Unitary IIB matrix model and the dynamical generation of the space time, Nucl

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TBM减速器两级行星齿轮传动系统动力学特性研究

TBM减速器两级行星齿轮传动系统动力学特性研究

2024年第48卷第3期Journal of Mechanical TransmissionTBM减速器两级行星齿轮传动系统动力学特性研究徐尤南1李明钦1刘汕娟1刘志强1,2(1 华东交通大学机电与车辆工程学院,江西南昌330013)(2 江西水利职业学院机电工程系,江西南昌330013)摘要隧道掘进机(Tunnel Boring Machine,TBM)减速器为TBM刀盘驱动系统重要部件。

为了揭示TBM减速器两级行星齿轮传动系统的动力学特性,考虑TBM减速器两级行星齿轮传动系统的齿面摩擦、时变啮合刚度、齿侧间隙、传递误差等影响因素,运用集中参数法建立了TBM减速器两级行星齿轮传动系统的扭转动力学模型,求解分析了动力学特性。

固有特性分析结果表明了行星轮系的3种振动模态形式:刚体模态、扭转振动模态、行星轮振动模态。

动态响应分析得到各齿轮的振动位移及齿轮副间的动态啮合力。

分析结果为该行星齿轮传动系统动态优化设计奠定了基础。

关键词TBM 两级行星轮系动力学动态响应固有特性Research on Dynamic Characteristics of the Two-stage Planetary GearTransmission System of TBM ReducersXu Younan1Li Mingqin1Liu Shanjuan1Liu Zhiqiang1,2(1 School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China)(2 Department of Mechanical and Electrical Engineering, Jiangxi Water Resources Institute, Nanchang 330013, China)Abstract Tunnel boring machine (TBM) reducers are important components of TBM's cutter-head drive system. In order to reveal the dynamic characteristics of the two-stage planetary gear transmission system of the TBM reducer, the influence factors such as tooth surface friction, the time-varying meshing stiffness, the back‑lash and meshing error of the two-stage planetary gear transmission system of the TBM reducer are considered. The torsional dynamics model of the two-stage planetary gear transmission system of the TBM reducer is estab‑lished by the lumped parameter method, and its dynamic characteristics are solved and analyzed. The inherent characteristic analysis results show three vibration modes of the planetary gear train: rigid body vibration mode, torsional vibration mode and the planetary gear vibration mode; the vibration displacement of each gear and the dynamic meshing force between the gear pairs are obtained from the dynamic response analysis. The result lays the foundation for the dynamic optimization design of planetary gear trains.Key words TBM Two-stage planetary gear train Dynamics Dynamic response Inherent character‑istic0 引言全断面硬岩隧道掘进机(Tunnel Boring Machine,TBM)[1]是集机、电、液及传感与信息技术于一体的国家重点工程建设、军事与国防工程所急需的重大技术装备。

非理想爆轰波阵面传播的LevelSet方法在爆轰驱动计算中的应用研究

非理想爆轰波阵面传播的LevelSet方法在爆轰驱动计算中的应用研究

非理想爆轰波阵面传播的Level Set方法在爆轰驱动计算中的应用研究姜洋*,钟敏,孙承纬,李平,柏劲松(中国工程物理研究院流体物理研究所,四川绵阳 621900)摘要:基于爆轰冲击波动力学(DSD)理论,研究了计算二维贴体坐标系中非理想爆轰波阵面传播问题的Level Set方法。

根据Hamilton-Jacobi方程的Godunov差分格式,提出了非正交的贴体坐标系中Level Set函数方程的差分格式及其相应的数值方法。

将Level Set方法应用于自行研制的二维流体动力学程序TDY2D得到编码TDY_DSD,对爆轰波的传播及爆轰驱动的实验模型进行了数值模拟计算,所得的计算值均与实验值符合较好,具有较高精度。

关键词:爆炸力学;爆轰驱动;DSD理论;Level-Set方法;贴体坐标系;数值模拟中图分类号:O241;O382;O383文献标识码:AApplication of the Level Set method for propagation of non-ideal detonation to the numerical simulation for acceleration of metals by detonation waveAbstract:Based on the DSD (Detonation Shock Dynamics) theory, the Level Set method in body-fitted coordinate for propagation of non-ideal detonation is studied in this paper. According to the Hamilton-Jacobi formulation of the Godunov’s scheme, the finite difference method and algorithm for the propagation equation of non-ideal detonation in non-orthogonal body-fitted coordinate are studied. Then the Level Set method is incorporated into the 2D hydrodynamic code TDY2D to form a combination code TDY_DSD and it is used to compute some models about the propagation of detonation wave and the motion of flyers driven by explosive. The computation results are in good agreement with the experimental data and the precision in calculation is high.Key words: Mechanics of explosion; Acceleration by detonation products; DSD theory; Level set method; Body-fitted coordinate; Numerical simulation*作者简介:姜洋(1976-),女,助理研究员,博士生 从事计算流体力学、计算爆炸力学的理论和数值计算方法方面研究通信地址:四川绵阳919信箱105分箱 邮编621900电话:***********Email:**********************0引言对复杂几何形状炸药中爆轰波阵面传播过程的描述是炸药驱动装置设计中的一个重要课题,在军事及工程研究中具有重要的实际应用价值。

The Solution Space of the Unitary Matrix Model String Equation and the

The Solution Space of the Unitary Matrix Model String Equation and the
2 2 +1 2 2 +1
1
interpretation of the UMM is not, however, very clear 22]. In view of this it seems worthwhile to explore their structure further. It is well known 24] that the string equation of the (p; q) HMM can be described as an operator equation P; Q] = 1, where P and Q are scalar ordinary di erential operators of order p and q respectively. They are the well de ned scaling limits of the operators of multiplication and di erentiation by the eigenvalues of the HMM on the orthonormal polynomials used to solve the model. The set of solutions to the string equation P; Q] = 1 was analyzed in 25] by means of the Sato Grassmannian Gr. It was proved that every solution of the string equation corresponds to a point in the big cell Gr(0) of Gr satisfying certain conditions. This fact was used to give a derivation of the Virasoro and W -constraints obtained in 26,27] along the lines of 28{31] and to describe the moduli space of solutions to this string equation. The aim of the present paper is to prove similar results for the version of the string equation arising in the UMM. It was shown in 32] that the string equation of the UMM takes the form P ; Q? ] = const., where for the kth multicritical point P and Q? are 2 2 matrices of di erential operators of order 2k and 1 respectively. For every solution of the string equation one can construct, with this result, a pair of points of the Gr(0) obeying certain conditions. These conditions lead directly to the Virasoro constraints for the corresponding -functions and give a description of the moduli space of solutions. We stress that the above results depend solely on the existence of a continuum limit in which the string equation has the form P ; Q? ] = const. and the matrices of di erential operators P and Q? have a particular form to be discussed in detail in subsequent sections. Our results do not depend on other details of the underlying matrix model. The paper is organized as follows. In section 2 we review the double scaling limit of the UMM in the operator formalism 32]. Since the square root of the speci c heat ows according to the mKdV hierarchy we note that its Miura transforms ow according to KdV and thus give rise to two -functions related by the Hirota bilinear equations of the mKdV hierarchy 33{35]. In section 3 we derive a description of the moduli space of the string equation in terms of a pair of points in Gr(0) related by certain conditions. In section 4 we show the correspondence between points in Gr(0) and solutions to the mKdV hierarchy. The Virasoro constraints are derived from invariance conditions on the points of Gr(0) along the lines of 28,29] . This is most conveniently done in the fermionic representation of the -functions of the mKdV hierarchy. Finally in section 5 we determine the moduli space of the string equation. It is found to be isomorphic to the two fold covering of the 2

汽车行业企业级BOM管理

汽车行业企业级BOM管理
Design-in-Context Support
EBOM Release & Change Control
BOM Restructure
Additional Information Maintain
Release By Plants
Support Product Mgmt
Support Supply Collaboration
Burlwood
Mahogany
Red leather
Software,Circuits
没有CAD数模旳工程零件
Paint, Fluids
Seals
E-BOM零件
swept volume
closed
Support Design-in-Context
Layout
Prod
Definition
Exterior
Enterprise Change Mgmt
Product Change Management
Engineering Change Management (Eng./Mfg/… Change)
Product Structure Building
Product Configuration
Validate and Analysis
Complete data model to support product line and configuration definitionEasily link to vehicle program management data, such as portfolio and requirements, make sure vehicle design/engineering reflect marketing requirementsProvide a Function BOM to facility cross departments early collaboration and vehicle development tasks breakdown

carhart四因子模型的momentum动量因子计算

carhart四因子模型的momentum动量因子计算

carhart四因子模型的momentum动量因子计算英文版Calculation of Momentum Factor in the Carhart Four-Factor ModelIn the realm of financial investing, momentum is a crucial concept that has garnered significant attention from investors and academics alike. The Carhart Four-Factor Model, an extension of the popular three-factor model proposed by Eugene Fama and Kenneth R. French, incorporates momentum as a fourth factor to better explain the performance of assets.The momentum factor in the Carhart model is designed to capture the tendency of stocks with strong recent performance to continue outperforming, and vice versa for stocks with weak performance. This factor is computed by ranking stocks based on their past returns, typically over a 12-month period. Stocks with the highest returns are assigned a positive momentumscore, while stocks with the lowest returns receive a negative momentum score.To calculate the momentum factor, one needs to follow these steps:Data Collection: Gather historical return data for a universe of stocks over a specified time period, such as 12 months.Ranking: Rank the stocks based on their total returns over the chosen time period. Stocks with the highest returns will be at the top, and those with the lowest returns will be at the bottom.Momentum Scoring: Assign momentum scores to the stocks. Typically, the top-performing stocks receive a score of 1, while the bottom-performing stocks receive a score of -1. Intermediate-performing stocks can be assigned scores accordingly, such as 0.5 or -0.5, depending on their ranking.Weighting: Determine the weights for each stock based on its market capitalization or some other metric. This step ensuresthat larger stocks have a greater impact on the momentum factor.Calculation: Calculate the momentum factor by summing the weighted momentum scores of all the stocks in the universe.The momentum factor obtained from this calculation can then be used in investment strategies that aim to capitalize on the momentum effect. Investors who believe in the momentum phenomenon would allocate funds to stocks with positive momentum scores, expecting them to continue their upward trend.In conclusion, the momentum factor in the Carhart Four-Factor Model is a crucial component that helps investors identify stocks with strong momentum and potential for further gains. By incorporating momentum into their investment decisions, investors can hope to enhance their returns and mitigate the risks associated with market fluctuations.中文版Carhart四因子模型的动量因子计算在金融投资领域,动量是一个备受投资者和学者关注的重要概念。

柔性梁

柔性梁
Keywords: Dynamics; Beams; ems
1. Introduction In the traditional approach to dynamic analysis of mechanisms and machines, one assumes that the systems are composed of rigid bodies. However, in modern systems operating at high speed one needs to account for a possibility of the systems undergoing severe elastic deformations to the extent that they cannot be ignored. Flexible beams, capable of properly describing large overall motion and strains, become an indispensable part of such a model. In practical applications of mechanism dynamic analysis, the modeling re®nement of accounting for system deformation can lead to an additional diculty related to so-called sti problems, which appear as a consequence of large dierences in system ¯exibility to various deformation patterns (e.g. axial vs ¯exural deformation). The same problem might also occur if the Lagrange multiplier technique is used to impose the constraints between dierent members, resulting in a set of dierential-algebraic equations describing the system motion.

