be considered and resolved. Numerical stability, curse of dimensionality, optimization difficulties, interpretability, and bias/fairness issues are among the key problems encountered in large language models. By developing innovative solutions and incorporating mathematical techniques, researchers and practitioners can overcome these challenges and further enhance the performance and reliability of these models in real-world applications.。

高二英语数学建模方法完形填空题30题答案解析版

高二英语数学建模方法完形填空题30题答案解析版

高二英语数学建模方法完形填空题30题答案解析版1Mathematical modeling plays a crucial role in various fields. It helps us understand complex phenomena and make predictions. Through mathematical modeling, we can analyze real-world problems and find solutions. The process of mathematical modeling involves several steps. First, we need to identify the problem and define the variables. Then, we build a mathematical model using equations and formulas. After that, we solve the model and analyze the results. Finally, we validate the model and use it to make decisions.1. Mathematical modeling is a powerful ___ for solving real-world problems.A. toolB. methodC. wayD. means答案:A。

“tool”表示工具,在这里最符合语境,数学建模是解决现实世界问题的有力工具。

“method”和“way”更多强调方法、途径;“means”也有方法、手段的意思,但不如“tool”贴切。

2. The process of mathematical modeling includes identifying the problem, defining the ___, building a model, solving it and analyzing theresults.A. constantsB. variablesC. numbersD. equations答案:B。

高二英语数学建模方法单选题20题(含答案)

高二英语数学建模方法单选题20题(含答案)

高二英语数学建模方法单选题20题(含答案)1. In a math modeling project, we need to “analyze data”. Which of the following phrases has a similar meaning?A. look up dataB. sort out dataC. examine dataD. collect data答案:C。

“analyze data”是分析数据的意思。

A 选项“look up data”是查找数据;B 选项“sort out data”是整理数据;C 选项“examine data”是检查、分析数据;D 选项“collect data”是收集数据。

所以正确答案是C。

2. When we build a math model, we often use “assumptions”. What does “assumptions” mean in this context?A. factsB. guessesC. resultsD. methods答案:B。

在建立数学模型时,“assumptions”是假设的意思。

A 选项“facts”是事实;B 选项“guesses”是猜测,与假设意思相近;C 选项“results”是结果;D 选项“methods”是方法。

所以正确答案是B。

3. In math modeling, “validate the model” means to _____.A. make the modelB. test the modelC. change the modelD. explain the model答案:B。

“validate the model”在数学建模中是验证模型的意思。

A 选项“make the model”是制作模型;B 选项“test the model”是测试模型,与验证模型意思相近;C 选项“change the model”是改变模型;D 选项“explain the model”是解释模型。

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题1.In the process of mathematical modeling, the factor that determines the outcome is called_____.A.independent variableB.dependent variableC.control variableD.extraneous variable答案:B。

本题考查数学建模中的基本术语。

独立变量(independent variable)是指在实验或研究中被研究者主动操纵的变量;因变量dependent variable)是指随着独立变量的变化而变化的变量,在数学建模中决定结果的因素通常是因变量;控制变量(control variable)是指在实验中保持不变的变量;无关变量(extraneous variable)是指与研究目的无关,但可能会影响研究结果的变量。

2.The statement “The value of y depends on the value of x” can be represented by a mathematical model where y is the_____.A.independent variableB.dependent variableC.control variableD.extraneous variable答案:B。

在“y 的值取决于x 的值”这句话中,y 是随着x 的变化而变化的变量,所以y 是因变量。

3.In a mathematical model, the variable that is held constant toobserve the effect on other variables is_____.A.independent variableB.dependent variableC.control variableD.extraneous variable答案:C。