Matrix and vector models in the strong coupling limit

Matrix and vector models in the strong coupling limit

Abstract: In this paper we consider matrix and vector models in the large N limit (N × N matrices and vectors with N 2 components). For the case of zero-dimensional model (D=0) it is proved that in the strong coupling limit g → ∞ statistical sums of both models coincide up to a coefficient. This is also true for D = 1.
where ϕ is a N × N matrix. The notation dϕ† dϕ should be interpreted in the following way: dϕ† dϕ ≡ Dϕ ≡ dReϕij dImϕij .
i,j
ln ZBV . N2 We will show that at D = 0 in the strong coupling limit (g → ∞) the leading asymptotics of the free energy of this matrix model coincides with the leading asymptotics of the free energy of the corresponding vector model. At D = 1 this is also true, but a more thorough consideration of this case is necessary. Of course, especially interesting are the cases D ≥ 2, but there the question of whether the described hypothesis is true is still open. We would like to emphasize from the beginning that at D ≥ 2 ultraviolet divergences appear both in matrix and vector models, therefore it only makes sense to speak about properly regularized theories. Notice that in all dimensions linear vector models with interaction 1 2 † N F (N 2 tr(ϕ ϕ)) are exactly soluble, i.e. Green’s functions of singlet variables can E=−

Phototaxis during the slug stage of Dictyostelium discoideum a model study

Phototaxis during the slug stage of Dictyostelium discoideum a model study

Phototaxis during the slug stageof Dictyostelium discoideum:a model studyAthanasius F.M.Mare e*,Alexander V.Pan¢lov and Paulien Hogeweg Theoretical Biology and Bioinformatics,University of Utrecht,Padualaan8,3584CH Utrecht,The Netherlands During the slug stage,the cellular slime mould Dictyostelium discoideum moves towards light sources.W e have modelled this phototactic behaviour using a hybrid cellular automata/partial di¡erential equation model.In our model,individual amoebae are not able to measure the direction from which the light comes,and di¡erences in light intensity do not lead to di¡erentiation in motion velocity among the amoebae.Nevertheless,the whole slug orientates itself towards the light.This behaviour is mediated by a modi¢cation of the cyclic AMP(cAMP)waves.As an explanation for phototaxis we propose the following mechanism,which is basically characterized by four processes:(i)light is focused on the distal side of the slug as a result of the so-called`lens-e¡ect';(ii)di¡erences in luminous intensity cause di¡erences in NH3concentration;(iii)NH3alters the excitability of the cell,and thereby the shape of the cAMP wave;and(iv)chemotaxis towards cAMP causes the slug to turn.W e show that this mechanism can account for a number of other behaviours that have been observed in experiments,such as bidirec-tional phototaxis and the cancellation of bidirectionality by a decrease in the light intensity or the addition of charcoal to the medium.Keywords:Dictyostelium;phototaxis;biological models;movement;ammonia;cyclic AMP1.INTRODUCTIONUpon starvation,individual amoebae of the micro-organism Dictyostelium discoideum start to aggregate and form migrating multicellular slugs.During the slug stage prestalk cells group in the anterior part of the slug, whereas prespore cells end up in the posterior part.Each slug culminates in a fruiting body consisting of a globule of spore cells on a slender stalk.The motion of amoebae is orchestrated by waves of cyclic AMP(cAMP),which are formed by a combination of a pulsatile cAMP excre-tion and a cAMP-mediated cAMP response.This is accompanied by a chemotactic response to cAMP. Migrating slugs are orientated by light(phototaxis), temperature gradients(thermotaxis),pH di¡erences (acidotaxis),and wind(rheotaxis).The orientation towards light(as well as towards the other cues)leads to migration towards the soil surface,which is bene¢cial for spore dispersal(Bonner et al.1985).A slug starts to turn towards a light source after 10min of irradiation by light coming from one side (Y umura et al.1992).Phototaxis and thermotaxis must use di¡erent pathways,because even light intensities that are too low to cause a local increase in temperature elicit a response.During phototaxis the slug functions as a lens that focuses light on the side opposite to the light source.This so called`lens-e¡ect'was¢rst postulated by Buder(1920)for Phycomyces,and was shown by F rancis (1964)to be the mechanism in D.discoideum(see also Po¡et al.1986).Several researchers have con¢rmed this lens-e¡ect by,for example,illuminating only half of the slug(F rancis1964),placing the slug in mineral oil (Bonner&Whit¢eld1965),or introducing neutral red into the cells(Ha der&Burkart1983).In all these cases it has been found that a slug orientates away from the light,because light is prevented from focusing on the distal side,but instead illuminates the proximal side more,due to absorption.It is not yet fully understood how the light signal is transformed into tactic behaviour.Bonner et al.(1988) argued that ammonia(NH3)could play an important role in this process,because they found that,on the one hand,light causes slugs to produce NH3,whereas on the other hand,the response to light diminishes when the slugs are completely surrounded by NH3.Several authors have reported negative chemotaxis away from NH3,and have shown that the amount of NH3produced by slugs is su¤cient for such a motion(Bonner et al.1986;F eit& Sollitto1987;Kosugi&Inouye1989;Y umura et al.1992).F rancis(1964)suggested that light may speed up cell motion,and that this could be a su¤cient explanation for phototaxis.This led to the hypothesis that light stimulates the local production of NH3,which in turn stimulates the cells to move faster,forcing the slug to turn towards the light(Bonner et al.1986,1988).However,there is still a dispute about whether NH3does indeed speed up cell motion,because more detailed studies reported that average slug speed may be una¡ected by NH3or light, and not even a transient increase could be detected (Smith et al.1982;Fisher1997).F urthermore,Davies et al. (1993)did not¢nd that NH3caused any change in chemotaxis towards cAMP.During phototaxis the trails of slugs belonging to certain mutant strains can become unstable,bidirectionalProc.R.Soc.Lond.B(1999)266,1351^13601351&1999The Royal Society Received8March1999Accepted13April1999*Author for correspondence(stan@binf.bio.uu.nl).(Fisher &Williams 1981),or even multidirectional (Fisher et al .1985).W e speci¢cally focus on the bidirectional mutants.At low light intensities their bidirectionality decreases,or even disappears (Po¡et al .1986).Moreover,even at high light intensities the trail of such mutants can be made more stable and shown to deviate less from the direction of light if activated charcoal is added to the substrate (Fisher &Williams 1981;Haser &Ha der 1992).There are some indications that this reaction is related to the absorption of NH 3by the charcoal (Bonner et al .1986;Haser &Ha der 1992).In general,slugs orientate towards a piece of charcoal,but if the charcoal is ¢rst saturated with NH 3,it is no longer capable of attracting slugs (Bonner 1993).This is yet another indication that the main e¡ect of the charcoal is to absorb NH 3.In this paper we model phototaxis in terms of an NH 3-mediated modi¢cation of the shape of cAMP waves only;no di¡erences in chemotactic response were assumed.W e show that this modi¢cation is su¤cient to account not only for phototaxis,but also for a number of other beha-viours which are observed in connection with phototaxis.2.THE MODELThere have been several models describing slug migra-tion (Odell &Bonner 1986;Williams et al .1986;Umeda 1989;Savill &Hogeweg 1997;Bretschneider et al .1995;Maree et al .1999).However,these models were not used to describe phototaxis.In this study we have extendedour model for thermotaxis (Maree et al .1999),a hybrid cellular automata (CA)/partial di¡erential equation model,which was formulated by Savill &Hogeweg (1997)to describe the development from single cells to crawling slugs.In these hybrid models a CA is used to1352 A.F.Mare e and others Phototaxis in D.discoideum slugsProc.R.Soc.Lond.B (1999)150075000700650350xyxx(a )(b )(c )Figure 1.Time sequences from simulations of motion during the slug phase of D.discoideum .(a )A simulation of phototaxis,withlight radiating from top to bottom.(b )A simulation of a mutant with bidirectional phototaxis.(c )A simulation of the same mutant surrounded by NH 3-absorbing charcoal.The inset in (a )shows the initial distribution for all three simulations,enlarged two times,with a tip consisting of oscillatory prestalk cells and a body consisting of 40%prestalk and 60%prespore cells.The ¢rst frames show the state after 15min (9000time-steps).Successive frames with intervals of (a )and (c )1h 15min (45000time-steps);and (b )1h 30min (54000time-steps).There are 430amoebae.One time-step (solution of the partial di¡erential equations)corresponds to ca .0.1s and one grid point to 5m m.Cell types are t P f a ,t ,p g where a is oscillatory prestalk (blue),t is prestalk (red),and p is prespore (green).Bond energies are J a ,a 3,J t ,t 5,J p ,p 7,J a ,M 7,J t ,M 8,J p ,M 9,J a ,t 6,J t ,p 8and J a ,p 9.T 2,V 30,l 0X 6,and " 200.The parameters used for the partial di¡erential equations areD c 1,C 1 20,C 2 3,C 3 15,41 0X 5,42 0X 0589,43 0X 5,k 3X 5,d c 0X 05,c 0 À0X 3,D n 15,d n in 0X 01,and b 0X 1.p 1in simulation (a ),and p 4in simulations (b )and (c ).In simulation (c )d n out 0X 3.The partial di¡erential equations are solved by the explicit Euler method (with time-step equal to 0.01and space-step equal to 0.37).The axes are given in grid points.represent individual amoebae and light,and partial di¡erential equations to model di¡usible chemicals.The models are based on a special CA model-formalism,developed by Glazier &Graner (1993).The strength of this formalism is that amoebae are represented as a group of connected automata instead of point-like objects.Therefore amoebae can slide past one another and deform themselves and adjoining amoebae by means of small changes in their boundaries.V ery recently,Jiang et al .(1998)have used the same formalism to describe tip formation during the mound stage of D.discoideum .In our previous study,on thermotaxis in D.discoideum slugs,we showed that taxis can develop as a result of di¡erences in the excitability of the amoebae.These di¡erences in excitability cause the cAMP waves to change shape,which,via the chemotaxis towards cAMP ,causes the slug to turn.W e have made several extensions to our previous model:the CA has been extended with a description of irradiation and refraction,an extra partial di¡erential equation has been added to describe the NH 3dynamics,and the cAMP-modulated cAMP response has become dependent on the NH 3concentration.Since the relationship between NH 3and cell speed is still under dispute,we have decided to omit from our model any di¡erences in chemotaxis between cell types or due to NH 3.W e study phototaxis in two-dimensional (2D)slugs.This is not a limitation of our model,because Bonner (1998)has recently managed to develop an experimental method for producing migrating 2D (one cell thick)slugs which share most basic properties with normal three-dimensional (3D)slugs.Each amoeba occupies about 30automata in the CA,and has an associated label t ,which indicates whether the cell type is prespore,prestalk or oscillatory prestalk (t Pf p ,t ,a g ).Each automaton that is part of an amoeba's boundary has a number of dimensionless free energy bonds.The magnitude of these bonds depends on the cell types they connect.The energy bonds are given by J t 1,t 240,where t i are the types of the two amoebae.The bond energy between an amoeba and the medium isgiven by J t Y M .The total free energy of an amoeba is given by H 'J cell,cell2J cell,medium l (v ÀV )2,(1)where v is the volume of the cell,V the target volume,and l the inelasticity .The ¢nal term ensures that the volume of a cell remains close to V .Mini-mization of the free energy of the amoebae causes deformation of the boundaries.The probability that the boundary will be deformed is either unity if ÁH 5À0X 8,or exp À (ÁH 0X 8)/T ]if ÁH 5À0X 8.W e choose bond energies so that the amoebae adhere to each other,but also so that,if given the possibility,they will sort themselves into three fairly homogeneous groups.Oscillatory cells adhere together more rigidly than prestalk cells,and the latter adhere together morerigidly than prespore cells.F or further details,see Maree et al .(1999).The luminous intensity l is calculated using the ray-tracing technique.F or some basics on ray tracing see,for example,Stavroudis (1972).In our computations,for every position along the upper border of the CA a ray is initiated,with a downward ray direction.Light that enters or leaves a slug is refracted.First,one ¢nds the point where the ray intersects the slug surface.At that point the normal to the surface is calculated:after assigning the value 0or 1to automata outside or inside the slug and taking the eight-neighbourhood into account,we compute the local gradient.The direction of the gradient is used as the surface normal.Knowing the surface normal and the angle of incidence,which is the angle between the incident ray and the surface normal,the direction of the refracted ray is determined using Snell's law .W e use a refractive index of 1.369,which is the value of the refractive index for D.discoideum ,as measured by Ha der &Burkart (1983).Next,one ¢nds the point of intersection of the refracted ray with the slug surface,and so on.T o map the ray,which can move not only horizontally and vertically,but in any direction,ontoPhototaxis in D.discoideum slugsA.F.Maree and others 1353Proc.R.Soc.Lond.B (1999)(a )(b )(c )source of cAMP waveproximal sideslanted wavefront distal sideFigure 2.(a )Snapshot of the luminous intensity distribution during phototaxis.Light is focused on the distal side due to the lens e¡ect.(b )Snapshot of the distribution of NH 3.Lowest concentrations are found on the proximal side.Di¡erent levels of the luminous intensity and the corresponding NH 3concentration are indicated by a colour ramp from dark red (low values)tobright yellow (high values).All parameters are as described in the legend to ¢gure 1.