高三英语读后数学家应用作文

高三英语读后数学家应用作文

In the vast expanse of human knowledge and innovation, mathematics stands as an unyielding pillar. Its influence is pervasive, transcending traditional boundaries to shape our everyday lives and future advancements. This essay aims to delve into the multifaceted applications of mathematicians' contributions across various fields, demonstrating their paramount importance in today's high-tech, data-driven world.Mathematics, often referred to as the 'language of science', is not merely a tool for solving numerical problems but serves as the foundation for numerous scientific breakthroughs. For instance, the work of renowned mathematician Alan Turing played a critical role during World War II through his decryption algorithms that helped crack the German Enigma code, thereby changing the course of history. This exemplifies how mathematicians contribute significantly to national security and strategic planning.In the realm of technology, mathematicians are the architects of the digital revolution. Their theoretical constructs underpin every facet of computing, from programming languages to encryption methods. Cryptography, largely based on complex mathematical theories like number theory, ensures secure transactions online and protects personal data, a fundamental aspect of the modern digital economy. Moreover, mathematicians have been instrumental in the development of algorithms that power artificial intelligence (AI) and machine learning (ML), enabling predictive analytics, autonomous vehicles, and sophisticated decision-making systems.The field of physics also heavily relies on mathematical prowess. From Isaac Newton’s laws of motion to Albert Einstein’s theory of relativity, mathematical models have provided the framework to understand and predict natural phenomena with unprecedented accuracy. Contemporary physicists still grapple with complex equations to unlock the mysteries of quantum mechanics, black holes, and the very fabric of space-time.In finance, quantitative analysts or 'quants' apply mathematical principles to develop financial models, assess risk, and optimize investment strategies.They devise algorithms that can predict market trends and manage portfolios, playing a pivotal role in stabilizing and advancing global economic systems. Further, actuaries utilize statistical and probabilistic methods to calculate insurance premiums and estimate potential risks, thereby safeguarding individuals and businesses alike.The life sciences too, have seen a surge in mathematical applications. Biostatisticians interpret complex biological data, helping researchers understand disease patterns, design clinical trials, and analyze genetic sequences. Mathematical modeling in epidemiology, such as predicting the spread of pandemics, has proven indispensable in public health management.Moreover, mathematicians play a vital role in environmental studies. Their models help scientists simulate climate change scenarios, predict weather patterns, and understand ecological dynamics. These predictions aid policymakers in making informed decisions about conservation and sustainability.Lastly, mathematicians contribute to the advancement of education itself. By refining teaching methodologies, they enhance students’ logical reasoning, problem-solving skills, and critical thinking – attributes essential in the 21st-century workforce.In conclusion, the role of mathematicians in contemporary society extends far beyond classrooms and textbooks. Their intellectual explorations and practical applications permeate virtually every sector, driving progress, innovation, and enhancing quality of life. Their ability to decipher patterns, quantify uncertainties, and model complexities is a testament to the profound impact of mathematics on our modern world. In a time where data reigns supreme, the demand for mathematicians who can translate numbers into actionable insights will only continue to grow. Thus, it is imperative that we foster a strong culture of mathematical literacy and appreciation to meet these burgeoning demands and propel humanity forward.Word Count: 746 wordsThis response, while extensive, is a condensed version of what could be a much longer, more detailed exploration of the topic. However, it underscores the multi-dimensional applications of mathematics and mathematicians in our society. To reach the desired length of over 1280 words, each section could be expanded further with examples, historical anecdotes, and detailed explanations of specific mathematical concepts and their real-world implications.。

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题1. In math modeling, we often use “variable” which means _____.A.constantB.changeable thingC.fixed numberD.unchangeable element答案:B。

“variable”意为“变量”,也就是可以变化的事物。

选项A“constant”是“常量”;选项C“fixed number”是“固定的数字”;选项D“unchangeable element”是“不可变的元素”。

2. The term “function” in math modeling is related to _____.A.operationB.relationC.actionD.process答案:B。

“function”在数学建模中是“函数”的意思,函数表示一种关系。

选项A“operation”是“操作”;选项C“action”是“行动”;选项D“process”是“过程”。

3. In math modeling, “parameter” is usually a _____.A.random valueB.fixed valueC.variable valueD.changing value答案:B。

“parameter”是“参数”,通常是一个固定的值。

选项A“random value”是“随机值”;选项C“variable value”是“变量值”;选项D“changing value”是“变化的值”。

4. The word “model” in math modeling can be described as _____.A.representationB.exampleC.caseD.situation答案:A。

“model”在数学建模中是“模型”的意思,可以被描述为一种表示、表征。

基于建模思想的初中数学实践与反思

基于建模思想的初中数学实践与反思

DANGDAIJIAOYANLUNCONG2019年09月初中时期是学生学习思维以及能力养成的关键时期,在本时期实施正确的教学引导是学生当下以及未来数学学习质量的保证。

在初中数学中,数学建模思想是学生必备的一种数学思想,数学建模思想的引入使初中生能够将抽象的数学问题描述转化为生动具体的数学模型,使数学问题变得更加简单明了、易于理解。

除此之外,数学建模思想在初中数学学习中的应用能够为学生逻辑思维锻炼提供一种思想平台,促进学生抽象思维以及分析逻辑思维水平的提高。

由此可见,培养学生数学建模思想是初中数学教学活动中必需的教学目标。

接下来,介绍几种数学建模思想在初中数学中的实践策略。

一、数学建模思想的意义与流程数学是一门与生活实际息息相关的学科,生活中许多实际问题的解决都依赖于数学方法与思想。

因此数学思想的培养不仅仅对学生数学学习有利,也对学生未来生活有益。

从数学建模思想在数学中的应用来看,数学模型的建立主要分为以下几个步骤:1.模型的准备。

模型的准备,指的是以教学内容为出发点,确定模型的种类、方法等等。

2.模型的假设。

模型的假设是指对在模型准备过程中所得到的信息进行分析处理,将问题的本质凸显出来,并利用已经掌握的信息以及资源进行合理性的语言假设。

3.模型构建。

这个步骤的完成需要建模者根据建模信息选择恰当的建模方法、建模工具等,同时建模者需要对信息进行处理,找出数学信息之间的数学关系,并将变量之间的关系与所选择的方法、工具结合起来,完成最终的模型构建。

4.求解模型。

在解答模型时,建模者需要选择正确的求解模型的方法,例如使用方程等方式。

随着时代的进步,计算机求解模型已经成为一种主要的方式。

5.分析模型。

在解答模型的基础上进行数据分析,主要包括误差分析、模型对数据的稳定性分析以及灵敏性分析等等。

6.模型检验。

在对模型进行分析并确定模型的可用后,建模者还需对模型进行检验,检验主要包括模型与实际生活问题之间的符合以及适用性。

数学建模论文英文

数学建模论文英文

数学建模论文英文Abstract:Mathematical modeling is an essential tool in various scientific and engineering disciplines, facilitating the understanding and prediction of complex systems. This paper explores the fundamental principles of mathematical modeling, its applications, and the methodologies employed in constructing and analyzing models. Through case studies, we demonstrate the power of mathematical models in solving real-world problems.Introduction:The introduction of mathematical modeling serves as a foundation for the entire paper. It provides an overview of the significance of mathematical modeling in modern problem-solving and sets the stage for the subsequent sections. It also outlines the objectives and scope of the paper.Literature Review:This section reviews existing literature on mathematical modeling, highlighting the evolution of the field, key concepts, and the diverse range of applications. It also identifies gaps in current knowledge that the present study aims to address.Methodology:The methodology section describes the approach taken to construct and analyze mathematical models. It includes theselection of appropriate mathematical tools, the formulation of the model, and the validation process. This section is crucial for ensuring the scientific rigor of the study.Model Development:In this section, we delve into the process of model development, including the identification of variables, the establishment of relationships, and the formulation of equations. The development of the model is presented in a step-by-step manner to ensure clarity and reproducibility.Case Studies:Case studies are presented to demonstrate the practical application of mathematical models. Each case study is carefully selected to illustrate the versatility and effectiveness of mathematical modeling in addressing specific problems.Results and Discussion:This section presents the results obtained from the application of the mathematical models to the case studies. The results are analyzed to draw insights and conclusions about the effectiveness of the models. The discussion also includes an evaluation of the model's limitations and potential areas for improvement.Conclusion:The conclusion summarizes the key findings of the paper and reflects on the implications of the study. It also suggests directions for future research in the field of mathematical modeling.References:A comprehensive list of references is provided to acknowledge the sources of information and ideas presented in the paper. The references are formatted according to a recognizedcitation style.Appendices:The appendices contain any additional information that supports the paper, such as detailed mathematical derivations, supplementary data, or extended tables and figures.Acknowledgments:The acknowledgments section, if present, expresses gratitudeto individuals or organizations that contributed to the research but are not authors of the paper.This structure ensures that the mathematical modeling paperis comprehensive, logically organized, and adheres to academic standards.。