(c )A schematic diagram that shows some of the basic properties of the cAMP wave during phototaxis.The blue lines show the wave shape at di¡erent locations in the slug.the discrete CA,we use Bresenham's line algorithm (Bresenham 1965).T o obtain the luminous intensity values at each point,we add up the intensities of all individual rays passing through it.By default,the intensity of an individual ray is set to unity,which we refer to as the default light intensity .T o change the intensity of the light source,we simply change the intensities of the incoming rays.Three partial di¡erential equations are used to describe the cAMP and NH 3dynamics.The cAMP dynamics can be described reasonably well in a quantitative way by two variable simpli¢ed equations of the FitzHugh ^Nagumo (FHN)type.F or some basic background on FHN-type models we refer to Grindrod (1996).Such a description reproduces the overall characteristics of cAMP waves such as refractoriness and curvature rela-tion.The main advantage of these models is their simpli-city,and therefore their capacity to connect the e¡ects visible in these models with basic properties of the cAMP signalling in D.discoideum .FHN-type models are appro-priate for a preliminary qualitative study of the behaviour but,of course,are not adequate for a detailed quantitative study .Here we investigate the basic e¡ects of heterogeneity in excitability,but we are not concerned with the details of cAMP signalling.So for our present purposes,the FHN models are preferred.F or this study we used the following equations,in which c represents cAMP concentration,and r refractori-ness of the cells.The third partial di¡erential equation describes the NH 3concentration:d cD c Ác Àf (c ,t ,n )Àr ,d rd t 4(c )(kc Àr ),d nd tD n Án g (l )Àd n in n ,(2)with f (c ,t ,n ) C 1c when c `c 1;f (c ,t ,n ) ÀC 2c a (t ,n )when c 14c 4c 2;f (c ,t ,n ) C 3(c À1)when c b c 2,and 4(c ) 41when c `c 1;4(c ) 42when c 14c 4c 2,and 4(c ) 43when c b c 2.T o make the function f (c ,t ,n )continuous,c 1 a (t ,n )/(C 1 C 2),and c 2 (a (t ,n ) C 3)/(C 2 C 3).If the luminous intensity is below the threshold l th 3,g (l ) 0,and if l 5l th ,g (l ) l .The small decay d n in is due to the assimilation of ammonia into amino acids (Dunbar &Wheldrake 1997).D n is much larger than D c ,due to the relatively low di¡usion coe¤cient of cAMP (Dworkin &Keller 1977).Each automaton in the CA is associated with one grid point in the discretized numerical partial di¡erential equations.W eijer et al .(1984)showed that the tip of the slug can be seen as a high-frequency pacemaker,whereas the body of the slug behaves as an excitable medium.This can be modelled using parameter a (t ,n ),which gives a stable limit cycle at negative values,and describes an excitable medium at positive values.Therefore,positive values are used to describe excitable amoebae,whereas negative values are used to describe the oscillatory amoebae in the tip.Our description of NH 3action is based on the following experimental data.Schindler and Sussman (1979)foundthat NH 3inhibits the cAMP-induced cAMP release;Williams et al .(1984)established that this is because NH 3blocks intracellular cAMP accumulation by inhibiting the transitory activation of adenylase cyclase in response to the binding of extracellular cAMP to cell surface recep-tors;and Darcy &Fisher (1990)produced evidence that this inhibition of the cAMP signalling is important in slug behaviour.Since in the model a (t ,n )represents the threshold for the cAMP response,we use the parameter a to express the inhibiting e¡ect of NH 3on the cAMP-modulated cAMP response.W e do this by specifying an NH 3-dependent increase of a .W e assume that this increase saturates:a (t ,n ) a 0tbn1 n /p,(3)with a 0p a 0t 0X 1and a 0a À0X 1.Outside the amoebae we only implemented a small cAMP decay,d c ,caused by external phosphodiesterase,and when we modelled absorption of NH 3by charcoal,we also implemented an NH 3decay d n out :d cd t D c Ác Àd c (c Àc 0),d nd tD n Án Àd n out n ,(4)where c 0is the minimal value of variable c .To create either an NH 3gradient or a background NH 3concentra-tion,we used ¢xed values for the NH 3concentration along the border of the whole CA.The dynamics inside and outside the slug were not modi¢ed.Chemotaxis is incorporated in the model by using the spatial gradient of the cAMP wavefront (Savill &Hogeweg 1997):ÁH H ÁH À"(c automaton Àc neighbour ),where ÁH H is the new change in energy .This makes it more likely that an amoeba will move towards a location with a higher cAMP concentration and less likely that it will move towards one with a lower cAMP concentration.Chemotaxis is only taken into account when cAMP is above a threshold c th 0X 05and refractoriness below a threshold r th 0X 2.Although the process of cell sorting already starts during the mound formation,slugs are still fully capable of generating the prestalk ^prespore pattern due to cell sorting (Sternfeld &David 1981).The ¢rst cells to sort out are the prestalk A cells,which form the tip and are the source of the cAMP waves (Williams et al .1989;Siegert &W eijer 1992).Therefore in all simulations a slug is initiated with a tip consisting of oscillatory prestalk cells.As a `worst case scenario'with respect to the cell sorting,the remaining prestalk cells,together with the prespore cells,are distributed randomly in the body of the slug.Please note,however,that slugs which have completed cell sorting show the same phototactic behaviour as slugs which are still in the process of cell sorting.3.PHOTOTAXISW e were able to reproduce phototaxis in D.discoideum .In ¢gure 1we demonstrate the phototactic behaviour of our model slugs in the form of a time-lapse image;¢gure 1a shows a simulation of phototaxis with light1354 A.F.Mare e and others Phototaxis in D.discoideum slugsProc.R.Soc.Lond.B (1999)Phototaxis in D.discoideum slugs A.F.Maree and others 1355Proc.R.Soc.Lond.B(1999)Figure 3.The change in the location of the slug in time,for di¡erent model experimental settings.Light radiates from top tobottom;the source of the NH 3gradient is also at the top.The location at t 0is set to (0,0),and the simulations lasted for 8h 20min (300000time-steps).The axes are given in grid points.For each plot 32simulations were performed.Slugs wereinitially positioned with respect to the light source at intervals of 22.58,from 08onwards.Two simulations per angle were carried out.(a )Wild type;default light intensity.(b )Wild type;NH 3gradient of 0.02/grid point.(c )Bidirectional mutant;default light intensity.(d )Bidirectional mutant;low mean light intensity of 0.8.To analyse the e¡ect of mean light intensities below unity,we needed to use a continuous function for the NH 3production;g (l ) l m /((l th À0X 5)m l m )l ,with m 12.When a mean light intensity of unity is used,simulations using the continuous function are qualitatively indiscernible from the simulations using the original split-function.(e )Bidirectional mutant;default light plus charcoal.(f )Wild type;default light plus background NH 3concentration of 2.0.All other parameters are as described in the legend to ¢gure1.1.00.50.018013590450.50.00.07.515.022.5(a )(b )αangle,(deg)αangle,(deg)αc h a n g e o f a n g l e (∆ ) (d e g m i n –1)Figure 4.(a )Change of angle towards the light source Á versus the position the slug takes up relative to the direction of irradiation .(b )A more detailed graph of the change of angle between 0and 22.58.(c )Scheme to indicate method used to calculate the change of angle.The various types of line show the change of angle made by the wild type (solid lines),the bidirectional mutant (dotted lines),the mutant at low light intensity (dashed lines),and the mutant surrounded by charcoal(dashed ^dotted lines).The means and s.e.m.values (indicated by the bars)were calculated using the data of ¢gure 3.The means and s.e.m.values were calculated at intervals of (a )98;or (b )2.58,using a Gaussian smoothing with a standard deviation of 98and 2.58again,respectively.The angle is calculated using the mean location of the oscillatory area relative to the mean location of the rest of the slug.The change of angle Á is calculated using a time di¡erence t of 2min 30s (1500time-steps).radiating from top to bottom.The inset shows the initial con¢guration.The oscillatory prestalk cells are coloured blue,the remaining prestalk cells red,and the prespore cells green.(W e have not tried to add pseudo-realism by matching the colours to experimental data from staining experiments.)Initially,the slug moves at an angle of908 relative to the light source,but after2h30min the angle has decreased to308.Within7h the slug has turned completely towards the light.The di¡erences in adhesion between the various cell types are important for the cell sorting(Mare e et al.1999),but have little e¡ect on the phototactic behaviour.In¢gure3the experiments are quanti¢ed,each plot showing the slug trails from32di¡erent simulations.All lines start at the same initial position(0,0),which is visible as a node where all lines originate.Every plot is based on16di¡erent initial orientations at intervals of 22.58.Figure3a shows normal phototaxis.The¢nal orienta-tion is independent of the initial angle,i.e.in the end the direction of motion is always towards the light.However, when the initial angle is larger than908turning of the model slug is initially slow.The mechanism of phototaxis can be described as follows.As a result of the lens e¡ect the light is focused on the distal side,as can be seen in¢gure2a which shows the distribution of the luminous intensity.Figure2b shows the corresponding NH3concentration.Note that although in a snapshot of the luminous intensity discrete e¡ects are clearly visible these do not a¡ect the long-term behaviour,because the slugs'boundary is continuously changing its shape therewith constantly changing the luminous intensity distribution.Because NH3inhibits cAMP it causes a longer oscillating period and a lower wave speed in the region of high NH3concentration (Mare e et al.1999).Hence a region with the shortest oscil-lating period will be located on the proximal side,which will serve as the source of the cAMP waves(see ¢gure2c).Hence the cAMP wave starts slanted,and remains slanted when it passes through the region where the light is focused,because in this region there are large di¡er-ences in NH3concentration(and thus in wave speed) between the proximal and distal side.More posteriorly there are only small variations in the NH3concentration, and as a consequence the cAMP wave straightens out due to the curvature e¡ect(see¢gure2c).The slanted cAMP wave,combined with the chemotaxis towards cAMP, leads to the phototactic response:the amoebae move preferably perpendicular to the wavefront,therewith pushing the oscillatory tip into the direction of the light source.Our model also reproduces experimentally observed negative taxis away from NH3.Figure3b shows the trails of32simulations in a¢eld with the source of NH3located at the top,without any light.The negative chemotaxis is clearly visible.Note that individual amoebae in our model do not have any NH3taxis.The mechanism is simply that the NH3partly di¡uses into the slug and causes an internal NH3gradient.This again gives rise to the di¡erences in excitability which lead to the tactic behaviour.Our model reproduces a deviation in the orientation towards light,similar to what is observed in the so-called bidirectional mutants.W e found this behaviour when the decrease in excitability saturates at higher NH3concen-trations.Figure1b shows a simulation in which we increased the value of p(which gives the half-saturation concentration)four times.The slug still demonstrates pronounced phototaxis,but the¢nal direction of motion is at an angle of148.Figure3c shows the trails of32 di¡erent simulations.This¢gure clearly shows bi-directional phototaxis.Initially,turning can be slow,but the¢nal deviation from the direction of the light path is independent of the initial angle.If initially the slug is positioned at an angle of08,random variations are ampli-¢ed by up to148.In our model and in experiment,bidirectional photo-taxis changes back into unidirectional phototaxis at low light intensities(Po¡et al.1986).Figure3d shows the trails of the`bidirectional mutant'from the previous simulations when the light intensity is decreased from unity to0.8.Now turning takes much longer and the trails are less stable,because the signal is weaker. However,bidirectionality has indeed disappeared.At light intensities stronger than0.8bidirectionality can still be observed,although it is weaker;at weaker light inten-sities phototaxis becomes increasingly less pronounced.F rom experiments it is known that charcoal reduces the deviation in slugs'orientation towards light.W e found similar results in our model.Figure1c shows such a simu-lation,in which the bidirectional mutant is surrounded by charcoal.W e emulated the addition of charcoal by including the external absorption of NH3in equation(4). The slug moves straight to the light,and the bidirectional behaviour has disappeared.Figure3e shows again that this result does not depend on the initial conditions.Even when the initial angle is1808,random deviations let the slug turn either to the right or the left.The mechanism of bidirectional phototaxis can be explained as follows.In equation(3),parameter p gives the NH3concentration at which the decrease in excit-ability is half-saturated,and thereby determines the range of NH3concentrations that a¡ect the excitability.If this parameter is increased(and hence the range is enlarged),di¡erences in excitability along the slug become larger.These di¡erences are locally largest between the area on which the light is focused and the area on the other,more excitable,proximal side.The source of the cAMP wave hardly changes position at all, because this location is determined mainly by the lowest NH3concentration.However,when the wave reaches the region on which the light is focused,the wave shape changes dramatically,as it moves much more slowly in the region of the light focus on the distal side of the slug than on the proximal side.As a result,the wave becomes much more slanted in this region than is found with normal phototaxis.The model does not contain explicit forces.However, the adhesion between the amoebae and between the amoebae and the medium create a surface tension (Glazier&Graner1993),which preserves to a certain extent the shape of the slug.Therefore the chemotactic motion can create force on the slug itself,which has a torque,due to the slanted wavefront.Because the wave is only substantially slanted at a certain distance behind the tip,the oscillatory area is pushed away from the light by1356 A.F.Mare e and others Phototaxis in D.discoideum slugs Proc.R.Soc.Lond.B(1999)。