Mathematical Modeling in Physics and Engineering

Mathematical Modeling in Physics and Engineering

Mathematical Modeling in Physics andEngineeringIntroductionMathematical modeling is a powerful tool that plays a crucial role in understanding and solving complex problems in various fields, including physics and engineering. It involves the use of mathematical equations and algorithms to describe and predict the behavior of physical systems. In this lesson, we will explore the importance of mathematical modeling in physics and engineering, and discuss its applications in different areas.I. The Role of Mathematical Modeling in PhysicsMathematical modeling is an essential component of physics research and experimentation. It allows physicists to formulate equations that describe the behavior of physical phenomena and predict their outcomes. By using mathematical models, physicists can simulate and analyze complex systems that are difficult or impossible to observe directly. For example, in quantum mechanics, mathematical models are used to describe the behavior of subatomic particles and predict their interactions.A. Classical MechanicsIn classical mechanics, mathematical modeling is used to describe the motion of objects under the influence of forces. The famous equations of motion, such as Newton's second law and the equations of projectile motion, are mathematical models that allow us to predict the behavior of objects in motion. These models are based on fundamental principles, such as conservation of energy and momentum, and have been extensively tested and validated through experiments.B. ElectromagnetismIn electromagnetism, mathematical modeling is used to describe the behavior of electric and magnetic fields, as well as the interactions between them. Maxwell's equations, a set of partial differential equations, form the foundation of mathematical modeling in electromagnetism. These equations describe how electric and magnetic fields are generated by charges and currents, and how they propagate through space. Mathematical models based on Maxwell's equations have been instrumental in the development of technologies such as radio waves, electric motors, and telecommunications.II. Mathematical Modeling in EngineeringMathematical modeling is also widely used in engineering to design and optimize systems, solve engineering problems, and predict the behavior of complex structures and processes. Engineers use mathematical models to simulate and analyze the performance of various systems, ranging from bridges and buildings to aircraft and spacecraft.A. Structural EngineeringIn structural engineering, mathematical modeling is used to analyze the behavior of buildings, bridges, and other structures under different loads and conditions. Finite element analysis (FEA), a mathematical modeling technique, is commonly used to simulate the behavior of structures and predict their response to external forces. By using mathematical models, engineers can optimize the design of structures, ensure their safety, and minimize costs.B. Fluid MechanicsIn fluid mechanics, mathematical modeling is used to describe the behavior of fluids, such as liquids and gases, and predict their flow patterns and properties. Mathematical models based on the Navier-Stokes equations are used to analyze fluid flow in pipes, channels, and other systems. This allows engineers to design efficient and safe transportation systems, such as pipelines and water supply networks, and optimize the performance of devices like pumps and turbines.III. Challenges and Limitations of Mathematical ModelingWhile mathematical modeling is a powerful tool, it also has its challenges and limitations. One of the main challenges is the complexity of real-world systems, which often involve multiple variables, nonlinearities, and uncertainties. Developing accurate mathematical models that capture the behavior of these systems can be a difficult task. Additionally, the accuracy and reliability of mathematical models depend on the quality of the data and assumptions used in their development.ConclusionMathematical modeling is a fundamental tool in physics and engineering that enables scientists and engineers to understand, predict, and solve complex problems. By formulating mathematical equations that describe the behavior of physical systems, researchers can simulate and analyze complex phenomena, design optimal solutions, and make informed decisions. However, mathematical modeling also has its challenges and limitations, and it is important to continuously refine and validate models to ensure their accuracy and reliability.。