计量经济学中英文词汇对照

计量经济学中英文词汇对照

Common variance Common variation Communality variance Comparability Comparison of bathes Comparison value Compartment model Compassion Complement of an event Complete association Complete dissociation Complete statistics Completely randomized design Composite event Composite events Concavity Conditional expectation Conditional likelihood Conditional probability Conditionally linear Confidence interval Confidence limit Confidence lower limit Confidence upper limit Confirmatory Factor Analysis Confirmatory research Confounding factor Conjoint Consistency Consistency check Consistent asymptotically normal estimate Consistent estimate Constrained nonlinear regression Constraint Contaminated distribution Contaminated Gausssian Contaminated normal distribution Contamination Contamination model Contingency table Contour Contribution rate Control

Experimental Modal Analysis

Experimental Modal Analysis
Modal Analysis 15
f(t) m c k x(t)
SDOF and MDOF Models Different Modal Analysis Techniques Exciting a Structure Measuring Data Correctly
=
+
+
+⋅⋅⋅+ Modal Analysis Post Processing
Dynamic stiffness (force / displacement) Impedance (force / velocity) Dynamic mass (force /acceleration)
Modal Analysis 13
Alternative Estimators
F(f) H(f) X(f)
2. Shaker Testing
– Modal Exciter ’shakes’ product...serial or parallel measurements – Many types of excitation techniques – Often used in more complex structures
2σ1
1
q1
2 ω01
Bearing
Bearing
1
Γ2
q2
2 ω02
Foundation
2σ 2
Γ3 1
q3
2 ω03
2σ 3
Modal Analysis 10
Definition of Frequency Response Function
F(f) F
H
H(f) X
X(f)

复合材料力学讲义

复合材料力学讲义
加捻的纤维束增强了基体
第32页/共132页
圆形截面纤维增强复合材料对E2的影响
上述分析基于纤维的横截面为方形或矩形时导出实际为圆形,对模型进行修正欧克尔采用了折算半径的概念,令R=df/sdf为圆截面纤维的直径,s为纤维的间距
折算半径实际上反映了纤维含量体积比Vf的影响
第33页/共132页
圆形截面纤维增强复合材料对E2的影响
Ec = (0.4)(6.9x103 MPa) + (0.6)(72.4x103 MPa) = 46.2 x 103 MPa
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刚度的材料力学分析方法
串联模型
与试验值相比,较小,由于纤维随机排列,兼有串联和并联的成分
(iso-stress)
表观弹性模量E2的确定:
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引 言
第2页/共132页
引 言
用实验方法系统测定各种复合材料的宏观弹性特性和微观力学性能的关系涉及参数太多,费用巨大复合材料性能不稳定和试验误差,使试验结果较为分散单用试验手段很难获得全面的、系统的和有良好规律的结果,需要有理论配合微观力学研究改进复合材料宏观特性减少试验工作量反向推算复合材料中纤维和基体的平均特性
In Borsic fiber-reinforced aluminum, the fibers are composed of a thick layer of boron deposited on a small – diameter tungsten filament.
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引 言
第15页/共132页
引 言
简单层板假设宏观均匀线弹性宏观地正交各向异性无初应力纤维假设均匀性线弹性各向同性规则地排列完全成一直线

中英文力学对准

中英文力学对准

一般力学类:分析力学 analytical mechanics拉格朗日乘子 Lagrange multiplier拉格朗日[量] Lagrangian拉格朗日括号 Lagrange bracket循环坐标 cyclic coordinate循环积分 cyclic integral哈密顿[量] Hamiltonian哈密顿函数 Hamiltonian function正则方程 canonical equation正则摄动 canonical perturbation正则变换 canonical transformation正则变量 canonical variable哈密顿原理 Hamilton principle作用量积分 action integral哈密顿-雅可比方程 Hamilton-Jacobi equation作用--角度变量 action-angle variables阿佩尔方程 Appell equation劳斯方程 Routh equation拉格朗日函数 Lagrangian function诺特定理 Noether theorem泊松括号 poisson bracket边界积分法 boundary integral method并矢 dyad运动稳定性 stability of motion轨道稳定性 orbital stability李雅普诺夫函数 Lyapunov function渐近稳定性 asymptotic stability结构稳定性 structural stability久期不稳定性 secular instability弗洛凯定理 Floquet theorem倾覆力矩 capsizing moment自由振动 free vibration固有振动 natural vibration暂态 transient state环境振动 ambient vibration反共振 anti-resonance衰减 attenuation库仑阻尼 Coulomb damping同相分量 in-phase component非同相分量 out-of -phase component超调量 overshoot 参量[激励]振动 parametric vibration模糊振动 fuzzy vibration临界转速 critical speed of rotation阻尼器 damper半峰宽度 half-peak width集总参量系统 lumped parameter system 相平面法 phase plane method相轨迹 phase trajectory等倾线法 isocline method跳跃现象 jump phenomenon负阻尼 negative damping达芬方程 Duffing equation希尔方程 Hill equationKBM方法 KBM method, Krylov-Bogoliu- bov-Mitropol'skii method马蒂厄方程 Mathieu equation平均法 averaging method组合音调 combination tone解谐 detuning耗散函数 dissipative function硬激励 hard excitation硬弹簧 hard spring, hardening spring谐波平衡法harmonic balance method久期项 secular term自激振动 self-excited vibration分界线 separatrix亚谐波 subharmonic软弹簧 soft spring ,softening spring软激励 soft excitation邓克利公式 Dunkerley formula瑞利定理 Rayleigh theorem分布参量系统 distributed parameter system优势频率 dominant frequency模态分析 modal analysis固有模态natural mode of vibration同步 synchronization超谐波 ultraharmonic范德波尔方程 van der pol equation频谱 frequency spectrum基频 fundamental frequencyWKB方法 WKB methodWKB方法Wentzel-Kramers-Brillouin method缓冲器 buffer风激振动 aeolian vibration嗡鸣 buzz倒谱cepstrum颤动 chatter蛇行 hunting阻抗匹配 impedance matching机械导纳 mechanical admittance机械效率 mechanical efficiency机械阻抗 mechanical impedance随机振动 stochastic vibration, random vibration隔振 vibration isolation减振 vibration reduction应力过冲 stress overshoot喘振surge摆振shimmy起伏运动 phugoid motion起伏振荡 phugoid oscillation驰振 galloping陀螺动力学 gyrodynamics陀螺摆 gyropendulum陀螺平台 gyroplatform陀螺力矩 gyroscoopic torque陀螺稳定器 gyrostabilizer陀螺体 gyrostat惯性导航 inertial guidance 姿态角 attitude angle方位角 azimuthal angle舒勒周期 Schuler period机器人动力学 robot dynamics多体系统 multibody system多刚体系统 multi-rigid-body system机动性 maneuverability凯恩方法Kane method转子[系统]动力学 rotor dynamics转子[一支承一基础]系统 rotor-support- foundation system静平衡 static balancing动平衡 dynamic balancing静不平衡 static unbalance动不平衡 dynamic unbalance现场平衡 field balancing不平衡 unbalance不平衡量 unbalance互耦力 cross force挠性转子 flexible rotor分频进动 fractional frequency precession半频进动half frequency precession油膜振荡 oil whip转子临界转速 rotor critical speed自动定心 self-alignment亚临界转速 subcritical speed涡动 whirl固体力学类:弹性力学 elasticity弹性理论 theory of elasticity均匀应力状态 homogeneous state of stress 应力不变量 stress invariant应变不变量 strain invariant应变椭球 strain ellipsoid均匀应变状态 homogeneous state of strain应变协调方程 equation of strain compatibility拉梅常量 Lame constants各向同性弹性 isotropic elasticity旋转圆盘 rotating circular disk 楔wedge开尔文问题 Kelvin problem布西内斯克问题 Boussinesq problem艾里应力函数 Airy stress function克罗索夫--穆斯赫利什维利法 Kolosoff- Muskhelishvili method基尔霍夫假设 Kirchhoff hypothesis板 Plate矩形板 Rectangular plate圆板 Circular plate环板 Annular plate波纹板 Corrugated plate加劲板 Stiffened plate,reinforcedPlate中厚板 Plate of moderate thickness弯[曲]应力函数 Stress function of bending 壳Shell扁壳 Shallow shell旋转壳 Revolutionary shell球壳 Spherical shell[圆]柱壳 Cylindrical shell锥壳Conical shell环壳 Toroidal shell封闭壳 Closed shell波纹壳 Corrugated shell扭[转]应力函数 Stress function of torsion 翘曲函数 Warping function半逆解法 semi-inverse method瑞利--里茨法 Rayleigh-Ritz method松弛法 Relaxation method莱维法 Levy method松弛 Relaxation量纲分析 Dimensional analysis自相似[性] self-similarity影响面 Influence surface接触应力 Contact stress赫兹理论 Hertz theory协调接触 Conforming contact滑动接触 Sliding contact滚动接触 Rolling contact压入 Indentation各向异性弹性 Anisotropic elasticity颗粒材料 Granular material散体力学 Mechanics of granular media热弹性 Thermoelasticity超弹性 Hyperelasticity粘弹性 Viscoelasticity对应原理 Correspondence principle褶皱Wrinkle塑性全量理论 Total theory of plasticity滑动 Sliding微滑Microslip粗糙度 Roughness非线性弹性 Nonlinear elasticity大挠度 Large deflection突弹跳变 snap-through有限变形 Finite deformation 格林应变 Green strain阿尔曼西应变 Almansi strain弹性动力学 Dynamic elasticity运动方程 Equation of motion准静态的Quasi-static气动弹性 Aeroelasticity水弹性 Hydroelasticity颤振Flutter弹性波Elastic wave简单波Simple wave柱面波 Cylindrical wave水平剪切波 Horizontal shear wave竖直剪切波Vertical shear wave体波 body wave无旋波 Irrotational wave畸变波 Distortion wave膨胀波 Dilatation wave瑞利波 Rayleigh wave等容波 Equivoluminal wave勒夫波Love wave界面波 Interfacial wave边缘效应 edge effect塑性力学 Plasticity可成形性 Formability金属成形 Metal forming耐撞性 Crashworthiness结构抗撞毁性 Structural crashworthiness 拉拔Drawing破坏机构 Collapse mechanism回弹 Springback挤压 Extrusion冲压 Stamping穿透Perforation层裂Spalling塑性理论 Theory of plasticity安定[性]理论 Shake-down theory运动安定定理 kinematic shake-down theorem静力安定定理 Static shake-down theorem 率相关理论 rate dependent theorem载荷因子load factor加载准则 Loading criterion加载函数 Loading function加载面 Loading surface塑性加载 Plastic loading塑性加载波 Plastic loading wave简单加载 Simple loading比例加载 Proportional loading卸载 Unloading卸载波 Unloading wave冲击载荷 Impulsive load阶跃载荷step load脉冲载荷 pulse load极限载荷 limit load中性变载 nentral loading拉抻失稳 instability in tension加速度波 acceleration wave本构方程 constitutive equation完全解 complete solution名义应力 nominal stress过应力 over-stress真应力 true stress等效应力 equivalent stress流动应力 flow stress应力间断 stress discontinuity应力空间 stress space主应力空间 principal stress space静水应力状态hydrostatic state of stress对数应变 logarithmic strain工程应变 engineering strain等效应变 equivalent strain应变局部化 strain localization应变率 strain rate应变率敏感性 strain rate sensitivity应变空间 strain space有限应变 finite strain塑性应变增量 plastic strain increment 累积塑性应变 accumulated plastic strain 永久变形 permanent deformation内变量 internal variable应变软化 strain-softening理想刚塑性材料 rigid-perfectly plastic Material刚塑性材料 rigid-plastic material理想塑性材料 perfectl plastic material 材料稳定性stability of material应变偏张量deviatoric tensor of strain应力偏张量deviatori tensor of stress 应变球张量spherical tensor of strain应力球张量spherical tensor of stress路径相关性 path-dependency线性强化 linear strain-hardening应变强化 strain-hardening随动强化 kinematic hardening各向同性强化 isotropic hardening强化模量 strain-hardening modulus幂强化 power hardening塑性极限弯矩 plastic limit bending Moment塑性极限扭矩 plastic limit torque弹塑性弯曲 elastic-plastic bending弹塑性交界面 elastic-plastic interface弹塑性扭转 elastic-plastic torsion粘塑性 Viscoplasticity非弹性 Inelasticity理想弹塑性材料 elastic-perfectly plastic Material极限分析 limit analysis极限设计 limit design极限面limit surface上限定理 upper bound theorem上屈服点upper yield point下限定理 lower bound theorem下屈服点 lower yield point界限定理 bound theorem初始屈服面initial yield surface后继屈服面 subsequent yield surface屈服面[的]外凸性 convexity of yield surface截面形状因子 shape factor of cross-section 沙堆比拟 sand heap analogy屈服Yield屈服条件 yield condition屈服准则 yield criterion屈服函数 yield function屈服面 yield surface塑性势 plastic potential能量吸收装置 energy absorbing device能量耗散率 energy absorbing device塑性动力学 dynamic plasticity塑性动力屈曲 dynamic plastic buckling塑性动力响应 dynamic plastic response塑性波 plastic wave运动容许场 kinematically admissible Field静力容许场 statically admissibleField流动法则 flow rule速度间断 velocity discontinuity滑移线 slip-lines滑移线场 slip-lines field移行塑性铰 travelling plastic hinge塑性增量理论 incremental theory ofPlasticity米泽斯屈服准则 Mises yield criterion普朗特--罗伊斯关系 prandtl- Reuss relation特雷斯卡屈服准则 Tresca yield criterion洛德应力参数 Lode stress parameter莱维--米泽斯关系 Levy-Mises relation亨基应力方程 Hencky stress equation赫艾--韦斯特加德应力空间Haigh-Westergaard stress space洛德应变参数 Lode strain parameter德鲁克公设 Drucker postulate盖林格速度方程Geiringer velocity Equation结构力学 structural mechanics结构分析 structural analysis结构动力学 structural dynamics拱 Arch三铰拱 three-hinged arch抛物线拱 parabolic arch圆拱 circular arch穹顶Dome空间结构 space structure空间桁架 space truss雪载[荷] snow load风载[荷] wind load土压力 earth pressure地震载荷 earthquake loading弹簧支座 spring support支座位移 support displacement支座沉降 support settlement超静定次数 degree of indeterminacy机动分析 kinematic analysis 结点法 method of joints截面法 method of sections结点力 joint forces共轭位移 conjugate displacement影响线 influence line三弯矩方程 three-moment equation单位虚力 unit virtual force刚度系数 stiffness coefficient柔度系数 flexibility coefficient力矩分配 moment distribution力矩分配法moment distribution method力矩再分配 moment redistribution分配系数 distribution factor矩阵位移法matri displacement method单元刚度矩阵 element stiffness matrix单元应变矩阵 element strain matrix总体坐标 global coordinates贝蒂定理 Betti theorem高斯--若尔当消去法 Gauss-Jordan elimination Method屈曲模态 buckling mode复合材料力学 mechanics of composites 复合材料composite material纤维复合材料 fibrous composite单向复合材料 unidirectional composite泡沫复合材料foamed composite颗粒复合材料 particulate composite层板Laminate夹层板 sandwich panel正交层板 cross-ply laminate斜交层板 angle-ply laminate层片Ply多胞固体 cellular solid膨胀 Expansion压实Debulk劣化 Degradation脱层 Delamination脱粘 Debond纤维应力 fiber stress层应力 ply stress层应变ply strain层间应力 interlaminar stress比强度 specific strength强度折减系数 strength reduction factor强度应力比 strength -stress ratio横向剪切模量 transverse shear modulus 横观各向同性 transverse isotropy正交各向异 Orthotropy剪滞分析 shear lag analysis短纤维 chopped fiber长纤维 continuous fiber纤维方向 fiber direction纤维断裂 fiber break纤维拔脱 fiber pull-out纤维增强 fiber reinforcement致密化 Densification最小重量设计 optimum weight design网格分析法 netting analysis混合律 rule of mixture失效准则 failure criterion蔡--吴失效准则 Tsai-W u failure criterion 达格代尔模型 Dugdale model断裂力学 fracture mechanics概率断裂力学 probabilistic fracture Mechanics格里菲思理论 Griffith theory线弹性断裂力学 linear elastic fracturemechanics, LEFM弹塑性断裂力学 elastic-plastic fracture mecha-nics, EPFM断裂 Fracture脆性断裂 brittle fracture解理断裂 cleavage fracture蠕变断裂 creep fracture延性断裂 ductile fracture晶间断裂 inter-granular fracture准解理断裂 quasi-cleavage fracture穿晶断裂 trans-granular fracture裂纹Crack裂缝Flaw缺陷Defect割缝Slit微裂纹Microcrack折裂Kink椭圆裂纹 elliptical crack深埋裂纹 embedded crack[钱]币状裂纹 penny-shape crack预制裂纹 Precrack 短裂纹 short crack表面裂纹 surface crack裂纹钝化 crack blunting裂纹分叉 crack branching裂纹闭合 crack closure裂纹前缘 crack front裂纹嘴 crack mouth裂纹张开角crack opening angle,COA裂纹张开位移 crack opening displacement, COD裂纹阻力 crack resistance裂纹面 crack surface裂纹尖端 crack tip裂尖张角 crack tip opening angle,CTOA裂尖张开位移 crack tip openingdisplacement, CTOD裂尖奇异场crack tip singularity Field裂纹扩展速率 crack growth rate稳定裂纹扩展 stable crack growth定常裂纹扩展 steady crack growth亚临界裂纹扩展 subcritical crack growth 裂纹[扩展]减速 crack retardation止裂crack arrest止裂韧度 arrest toughness断裂类型 fracture mode滑开型 sliding mode张开型 opening mode撕开型 tearing mode复合型 mixed mode撕裂 Tearing撕裂模量 tearing modulus断裂准则 fracture criterionJ积分 J-integralJ阻力曲线 J-resistance curve断裂韧度 fracture toughness应力强度因子 stress intensity factorHRR场 Hutchinson-Rice-Rosengren Field守恒积分 conservation integral有效应力张量 effective stress tensor应变能密度strain energy density能量释放率 energy release rate内聚区 cohesive zone塑性区 plastic zone张拉区 stretched zone热影响区heat affected zone, HAZ延脆转变温度 brittle-ductile transitiontemperature剪切带shear band剪切唇shear lip无损检测 non-destructive inspection双边缺口试件double edge notchedspecimen, DEN specimen单边缺口试件 single edge notchedspecimen, SEN specimen三点弯曲试件 three point bendingspecimen, TPB specimen中心裂纹拉伸试件 center cracked tension specimen, CCT specimen中心裂纹板试件 center cracked panelspecimen, CCP specimen紧凑拉伸试件 compact tension specimen, CT specimen大范围屈服large scale yielding小范围攻屈服 small scale yielding韦布尔分布 Weibull distribution帕里斯公式 paris formula空穴化 Cavitation应力腐蚀 stress corrosion概率风险判定 probabilistic riskassessment, PRA损伤力学 damage mechanics损伤Damage连续介质损伤力学 continuum damage mechanics细观损伤力学 microscopic damage mechanics累积损伤 accumulated damage脆性损伤 brittle damage延性损伤 ductile damage宏观损伤 macroscopic damage细观损伤 microscopic damage微观损伤 microscopic damage损伤准则 damage criterion损伤演化方程 damage evolution equation 损伤软化 damage softening损伤强化 damage strengthening 损伤张量 damage tensor损伤阈值 damage threshold损伤变量 damage variable损伤矢量 damage vector损伤区 damage zone疲劳Fatigue低周疲劳 low cycle fatigue应力疲劳 stress fatigue随机疲劳 random fatigue蠕变疲劳 creep fatigue腐蚀疲劳 corrosion fatigue疲劳损伤 fatigue damage疲劳失效 fatigue failure疲劳断裂 fatigue fracture疲劳裂纹 fatigue crack疲劳寿命 fatigue life疲劳破坏 fatigue rupture疲劳强度 fatigue strength疲劳辉纹 fatigue striations疲劳阈值 fatigue threshold交变载荷 alternating load交变应力 alternating stress应力幅值 stress amplitude应变疲劳 strain fatigue应力循环 stress cycle应力比 stress ratio安全寿命 safe life过载效应 overloading effect循环硬化 cyclic hardening循环软化 cyclic softening环境效应 environmental effect裂纹片crack gage裂纹扩展 crack growth, crack Propagation裂纹萌生 crack initiation循环比 