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On the Mathematical Modeling of MemristorsA.G.Radwan1,2,M.Affan Zidan1and K.N.Salama11Electrical Engineering ProgramKing Abdullah University of Science and Technology(KAUST)Thuwal,Kingdom of Saudi Arabia23955-69002Department of Engineering MathematicsFaculty of Engineering,Cairo University,EgyptEmail:{ahmed.radwan,mohammed.zidan,khaled.salama}@.saAbstract—Since the fourth fundamental element(Memristor)became a reality by HP labs,and due to its huge potential,itsmathematical models became a necessity.In this paper,we pro-vide a simple mathematical model of Memristors characterizedby linear dopant drift for sinusoidal input voltage,showing a highmatching with the nonlinear SPICE simulations.The frequencyresponse of the Memristor’s resistance and its bounding condi-tions are derived.The fundamentals of the pinched i-v hysteresis,such as the critical resistances,the hysteresis power and themaximum operating current,are derived for thefirst time.I.I NTRODUCTIONThe Memristor(M)is believed to be the fourth fundamentaltwo terminals passive element,beside the Resistor(R),theCapacitor(C)and the Inductor(L).The existence of suchelement was postulated by Leon Chua in the seventies[1].Recently a practical implementation of the Memristor usingpartially doped T i O2was presented by Hewlett-Packard[2]asshown in Fig.1,where w∈(0,D).When the applied potentialis removed the dopant boundary will remain at its locationwhich will be the initial value for any later movements.According to[2]the memristive property naturally appearsin nanoscale devices,so the understanding of Memristance willimprove the studying and modeling of nanodevices character-istic,which includes the current-voltage hysteresis behaviorobserved in many nanodevices.The invention of the Memristorwas the key for postulating new elements by Chua such as theMemcapacitors and the Meminductors[3].The Memristor found application in memory[4],biol-ogy[5],and spintronic[6].Recently the Memristor wasmodeled using a linearized model of the pinched i-v hysteresisas in[7]or qualitatively as in[8],[9].However these effortsfailed to capture many of its characteristics.A Memristorsmodel for DC,square and triangular signals is presentedin[10].This model is used in the analysis of Memristor-basedWien-family oscillators[11].The instantaneous resistance R of the Memristor is givenby[2],R=x·R on+(1−x)R off(1)where x=w/D,R on is the resistance of the completelydoped Memristor,and R off is for the undoped Memristor.The speed dopant movement is defined as,dx dt =k·i(t)·f(x)(2)4VoltageResistanceíVoltageCurrentTime'RSHG 5HJLRQ8QRSHG 5HJLRQ0HWDO &RQWDFWFig.1.The abstract structure of the HP’s Memristor[2],current versustime,and current and resistance versus input voltage are plotted for sinusoidalwave input voltage of1Hz frequency and−1.5V peak voltage.Memristor’sParameters:R off=38kΩ,Ron=100Ω,Ri=4kΩand p=10.where f(x)=1−(2x−1)2p is the window function fornonlinear dopant drift given in[12],and k=µv·R on/D2.Fig.1shows some basic relationships between the current,voltage,and resistance for the HP’s Memristor.In this paper the frequency response of the Memristor’sresistance is studied in the case of linear dopant drift forsinusoidal waveform for thefirst time.A condition on the ratioof(v o/f)to maintain unsaturated resistance is introduced.A simple implicit equation for the pinched i-v hysteresis isderived,showing its general fundamentals such as criticalresistances,maximum current magnitude and location,andthe power under and inside this hysteresis.All mathematicalequations are compared with the nonlinear Memristor’s SPICEmodel given in[12],showing perfect matching.II.T HE F REQUENCY R ESPONSE OF THE M