cycle ratio实验应力分析 experimental stressAnalysis工作[应变]片 active[strain] gage基底材料 backing material应力计stress gage零[点]飘移zero shift, zero drift应变测量 strain measurement应变计strain gage应变指示器 strain indicator应变花 strain rosette应变灵敏度 strain sensitivity机械式应变仪 mechanical strain gage 直角应变花 rectangular rosette引伸仪 Extensometer应变遥测 telemetering of strain横向灵敏系数 transverse gage factor 横向灵敏度 transverse sensitivity焊接式应变计 weldable strain gage 平衡电桥 balanced bridge粘贴式应变计 bonded strain gage粘贴箔式应变计bonded foiled gage粘贴丝式应变计 bonded wire gage 桥路平衡 bridge balancing电容应变计 capacitance strain gage 补偿片 compensation technique补偿技术 compensation technique基准电桥 reference bridge电阻应变计 resistance strain gage温度自补偿应变计 self-temperature compensating gage半导体应变计 semiconductor strain Gage集流器slip ring应变放大镜 strain amplifier疲劳寿命计 fatigue life gage电感应变计 inductance [strain] gage 光[测]力学 Photomechanics光弹性 Photoelasticity光塑性 Photoplasticity杨氏条纹 Young fringe双折射效应 birefrigent effect等位移线 contour of equalDisplacement暗条纹 dark fringe条纹倍增 fringe multiplication干涉条纹 interference fringe等差线 Isochromatic等倾线 Isoclinic等和线 isopachic应力光学定律 stress- optic law主应力迹线 Isostatic亮条纹 light fringe 光程差optical path difference热光弹性 photo-thermo -elasticity光弹性贴片法 photoelastic coating Method光弹性夹片法 photoelastic sandwich Method动态光弹性 dynamic photo-elasticity空间滤波 spatial filtering空间频率 spatial frequency起偏镜 Polarizer反射式光弹性仪 reflection polariscope残余双折射效应 residual birefringent Effect应变条纹值 strain fringe value应变光学灵敏度 strain-optic sensitivity 应力冻结效应 stress freezing effect应力条纹值 stress fringe value应力光图 stress-optic pattern暂时双折射效应 temporary birefringent Effect脉冲全息法 pulsed holography透射式光弹性仪 transmission polariscope 实时全息干涉法 real-time holographicinterfero - metry网格法 grid method全息光弹性法 holo-photoelasticity全息图Hologram全息照相 Holograph全息干涉法 holographic interferometry 全息云纹法 holographic moire technique 全息术 Holography全场分析法 whole-field analysis散斑干涉法 speckle interferometry散斑Speckle错位散斑干涉法 speckle-shearinginterferometry, shearography散斑图Specklegram白光散斑法white-light speckle method云纹干涉法 moire interferometry[叠栅]云纹 moire fringe[叠栅]云纹法 moire method云纹图 moire pattern离面云纹法 off-plane moire method参考栅 reference grating试件栅 specimen grating分析栅 analyzer grating面内云纹法 in-plane moire method脆性涂层法 brittle-coating method条带法 strip coating method坐标变换 transformation ofCoordinates计算结构力学 computational structuralmecha-nics加权残量法weighted residual method有限差分法 finite difference method有限[单]元法 finite element method配点法 point collocation里茨法 Ritz method广义变分原理 generalized variational Principle最小二乘法 least square method胡[海昌]一鹫津原理 Hu-Washizu principle 赫林格-赖斯纳原理 Hellinger-Reissner Principle修正变分原理 modified variational Principle约束变分原理 constrained variational Principle混合法 mixed method杂交法 hybrid method边界解法boundary solution method有限条法 finite strip method半解析法 semi-analytical method协调元 conforming element非协调元 non-conforming element混合元 mixed element杂交元 hybrid element边界元 boundary element强迫边界条件 forced boundary condition 自然边界条件 natural boundary condition 离散化 Discretization离散系统 discrete system连续问题 continuous problem广义位移 generalized displacement广义载荷 generalized load广义应变 generalized strain广义应力 generalized stress界面变量 interface variable 节点 node, nodal point[单]元 Element角节点 corner node边节点 mid-side node内节点 internal node无节点变量 nodeless variable杆元 bar element桁架杆元 truss element梁元 beam element二维元 two-dimensional element一维元 one-dimensional element三维元 three-dimensional element轴对称元 axisymmetric element板元 plate element壳元 shell element厚板元 thick plate element三角形元 triangular element四边形元 quadrilateral element四面体元 tetrahedral element曲线元 curved element二次元 quadratic element线性元 linear element三次元 cubic element四次元 quartic element等参[数]元 isoparametric element超参数元 super-parametric element亚参数元 sub-parametric element节点数可变元 variable-number-node element拉格朗日元 Lagrange element拉格朗日族 Lagrange family巧凑边点元 serendipity element巧凑边点族 serendipity family无限元 infinite element单元分析 element analysis单元特性 element characteristics刚度矩阵 stiffness matrix几何矩阵 geometric matrix等效节点力 equivalent nodal force节点位移 nodal displacement节点载荷 nodal load位移矢量 displacement vector载荷矢量 load vector质量矩阵 mass matrix集总质量矩阵 lumped mass matrix相容质量矩阵 consistent mass matrix阻尼矩阵 damping matrix瑞利阻尼 Rayleigh damping刚度矩阵的组集 assembly of stiffnessMatrices载荷矢量的组集 consistent mass matrix质量矩阵的组集 assembly of mass matrices 单元的组集 assembly of elements局部坐标系 local coordinate system局部坐标 local coordinate面积坐标 area coordinates体积坐标 volume coordinates曲线坐标 curvilinear coordinates静凝聚 static condensation合同变换 contragradient transformation形状函数 shape function试探函数 trial function检验函数test function权函数 weight function样条函数 spline function代用函数 substitute function降阶积分 reduced integration零能模式 zero-energy modeP收敛 p-convergenceH收敛 h-convergence掺混插值 blended interpolation等参数映射 isoparametric mapping双线性插值 bilinear interpolation小块检验 patch test非协调模式 incompatible mode 节点号 node number单元号 element number带宽 band width带状矩阵 banded matrix变带状矩阵 profile matrix带宽最小化minimization of band width波前法 frontal method子空间迭代法 subspace iteration method 行列式搜索法determinant search method逐步法 step-by-step method纽马克法Newmark威尔逊法 Wilson拟牛顿法 quasi-Newton method牛顿-拉弗森法 Newton-Raphson method 增量法 incremental method初应变 initial strain初应力 initial stress切线刚度矩阵 tangent stiffness matrix割线刚度矩阵 secant stiffness matrix模态叠加法mode superposition method平衡迭代 equilibrium iteration子结构 Substructure子结构法 substructure technique超单元 super-element网格生成 mesh generation结构分析程序 structural analysis program 前处理 pre-processing后处理 post-processing网格细化 mesh refinement应力光顺 stress smoothing组合结构 composite structure流体动力学类:流体动力学 fluid dynamics连续介质力学 mechanics of continuous media介质medium流体质点 fluid particle无粘性流体 nonviscous fluid, inviscid fluid连续介质假设 continuous medium hypothesis流体运动学 fluid kinematics水静力学 hydrostatics 液体静力学 hydrostatics支配方程 governing equation伯努利方程 Bernoulli equation伯努利定理 Bernonlli theorem毕奥-萨伐尔定律 Biot-Savart law欧拉方程Euler equation亥姆霍兹定理 Helmholtz theorem开尔文定理 Kelvin theorem涡片 vortex sheet库塔-茹可夫斯基条件 Kutta-Zhoukowskicondition布拉休斯解 Blasius solution达朗贝尔佯廖 d'Alembert paradox 雷诺数 Reynolds number施特鲁哈尔数 Strouhal number随体导数 material derivative不可压缩流体 incompressible fluid 质量守恒 conservation of mass动量守恒 conservation of momentum 能量守恒 conservation of energy动量方程 momentum equation能量方程 energy equation控制体积 control volume液体静压 hydrostatic pressure涡量拟能 enstrophy压差 differential pressure流[动] flow流线stream line流面 stream surface流管stream tube迹线path, path line流场 flow field流态 flow regime流动参量 flow parameter流量 flow rate, flow discharge涡旋 vortex涡量 vorticity涡丝 vortex filament涡线 vortex line涡面 vortex surface涡层 vortex layer涡环 vortex ring涡对 vortex pair涡管 vortex tube涡街 vortex street卡门涡街 Karman vortex street马蹄涡 horseshoe vortex对流涡胞 convective cell卷筒涡胞 roll cell涡 eddy涡粘性 eddy viscosity环流 circulation环量 circulation速度环量 velocity circulation 偶极子 doublet, dipole驻点 stagnation point总压[力] total pressure总压头 total head静压头 static head总焓 total enthalpy能量输运 energy transport速度剖面 velocity profile库埃特流 Couette flow单相流 single phase flow单组份流 single-component flow均匀流 uniform flow非均匀流 nonuniform flow二维流 two-dimensional flow三维流 three-dimensional flow准定常流 quasi-steady flow非定常流unsteady flow, non-steady flow 暂态流transient flow周期流 periodic flow振荡流 oscillatory flow分层流 stratified flow无旋流 irrotational flow有旋流 rotational flow轴对称流 axisymmetric flow不可压缩性 incompressibility不可压缩流[动] incompressible flow 浮体 floating body定倾中心metacenter阻力 drag, resistance减阻 drag reduction表面力 surface force表面张力 surface tension毛细[管]作用 capillarity来流 incoming flow自由流 free stream自由流线 free stream line外流 external flow进口 entrance, inlet出口exit, outlet扰动 disturbance, perturbation分布 distribution传播 propagation色散 dispersion弥散 dispersion附加质量added mass ,associated mass收缩 contraction镜象法 image method无量纲参数 dimensionless parameter几何相似 geometric similarity运动相似 kinematic similarity动力相似[性] dynamic similarity平面流 plane flow势 potential势流 potential flow速度势 velocity potential复势 complex potential复速度 complex velocity流函数 stream function源source汇sink速度[水]头 velocity head拐角流 corner flow空泡流cavity flow超空泡 supercavity超空泡流 supercavity flow空气动力学 aerodynamics低速空气动力学 low-speed aerodynamics 高速空气动力学 high-speed aerodynamics 气动热力学 aerothermodynamics亚声速流[动] subsonic flow跨声速流[动] transonic flow超声速流[动] supersonic flow锥形流 conical flow楔流wedge flow叶栅流 cascade flow非平衡流[动] non-equilibrium flow细长体 slender body细长度 slenderness钝头体 bluff body钝体 blunt body翼型 airfoil翼弦 chord薄翼理论 thin-airfoil theory构型 configuration后缘 trailing edge迎角 angle of attack失速stall脱体激波detached shock wave 波阻wave drag诱导阻力 induced drag诱导速度 induced velocity临界雷诺数critical Reynolds number前缘涡 leading edge vortex附着涡 bound vortex约束涡 confined vortex气动中心 aerodynamic center气动力 aerodynamic force气动噪声 aerodynamic noise气动加热 aerodynamic heating离解 dissociation地面效应 ground effect气体动力学 gas dynamics稀疏波 rarefaction wave热状态方程thermal equation of state喷管Nozzle普朗特-迈耶流 Prandtl-Meyer flow瑞利流 Rayleigh flow可压缩流[动] compressible flow可压缩流体 compressible fluid绝热流 adiabatic flow非绝热流 diabatic flow未扰动流 undisturbed flow等熵流 isentropic flow匀熵流 homoentropic flow兰金-于戈尼奥条件 Rankine-Hugoniot condition状态方程 equation of state量热状态方程 caloric equation of state完全气体 perfect gas拉瓦尔喷管 Laval nozzle马赫角 Mach angle马赫锥 Mach cone马赫线Mach line马赫数Mach number马赫波Mach wave当地马赫数 local Mach number冲击波 shock wave激波 shock wave正激波normal shock wave斜激波oblique shock wave头波 bow wave附体激波 attached shock wave激波阵面 shock front激波层 shock layer压缩波 compression wave反射 reflection折射 refraction散射scattering衍射 diffraction绕射 diffraction出口压力 exit pressure超压[强] over pressure反压 back pressure爆炸 explosion爆轰 detonation缓燃 deflagration水动力学 hydrodynamics液体动力学 hydrodynamics泰勒不稳定性 Taylor instability 盖斯特纳波 Gerstner wave斯托克斯波 Stokes wave瑞利数 Rayleigh number自由面 free surface波速 wave speed, wave velocity 波高 wave height波列wave train波群 wave group波能wave energy表面波 surface wave表面张力波 capillary wave规则波 regular wave不规则波 irregular wave浅水波 shallow water wave深水波deep water wave重力波 gravity wave椭圆余弦波 cnoidal wave潮波tidal wave涌波surge wave破碎波 breaking wave船波ship wave非线性波 nonlinear wave孤立子 soliton水动[力]噪声 hydrodynamic noise 水击 water hammer空化 cavitation空化数 cavitation number 空蚀 cavitation damage超空化流 supercavitating flow水翼 hydrofoil水力学 hydraulics洪水波 flood wave涟漪ripple消能 energy dissipation海洋水动力学 marine hydrodynamics谢齐公式 Chezy formula欧拉数 Euler number弗劳德数 Froude number水力半径 hydraulic radius水力坡度 hvdraulic slope高度水头 elevating head水头损失 head loss水位 water level水跃 hydraulic jump含水层 aquifer排水 drainage排放量 discharge壅水曲线back water curve压[强水]头 pressure head过水断面 flow cross-section明槽流open channel flow孔流 orifice flow无压流 free surface flow有压流 pressure flow缓流 subcritical flow急流 supercritical flow渐变流gradually varied flow急变流 rapidly varied flow临界流 critical flow异重流density current, gravity flow堰流weir flow掺气流 aerated flow含沙流 sediment-laden stream降水曲线 dropdown curve沉积物 sediment, deposit沉[降堆]积 sedimentation, deposition沉降速度 settling velocity流动稳定性 flow stability不稳定性 instability奥尔-索末菲方程 Orr-Sommerfeld equation 涡量方程 vorticity equation泊肃叶流 Poiseuille flow奥辛流 Oseen flow剪切流 shear flow粘性流[动] viscous flow层流 laminar flow分离流 separated flow二次流 secondary flow近场流near field flow远场流 far field flow滞止流 stagnation flow尾流 wake [flow]回流 back flow反流 reverse flow射流 jet自由射流 free jet管流pipe flow, tube flow内流 internal flow拟序结构 coherent structure 猝发过程 bursting process表观粘度 apparent viscosity 运动粘性 kinematic viscosity 动力粘性 dynamic viscosity 泊 poise厘泊 centipoise厘沱 centistoke剪切层 shear layer次层 sublayer流动分离 flow separation层流分离 laminar separation 湍流分离 turbulent separation 分离点 separation point附着点 attachment point再附 reattachment再层流化 relaminarization起动涡starting vortex驻涡 standing vortex涡旋破碎 vortex breakdown 涡旋脱落 vortex shedding压[力]降 pressure drop压差阻力 pressure drag压力能 pressure energy型阻 profile drag滑移速度 slip velocity无滑移条件 non-slip condition 壁剪应力 skin friction, frictional drag壁剪切速度 friction velocity磨擦损失 friction loss磨擦因子 friction factor耗散 dissipation滞后lag相似性解 similar solution局域相似 local similarity气体润滑 gas lubrication液体动力润滑 hydrodynamic lubrication 浆体 slurry泰勒数 Taylor number纳维-斯托克斯方程 Navier-Stokes equation 牛顿流体 Newtonian fluid边界层理论boundary later theory边界层方程boundary layer equation边界层 boundary layer附面层 boundary layer层流边界层laminar boundary layer湍流边界层turbulent boundary layer温度边界层thermal boundary layer边界层转捩boundary layer transition边界层分离boundary layer separation边界层厚度boundary layer thickness位移厚度 displacement thickness动量厚度 momentum thickness能量厚度 energy thickness焓厚度 enthalpy thickness注入 injection吸出suction泰勒涡 Taylor vortex速度亏损律 velocity defect law形状因子 shape factor测速法 anemometry粘度测定法 visco[si] metry流动显示 flow visualization油烟显示 oil smoke visualization孔板流量计 orifice meter频率响应 frequency response油膜显示oil film visualization阴影法 shadow method纹影法 schlieren method烟丝法smoke wire method丝线法 tuft method。