EMRISTOR’SR ESISTANCEThe instantaneous resistance of the Memristor subjected toa sinusoidal input can be derived from equations(3)and(2),for linear dopant f(x),and can be simplified as,R2=R2i−2V o kR dπfsin2(πft),R∈(R on,R off)(3)where k=µv·R on/D2,µv is dopant drift mobility,f is thefrequency of the input sinusoidal waveform,R i is the initialMemristor resistance at t=0,and R d=R off−R on is 22nd International Conference on Microelectronics (ICM 2010)978-1-4244-5816-5/09/$26.00 ©2009 IEEEFig.2.Memristor’s resistance versus time−V o plane for1Hz sinusoidal waveform with f=1Hz,R off=16kΩ,R o n=100Ω,and Ri=6kΩ. the difference between the boundary resistances.However,the instantaneous resistance must be bounded by R o n and R o ff. The range of R depends on the sign of V o.Hence R∈[R i,R off)in case of V o<0or(R on,R i]for V o>0.For any sinusoidal signal starting with a zero voltage,the Memristor’s resistance reaches its maximum or minimum value at t= (2n+1)T/2;n=0,1,2,···,such that,R2M=R2i−2V o kR dπf,R M∈(R on,R off)(4)The magnitude of R M,whether maximum or minimum,de-pends on the sign of V o.However,at t=nT,n=0,1,2,···, the resistance returns to its initial value,R=R i.Fig.2 shows the3D surfaces of the Memristor’s resistance versus t-V o plane.The resistance has the shape of sin2(·).However for high amplitude voltage there is clipping effect due to the bounding R off and R on.As|V o|increases the effect of the boundary resistances R off,R on appears as clipping effect.Fig.3shows the Memristor’s resistance versus v(t)-f plane. Generally,the resistance clipping to R off exists at lower frequency(from the upper saturation plane).However,as frequency increases,the clipping interval decreases in time until it vanishes at a certain frequency.Any cross-section at fixed high frequency will showan ellipse shape,and the small Fig.3.Memristor’s resistance versus a sinusoidal input and frequency for a peak input voltage of3V,R off=38kΩ,R o n=100Ω,and Ri=11kΩ.FequancyResistanceRangeFequancyResistanceRangeV=1.5 Volt(a)(b)Fig.4.Range of Memristor’s resistance versus frequency,for sinusoidal wave input voltage for different(a)R off,and(b)peak voltages V o.Memristor’s Parameters:R on=100Ω,R i=4kΩ.FrequencyRMaxTimeResistance(b)(a)Fig. 5.Numerical and SPICE simulations in(a)time domain,with Memristor’s Parameters:R on=100Ω,and R i=4kΩ(b)frequency domain, with Memristor’s Parameters:R on=100Ω,R i=11kΩand p=10.axis of the ellipse area decreases and rotates as frequency increase.Fig.4shows the range of Memristor’s resistance for both ±V o for two different cases.Fig.4(a)studies the effect of R off on the operating range of resistance.As R off decreases the Memristor’s resistance saturates at R off for wider range of frequencies but it saturates at R on for less range of frequencies.However,forfixed R o ff and two different values of V o as shown in Fig.4(b),as V o increases both ranges of frequencies increase.Fig.5(a)shows the the resistance given by equation(3)compared to the nonlinear Memristor’s SPICE model given in[12].Fig.5(b)shows the maximum resistance of the transient SPICE simulation results for different frequencies chosen as 6points/decade,while the solid lines are the calculated maxi-mum frequencies according to equation(4).These simulations are repeated for two different cases showing identical matching with the SPICE model.Both the frequency and the voltage amplitude affect the resistance range and the rotation of the pinched i-v hysteresis.The boundaries for non-saturating resistance are given by,(V o/f)off=π2kR d R2i−R2off <0(5a)(V o/f)on=π2kR d R2i−R2on >0(5b) The difference between these boundaries,which is indepen-dent on R i,is given by,|V o/f|sat=πD22µv(1+R off/R on)(6)Fig.6.Intervals of the Memristor’s resistance versus (V o /f ).which is independent of R i .This value is considered as the minimax ratio |V o /f |required for the Memristor’s resistance to reach saturation,either R off or R on ,when R i equals to R on or R off respectively as shown in Fig.6.According to equation (4),the range of the Memristor’s resistance depends on the value of R M which is inversely proportional to the frequency (f ).Thus the resistance range decreases with the increase of the frequency.Consequently the Memristor’s current range decreases leading to a shrinkage of the pinched hysteresis.This will be further elaborated in the next section.III.Q UALITATIVE S TUDY OF THE i -v H YSTERESIS The 3D pinched i -v hysteresis as a function of frequency is shown in Fig.7.As the frequency increases,with same amplitude voltage,the pinched hysteresis shrinks and rotates.This figure describes the rotation only in very narrow band of frequencies from 0.5Hz up to 2Hz.The dotted points in Fig.8(a)shows the SPICE model pinched i -v hysteresis for V o =−1.5and f =1Hz.However the solid line represents the numerical simulation using the mathematical model at the same input data,showing the exact matching with the nonlinear SPICE model.In this section some of the main characteristics of the pinched i -v hysteresis such as maximum current,angle of rotation with frequency,and power inside the hysteresis are derived.A.Implicit Resistance Equation:The implicit equations which relate R M as function of v is,R ±= R 2i−V o kRd πf1±1−(v/V o )2 (7)Fig.7.Memristor’s 3D i -v hysteresis for a sinusoidal input voltage.−4VoltageC u r r e n t(a)(b)Fig.8.(a)i -v hysteresis at frequency of 1Hz and peak voltage of −1.5V olt using R off =16k Ω,R on =100Ω,R i =4k Ωand p =10.(b)General schematic of the Memristor’s pinched i -v hysteresis.where the positive sign is applied when the voltage increases and the negative when decreases.Thus the rate of change of v must be known (ie memorize the previous value).Conse-quently,the implicit equations for the i -v hysteresis is given by i 1,2=v/R ±,which matches the SPICE simulations as shown in Fig.8(a).B.Location of the peak current:The peak current,occurring at the point b on the pinched i -v hysteresis,is given by i ∗=v/R ∗,where v ∗andR ∗are the voltage and resistance at point b as shown in Fig.8(b).Let,y =1−(v/V o )2,α1=R 2in−V o kR d πf ,α1=V o kR d πf(8)The critical point at which the maximum current occurs canbe obtained by evaluating,∂i/∂v =(∂i/∂y )(∂y/∂v )=0,which can be simplified into the following quadratic equation,y 2±2α1α2y +1=0(9a)y =1−πfR i n (R i −R M )V o kR d =πf (R i −R M )22V o kR d (9b)v ∗=V o 1−y 2,R ∗=R in R M(9c)Fig.9shows the peak current values i ∗and its location v ∗versus the frequency for unsaturated conditions.The minimum value of the frequency is calculated by the maximum absolute value of the equation (5a)and (5b).In this case to avoid Mem-ristor’s resistance saturation the frequency must be greater than 33.7784Hz,when V o =3V ,R in =3K Ω,R on =100Ω,and R off =16K Ω.C.Angle of the i -v hysteresis at peak voltageAs shown in Fig.8(b),the angle αm indicates the rotation of the hysteresis curve and is given by,αm =cot −1(R a )=cot −1R in R M (10)where R a is the resistance at the peak voltage v =V o as,R a =(R 2i +R 2M)/2(11)Fig.10plots the Memristor’s resistance versus frequency.This angle depends on the initial resistance R i and the ratioFig.9.Calculated peak current and its location v ∗and its exact value.V o /f as discussed before.The maximum range of this angle is given by,cot −1(R off )<αm <cot −1(R on )(12)D.Area enclosed by the i -v hysteresis:The power under the curves C 1and C 2,and the enclosed area inside the pinched