latent class model

latent class model

Latent Class ModelsbyJay Magidson, Ph.D.Statistical Innovations Inc.Jeroen K. Vermunt, Ph.D.Tilburg University, the NetherlandsOver the past several years more significant books have been published on latent class and other types of finite mixture models than any other class of statistical models. The recent increase in interest in latent class models is due to the development of extended computer algorithms, which allow today's computers to perform latent class analysis on data containing more than just a few variables. In addition, researchers are realizing that the use of latent class models can yield powerful improvements over traditional approaches to cluster, factor, regression/segmentation, as well as to multivariable biplots and related graphical displays.What are Latent Class Models?Traditional models used in regression, discriminant and log-linear analysis contain parameters that describe only relationships between the observed variables. Latent class (LC) models (also known as finite mixture models) differ from these by including one or more discrete unobserved variables. In the context of marketing research, one will typically interpret the categories of these latent variables, the latent classes, as clusters or segments (Dillon and Kumar 1994; Wedel and Kamakura 1998). In fact, LC analysis provides a powerful new tool to identify important market segments in target marketing. LC models do not rely on the traditional modeling assumptions which are often violated in practice (linear relationship, normal distribution, homogeneity). Hence, they are less subject to biases associated with data not conforming to model assumptions. In addition, LC models have recently been extended (Vermunt and Magidson, 2000a, 2000b) to include variables of mixed scale types (nominal, ordinal, continuous and/or count variables) in the same analysis. Also, for improved cluster or segment description the relationship between the latent classes and external variables (covariates) can be assessed simultaneously with the identification of the clusters. This eliminates the need for the usual second stage of analysis where a discriminant analysis is performed to relate the cluster results to demographic and other variables.Kinds of Latent Class ModelsThree common statistical application areas of LC analysis are those that involve1)clustering of cases,2)variable reduction and scale construction, and3)prediction.This paper introduces the three major kinds of LC models:•LC Cluster Models,•LC Factor Models,•LC Regression Models.Our illustrative examples make use of the new computer program (Vermunt and Magidson, 2000b) called Latent GOLD®.LC Cluster ModelsThe LC Cluster model:•identifies clusters which group together persons (cases) who share similar interests/values/characteristics/behavior,•includes a K-category latent variable, each category representing a cluster. Advantages over traditional types of cluster analysis include:•probability-based classification: Cases are classified into clusters based upon membership probabilities estimated directly from the model,•variables may be continuous, categorical (nominal or ordinal), or counts or any combination of these,•demographics and other covariates can be used for cluster description.Typical marketing applications include:•exploratory data analysis,•development of a behavioral based and other segmentations of customers and prospects.Traditional clustering approaches utilize unsupervised classification algorithms that group cases together that are "near" each other according to some ad hoc definition of "distance". In the last decade interest has shifted towards model-based approaches which use estimated membership probabilities to classify cases into the appropriate cluster. The most popular model-based approach is known as mixture-model clustering, where each latent class represents a hidden cluster (McLachlan and Basford, 1988). Within the marketing research field, this method is sometimes referred to as “latent discriminant analysis” (Dillon and Mulani, 1999). Today's high-speed computers make these computationally intensive methods practical.For the general finite mixture model, not only continuous variables, but also variables that are ordinal, nominal or counts, or any combination of these can be included. Also, covariates can be included for improved cluster description.As an example, we used the LC cluster model to develop a segmentation of current bank customers based upon the types of accounts they have. Separate models were developed specifying different numbers of clusters and the model selected was the one that had the lowest BIC statistic.This criteria resulted in 4 segments which were named:1)Value Seekers (15% of customers),2)Conservative Savers (35% of customers),3)Mainstreamers (40% of customers),4)Investors (10% of customers).For each customer, the model gave estimated membership probabilities for each segment based on their account mix. The resulting segments were verified to be very homogeneous and to differ substantially from each other not only with respect to their mix of accounts, but also with respect to demographics, and profitability. In addition, examination of survey data among the sample of customers for which customer satisfaction data were obtained found some important attitudinal and satisfaction differences between the segments as well. Value seekers were youngest and a high percentage were new customers. Basic savers were oldest.Investors were the most profitable customer segment by far. Although only 10% of all customers, they accounted for over 30% of the bank’s deposits. Survey data pinpointed the areas of the bank with which this segment was least satisfied and a LC regression model (see below) on follow-up data related their dissatisfaction to attrition. The primary uses of the survey data was to identify reasons for low satisfaction and to develop strategies of improving satisfaction in the manner that increased retention.This methodology of segmenting based on behavioral information available on all customers offers many advantages over the common practice of developing segments from survey data and then attempting to allocate all customers to the different clusters. Advantages of developing a segmentation based on behavioral data include:•past behavior is known to be the best predictor of future behavior,•all customers can be assigned to a segment directly, not just the sample for which survey data is available,•improved reliability over segmentations based on attitudes, demographics, purchase intent and other survey variables (when segment membership is based on survey data,a large amount of classification error is almost always present for non-surveyedcustomers) .LC Factor ModelsThe LC Factor model:•identifies factors which group together variables sharing a common source of variation,•can include several ordinal latent variables, each of which contains 2 or more levels,•is similar to maximum likelihood factor analysis in that its use may be exploratory or confirmatory and factors may be assumed to be correlated or uncorrelated(orthogonal).Advantages over traditional factor analysis are:•factors need not be rotated to be interpretable,•factor scores are obtained directly from the model without imposing additional assumptions,•variables may be continuous, categorical (nominal or ordinal), or counts or any combination of these,•extended factor models can be estimated that include covariates and correlated residuals.Typical marketing applications include:•development of composite variables from attitudinal survey items,•development of perceptual maps and other kinds of biplots which relate product and brand usage to behavioral and attitudinal measures and to demographics,•estimation of factor scores,•direct conversion from factors to segments.The conversion of ordinal factors to segments is straightforward. For example, consider a model containing 2 dichotomous factors. In this case, the LC factor model provides membership classification probabilities directly for 4 clusters (segments) based on the classification of cases as high vs. low on each factor: segment 1 = (low, low); segment 2 = (low, high); segment 3 = (high, low) and segment 4 = (high, high). Magidson and Vermunt (2000) found that LC factor models specifying uncorrelated factors often fit data better than comparable cluster models (i.e., cluster models containing the same number of parameters).Figure 1 provides a bi-plot in 2-factor space of lifestyle interests where the horizontal axis represents the probability of being high on factor 1 and the vertical axis the probability of being high on factor 2. The variable AGE was included directly in the LC Factor model as a covariate and therefore shows up in the bi-plot to assist in understanding the meaning of the factors. For example, we see that persons aged 65+ are most likely to be in the (low, high) segment, as are persons expressing an interest in sewing. As a group, their (mean) factor scores are (Factor 1, Factor 2) = (.06, .67).Since these factor scores have a distinct probabilistic interpretation, this bi-plot represents an improvement over traditional biplots and perceptual maps (see Magidson and Vermunt 2000). Individual cases can also be plotted based on their factor scores.Figure 1: Bi-plot for life-style dataThe factor model can also be used to deal with measurement and classification errors in categorical variables. It is actually equivalent to a latent trait (IRT) model without the requirement that the traits be normally distributed.LC Regression ModelsThe LC Regression model, also known as the LC Segmentation model:• is used to predict a dependent variable as a function of predictors,• includes an R-category latent variable, each category representing a homogeneous population (class, segment),• different regressions are estimated for each population (for each latent segment),• classifies cases into segments and develops regression models simultaneously.Advantages over traditional regression models include:• relaxing the traditional assumption that the same model holds for all cases (R=1)allows the development of separate regressions to be used to target each segment,• diagnostic statistics are available to determine the value for R,Factor10.00.20.40.60.8 1.0Factor21.00.80.60.40.20.0AGECAMPINGHUNTINGFISHINGWINESKNITTINGSEWINGFITNESSTENNISGOLFSKIBIKINGBOATINGGARDENTRAVEL 18-2425-3435-4445-5455-6465+camp huntfish wines knit sew fitness tennis golf ski bikeboat gardentravel•for R > 1, covariates can be included in the model to improve classification of each case into the most likely segment.Typical marketing applications include:•customer satisfaction studies: identify particular determinants of customer satisfaction that are appropriate for each customer segment,•conjoint studies: identify the mix of product attributes that appeal to different market segments,•more generally: identify segments that differ from each other with respect to some dependent variable criterion.Like traditional regression modeling, LC regression requires a computer program. As LC regression modeling is relatively new, very few programs currently exist. Our comparisons between LC regression and traditional linear regression are based on the particular forms of LC regression that are implemented in the Latent GOLD® program. For other software see Wedel and DeSarbo (1994) and Wedel and Kamakura (1998). Typical regression programs utilize ordinary least squares estimation in conjunction with a linear model. In particular, such programs are based on two restrictive assumptions about data that are often violated in practice:1)the dependent variable is continuous with prediction error normally distributed,2)the population is homogeneous - one model holds for all cases.LC regression as implemented in the Latent GOLD® program relaxes these assumptions: 1) it accommodates dependent variables that are continuous, categorical (binary, polytomous nominal or ordinal), binomial counts, or Poisson counts,2) the population needs not be homogeneous (i.e., there may be multiple populations as determined by the BIC statistic).One potential drawback for LC models is that there is no guarantee that the solution will be the maximum likelihood solution. LC computer programs typically employ the EM or Newton Raphson algorithm which may converge to a local as opposed to a global maximum. Some programs provide randomized starting values to allow users to increase the likelihood of converging to a global solution by starting the algorithm at different randomly generated starting places. An additional approach is to use Bayesian prior information in conjunction with randomized starting values which eliminates the possibility of obtaining boundary (extreme) solutions and reduces the chance of obtaining local solutions. Generally speaking, we have achieved good results using 10 randomized starting values and small Bayes constants (the default option in the Latent GOLD program).In addition to using predictors to estimate separate regression model for each class, covariates can be specified to refine class descriptions and improve classification of cases into the appropriate latent classes. In this case, LC regression analysis consists of 3 simultaneous steps:1)identify latent classes or hidden segments2)use demographic and other covariates to predict class membership, and3)classify cases into the appropriate classes/segmentsDependent variables may also include repeated/correlated observations of the kind often collected in conjoint marketing studies where persons are asked to rate different product profiles. Below is an example of a full factorial conjoint study designed to assist in the determination of the mix of product attributes for a new product.Conjoint Case StudyIn this example, 400 persons were asked to rate each of 8 different attribute combinations regarding their likelihood to purchase. Hence, there are 8 records per case; one record for each cell in this 2x2x2 conjoint design based on the following attributes:•FASHION (1 = Traditional; 2 = Modern),•QUALITY (1 = Low; 2 = High),•PRICE (1 = Lower; 2 = Higher) .The dependent variable (RATING) is the rating of purchase intent on a five-point scale. The three attributes listed above are used as predictor variables in the model and the following demographic variables are used as covariates:•SEX (1 = Male; 2 = Female),•AGE (1 = 16-24; 2 = 25-39; 3 = 40+).The goal of a traditional conjoint study of this kind is to determine the relative effects of each attribute in influencing one’s purchase decision; a goal attained by estimating regression (or logit) coefficients for these attributes. When the LC regression model is used with the same data, a more general goal is attained. First, it is determined whether the population is homogeneous or whether there exists two or more distinct populations (latent segments) which differ with respect to the relative importance placed on each of the three attributes. If multiple segments are found, separate regression models are estimated simultaneously for each. For example, for one segment, price may be found to influence the purchase decision, while a second segment may be price insensitive, but influenced by quality and modern appearance.We will treat RATING as an ordinal dependent variable and estimate several different models to determine the number of segments (latent classes). We will then show how this methodology can be used to describe the demographic differences between these segments and to classify each respondent into the segment which is most appropriate. We estimated one- to four-class models with and without covariates. Table 1 reports the obtained test results. The BIC values indicate that the three-class model is the best model (BIC is lowest for this model) and that the inclusion of covariates significantly improves the model.Table 1: Test results for regression models for conjoint dataModelLog-likelihood BIC-valueNumber ofparametersWithout covariatesOne segment-440288467Two segments-4141831915Three segments-4087831223Four segments-4080834631With covariatesTwo segments-4088828418Three segments-4036824629Four segments-4026829340The parameter estimates of the three-class model with covariates are reported in Tables 2 and 3 and 4. As can be seen from the first row of Table 2, segment 1 contains about 50% of the subjects, segment 2 contains about 25% and segment 3 contains the remaining 25%. Examination of class-specific probabilities shows that overall, segment 1 is least likely to buy (only 5% are Very Likely to buy) and segment 3 is most likely (21% are Very Likely to buy).♦Table 2: Profile outputClass 1Class 2Class 3Segment Size0.490.260.25RatingVery Unlikely0.210.100.05Not Very Likely0.430.200.12Neutral0.200.370.20Somewhat Likely0.100.210.43Very Likely0.050.110.21♦Table 3: Beta's or parameters of model for dependent variableClass 1Class 2Class 3Wald p-value Wald(=)p-value Fashion 1.97 1.140.04440.19 4.4e-95191.21 3.0e-42Quality0.040.85 2.06176.00 6.5e-38132.33 1.8e-29Price-1.04-0.99-0.94496.38 2.9e-1070.760.68The beta parameter for each predictor is a measure of the influence of that predictor on RATING. The beta effect estimates under the column labeled Class 1 suggest that segment 1 is influenced in a positive way by products for which FASHION = Modern (beta = 1.97) and in negative way by PRICE = Higher (beta = -1.04), but not by QUALITY (beta is approximately 0). We also see that segment 2 is influenced by all 3 attributes, having a preference for those product choices that are modern (beta = 1.14), high quality (beta = .85) and lower priced (beta = -0.99). Members of segment 3 preferhigh quality (beta = 2.06) and the lower (beta = -.94) product choices, but are not influenced by FASHION.Note that PRICE has more or less the same influence on all three segments. The Wald (=) statistic indicates that the differences in these beta effects across classes are not significant (the p-value = .68 which is much higher than .05, the standard level for assessing statistical significance). This means that all 3 segments exhibit price sensitivity to the same degree. This is confirmed when we estimate a model in which this effect is specified to be class-independent. The p-value for the Wald statistic for PRICE is2.9x10-107 indicating that the amount of price sensitivity is highly significant.With respect to the effect of the other two attributes we find large between-segment differences. The predictor FASHION has a strong influence on segment 1, a less strong effect on segment 2, and virtually no effect on segment 3. QUALITY has a strong effect on segment 3, a less strong effect on segment 2, and virtually no effect on segment 1. The fact that the influence of FASHION and QUALITY differs significantly between the 3 segments is confirmed by the significant p-values associated with the Wald(=) statistics for these attributes. For example, for FASHION, the p-value = 3.0x10-42.The beta parameters of the regression model can be used to name the latent segments. Segment 1 could be named the “Fashion-Oriented” segment, segment 3 the “Quality-Oriented” segment, and segment 2 is the segment that takes into account all 3 attributes in their purchase decision.♦Table 4: Gamma's: parameters of model for latent distributionClass 1Class 2Class 3Wald p-valueSexMale-0.560.71-0.1524.47 4.9e-6Female0.56-0.710.15Age16-250.84-0.59-0.2453.098.1e-1126-40-0.320.59-0.2740+-0.520.010.51The parameters of the (multinomial logit) model for the latent distribution appear in Table 4. These show that females have a higher probability of belonging to the “Fashion-oriented” segment (segment 1), while males more often belong to segment 2. The Age effects show that the youngest age group is over-represented in the “Fashion-oriented”segment, while the oldest age group is over-represented in the “Quality oriented”Segment.ConclusionsWe introduced three kinds of LC models and described applications of each that are of interest in marketing research, survey analysis and related fields. It was shown that LC analysis can be used as a replacement for traditional cluster analysis techniques, as a factor analytic tool for reducing dimensionality, and as a tool for estimating separate regression models for each segment. In particular, these models offer powerful new approaches for identifying market segments.BIOSJay Magidson is founder and president of Statistical Innovations, a Boston based consulting, training and software development firm specializing in segmentation modeling. His clients have included A.C. Nielsen, Household Finance, and National Geographic Society. He is widely published on the theory and applications of multivariate statistical methods, and was awarded a patent for a new innovative graphical approach for analysis of categorical data. He taught statistics at Tufts and Boston University, and is chair of the Statistical Modeling Week workshop series. Dr. Magidson designed the SPSS CHAID™ and GOLDMineR® programs, and is the co-developer (with Jeroen Vermunt) of Latent GOLD®.Jeroen Vermunt is Assistant Professor in the Methodology Department of the Faculty of Social and Behavioral Sciences, and Research Associate at the Work and Organization Research Center at Tilburg University in the Netherlands. He has taught a variety of courses and seminars on log-linear analysis, latent class analysis, item response models, models for non-response, and event history analysisall over the world, as well as published extensively on these subjects. Professor Vermunt is developer of the LEM program and co-developer (with Jay Magidson) of Latent GOLD® .ReferencesDillon, W.R., and Kumar, A. (1994). Latent structure and other mixture models in marketing: An integrative survey and overview. R.P. Bagozzi (ed.), Advanced methods of Marketing Research, 352-388,Cambridge: Blackwell Publishers.Dillon, W.R.. and Mulani, N. (1989) LADI: A latent discriminant model for analyzing marketing research data. Journal of Marketing Research, 26, 15-29.Magidson J., and Vermunt, J.K. (2000), Latent Class Factor and Cluster Models, Bi-plots and Related Graphical Displays. Submitted for publication.McLachlan, G.J., and Basford, K.E. (1988). Mixture models: inference and application to clustering. New York: Marcel Dekker.Vermunt, J.K. & Magidson, J. (2000a). “Latent Class Cluster Analysis”, chapter 3 in J.A. Hagenaars and A.L. McCutcheon (eds.), Advances in Latent Class Analysis. Cambridge University Press.Vermunt, J.K. & Magidson, J. (2000b). Latent GOLD 2.0 User's Guide. Belmont, MA: Statistical Innovations Inc.Wedel, M., and DeSarbo, W.S (1994). A review of recent developments in latent class regression models. R.P. Bagozzi (ed.), Advanced methods of Marketing Research, 352-388, Cambridge: Blackwell Publishers. Wedel, M., and Kamakura, W.A. (1998). Market segmentation: Concepts and methodological foundations. Boston: Kluwer Academic Publishers.。