hysteresis is a key characteristic of the Memristive device.The area inside one loop of the i -v characteristic of the HP’s Memristor is,A Hysteresis =− C 1i 1d −C 2i 2d v (13)where the negative signs are due to the counterclockwisedirection of the closed loop.The area under any curves between v =v 1and v 2can be calculated as,A 1=−2π2f 2k 2R 2d r 2=R ar 1=R iR 2i −V o kR d πf−r 2 dr =2π2f 23k 2R 2d(R M −R a ) R a R M −R 2i (14)Fig.10.The relationship between the initial resistance,resistance at peak voltage,resistance at peak current,and the minimum resistance versus frequency when V o =3V ,R i =3k Ω,R on =100Ω,and R off =16k Ω.In addition,A 2can be given by,A 2=2π2f 23k 2R 2d(R a −R i ) R 2M −R i R a (15)So,the hysteresis power (enclosed by the two curves)is,A Hysteresis =A 2−A 1=π2f 23k 2R 2d|R initial −R M |3(16)As the frequency increases by small amount,the difference between the R i and R M decreases,which compensates the i -v hysteresis area.This decreasing will be dominant,and this area at high frequency can be approximated as,A Hysteresis ≈V 3o kR d 3πfR 3initial 1+V o kR d2πfR 2initial(17)These expressions are valid if and only if R ∈(R on ,R off ).IV.C ONCLUSIONIn this paper,we present a mathematical model for Memris-tors characterized by linear dopant drift under sinusoidal input voltage stimulation.The model describes the Memristor’s resistance using its voltage and current without the need for the flux (φ)or charge equations (Q ).The frequency response of the Memristor’s resistance and its bounding conditions are derived for the first time.A simple implicit equation for the pinched i -v hysteresis is derived,describing its general characteristics such as critical resistances,maximum current magnitude and location,and the power inside this hysteresis.All the derived formulas are compared with the nonlinear Memristor’s SPICE model showing perfect matching.R EFERENCES[1]L.Chua,“Memristor-the missing circuit element,”IEEE Transactionson Circuit Theory ,vol.18,no.5,pp.507–519,1971.[2] D.B.Strukov,G.S.Snider,and D.R.Stewart,“The missing memristorfound,”Nature ,vol.435,pp.80–83,2008.[3]M.Di Ventra,Y .Pershin,and L.Chua,“Circuit elements with memory:Memristors,memcapacitors,and meminductors,”Proceedings of the IEEE ,vol.97,no.10,pp.1717–1724,2009.[4]P.O.V ontobel,W.Robinett,P.J.Kuekes,D.R.Stewart,J.Straznicky,and R.S.Williams,“Writing to and reading from a nano-scale crossbar memory based on memristors,”Nanotechnology ,vol.20,no.42,p.425204,2009.[5]Y .V .Pershin, Fontaine,and M.Di Ventra,“Memristive model ofamoeba learning,”Phys.Rev.E ,vol.80,no.2,p.021926,2009.[6]Y .V .Pershin and M.Di Ventra,“Spin memristive systems:Spin memoryeffects in semiconductor spintronics,”Phys.Rev.B ,vol.78,no.11,p.113309,2008.[7] D.Wang,Z.Hu,X.Yu,and J.Yu,“A PWL model of memristor andits application example,”International Conference on Communications,Circuits and Systems,2009.ICCCAS 2009,pp.932–934,July 2009.[8] F.Y .Wang,“Memristor for intrductory physics,”Physics.class-ph ,pp.1–4,2008.[9]Y .N.Joglekar and S.J.Wolf,“The elusive memristor:properties of basicelectrical circuits,”European Journal of Physics ,vol.30,pp.661–675,2009.[10] A.G.Radwan,M.A.Zidan,and K.N.Salama,“HP Memristor math-ematical model for periodic signals and DC,”53rd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS),pp.861–864,Aug 2010.[11] A.Talukdar,A.G.Radwan,and K.N.Salama,“Time domain oscillatingpoles:Stability redefined in memristor based wien-oscillators,”IEEE International Conference on Microelectronics (ICM),Dec 2010.[12]Z.Biolek,D.Biolek,and V .Biolkova,“Spice model of memristor withnonlinear dopant drift,”Radioengineering ,vol.18,no.2,pp.210–214,2009.。

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