Copula-based multivariate GARCH model with uncorrelated dependent errors

Copula-based multivariate GARCH model with uncorrelated dependent errors

Copula-based multivariate GARCH model with uncorrelated dependent errors$
Tae-Hwy Lee a,∗ , Xiangdong Long b,1
a b
Department of Economics, University of California, Riverside, CA 92521, USA Judge Business School, University of Cambridge, UK
1. Introduction Modeling the conditional covariance matrix is in the core of financial econometrics, as it is crucial for the asset allocation, financial risk management, and derivatives pricing. The multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) models in the literature include the BEKK model by Engle and Kroner (1995), the dynamic conditional correlation (DCC) model by Engle (2002), and the varying correlation (VC) model by Tse and Tsui (2002). However, these models have been estimated under the conditional multivariate normality assumption, while this assumption has been rejected in much of the empirical findings — Fama and French (1993), Richardson and Smith (1993), Longin and Solnik (2001),Mashal and Zeevi (2002), among many others.

线性代数课程中“初等矩阵”的教学设计与思考

线性代数课程中“初等矩阵”的教学设计与思考

创新教育185DOI:10.16660/ki.1674-098X.2007-5640-5366线性代数课程中“初等矩阵”的教学设计与思考①李宁*(河南财经政法大学 数学与信息科学学院 河南郑州 450046)摘 要:线性代数作为高等学校理工科专业的公共基础课,与高等数学相比,线性代数的内容更加抽象,对学生的计算能力要求较高。

目前在课堂教学中教师侧重公式的推导、定理的证明,缺乏实用性。

因此,教师应结合背景知识及现代信息技术,激发学生的学习兴趣,培养学生的分析问题、解决问题的能力,为进一步学习后续课程以及在经济工作中解决一些实际问题打下坚实的理论基础。

关键词:线性代数 基本初等矩阵 Matlab中图分类号:G642 文献标识码:A 文章编号:1674-098X(2020)10(b)-0185-03Teaching Design and Thinking on "Elementary Matrix"in Linear AlgebraLI Ning *(College of Mathematics and Information Science, Henan University of Economics and Law, Zhengzhou,Henan Province, 450046 China)Abstract: Linear algebra is a public basic course for science and engineering students in the institution of higher education, compared with advanced mathematics, its content is more abstract, which requires higher computing ability of students. At present, teachers mainly focus on the derivation of formulas and the proof of theorems, which is lack of practicability. Hence, teachers should combine background knowledge and modern information technology to stimulate students' interest in learning and cultivate their ability to analyze and solve problems, which lay a solid theoretical foundation for the follow-up courses and solving some practical problems in economic work.Key Words: Linear algebra; Basic elementary matrix; Matlab①基金项目:河南省高校科技创新人才项目(项目编号:20HASTIT024)。

WoodburyMatrix 0.0.3 说明书

WoodburyMatrix 0.0.3 说明书

Package‘WoodburyMatrix’June19,2023Title Fast Matrix Operations via the Woodbury Matrix IdentityVersion0.0.3Description A hierarchy of classes and methods for manipulating matrices formed implic-itly from the sums of the inverses of other matrices,a situation commonly encountered in spa-tial statistics and relatedfields.Enables easy use of the Woodbury matrix identity and the ma-trix determinant lemma to allow computation(e.g.,solving linear systems)without hav-ing to form the actual matrix.More information on the underlying linear alge-bra can be found in Harville,D.A.(1997)<doi:10.1007/b98818>.URL https:///mbertolacci/WoodburyMatrixBugReports https:///mbertolacci/WoodburyMatrix/issuesLicense MIT+file LICENSEEncoding UTF-8RoxygenNote7.1.0Imports Matrix,methodsSuggests covr,lintr,testthat,knitr,rmarkdownVignetteBuilder knitrNeedsCompilation noAuthor Michael Bertolacci[aut,cre,cph](<https:///0000-0003-0317-5941>)Maintainer Michael Bertolacci<**********************>Repository CRANDate/Publication2023-06-1903:40:02UTCR topics documented:determinant,WoodburyMatrix,logical-method (2)instantiate (2)mahalanobis (3)normal-distribution-methods (4)solve-methods (5)WoodburyMatrix (6)WoodburyMatrix-class (8)12instantiateIndex11 determinant,WoodburyMatrix,logical-methodCalculate the determinant of a WoodburyMatrix objectDescriptionCalculates the(log)determinant of a WoodburyMatrix using the matrix determinant lemma.Usage##S4method for signature WoodburyMatrix,logicaldeterminant(x,logarithm)Argumentsx A object that is a subclass of WoodburyMatrixlogarithm Logical indicating whether to return the logarithm of the matrix.ValueSame as base::determinant.See Alsobase::determinantinstantiate Instantiate a matrixDescriptionThis is a generic to represent direct instantiation of an implicitly defined matrix.In general this is a bad idea,because it removes the whole purpose of using an implicit representation.Usageinstantiate(x)##S4method for signature GWoodburyMatrixinstantiate(x)##S4method for signature SWoodburyMatrixinstantiate(x)mahalanobis3Argumentsx Implicit matrix to directly instantiate.ValueThe directly instantiated matrix.Functions•instantiate,GWoodburyMatrix-method:Method for general matrices.•instantiate,SWoodburyMatrix-method:Method for symmetric matrices.See AlsoWoodburyMatrix,WoodburyMatrixmahalanobis Mahalanobis distanceDescriptionGeneric for computing the squared Mahalanobis distance.Usagemahalanobis(x,center,cov,inverted=FALSE,...)##S4method for signature ANY,ANY,SWoodburyMatrixmahalanobis(x,center,cov,inverted=FALSE,...)Argumentsx Numeric vector or matrixcenter Numeric vector representing the mean;if omitted,defaults to zero meancov Covariance matrixinverted Whether to treat cov as a precision matrix;must be FALSE for SWoodburyMatrix objects....Passed to the Cholesky function.Methods(by class)•x=ANY,center=ANY,cov=SWoodburyMatrix:Use the Woodbury matrix identity to com-pute the squared Mahalanobis distance with the implicit matrix as the covariance.See Alsomahalanobis4normal-distribution-methodsnormal-distribution-methodsNormal distribution methods for SWoodburyMatrix objectsDescriptionDraw samples and compute density functions for the multivariate normal distribution with an SWoodburyMatrix object as its covariance matrix.Usagedwnorm(x,mean,covariance,log=FALSE)rwnorm(n,mean,covariance)Argumentsx A numeric vector or matrix.mean Optional mean vector;defaults to zero mean.covariance WoodburyMatrix object.log Logical indicating whether to return log of density.n Number of samples to return.If n=1,returns a vector,otherwise returns an nby nrow(W)matrix.Functions•dwnorm:Compute the density of the distribution•rwnorm:Draw samples from the distributionSee AlsoWoodburyMatrixExampleslibrary(Matrix)#Trivial example with diagonal covariance matricesW<-WoodburyMatrix(Diagonal(10),Diagonal(10))x<-rwnorm(10,covariance=W)print(dwnorm(x,covariance=W,log=TRUE))solve-methods5 solve-methods Solve methods for WoodburyMatrix objectsDescriptionMethods based on solve to solve a linear system of equations involving WoodburyMatrix objects.These methods take advantage of the Woodbury matrix identity and therefore can be much more time and memory efficient than forming the matrix directly.Calling this function while omitting the b argument returns the inverse of a.This is NOT recom-mended,since it removes any benefit from using an implicit representation of a.Usage##S4method for signature GWoodburyMatrix,missingsolve(a)##S4method for signature GWoodburyMatrix,numLikesolve(a,b)##S4method for signature GWoodburyMatrix,matrixsolve(a,b)##S4method for signature GWoodburyMatrix,ANYsolve(a,b)##S4method for signature SWoodburyMatrix,missingsolve(a)##S4method for signature SWoodburyMatrix,numLikesolve(a,b)##S4method for signature SWoodburyMatrix,matrixsolve(a,b)##S4method for signature SWoodburyMatrix,ANYsolve(a,b)Argumentsa WoodburyMatrix object.b Matrix,vector,or similar(needs to be compatible with the submatrices a@A anda@V or a@X that define the WoodburyMatrix).ValueThe solution to the linear system,or the inverse of the matrix.The class of the return value will bea vector ifb is a vector,and may otherwise be either a regular matrix or a subclass of Matrix,withthe specific subclass determined by a and b.Functions•solve,GWoodburyMatrix,missing-method:Invert the matrix•solve,GWoodburyMatrix,numLike-method:Solve the linear system•solve,GWoodburyMatrix,matrix-method:Solve the linear system•solve,GWoodburyMatrix,ANY-method:Solve the linear system•solve,SWoodburyMatrix,missing-method:Invert the symmetric matrix•solve,SWoodburyMatrix,numLike-method:Solve the linear system•solve,SWoodburyMatrix,matrix-method:Solve the linear system•solve,SWoodburyMatrix,ANY-method:Solve the linear systemSee AlsoWoodburyMatrix,WoodburyMatrixWoodburyMatrix Create a Woodbury matrix identity matrixDescriptionCreates an implicitly defined matrix representing the equationA−1+UB−1V,where A,U,B and V are n x n,n x p,p x p and p x n matrices,respectively.A symmetric special case is also possible withA−1+XB−1X ,where X is n x p and A and B are additionally symmetric.The available methods are described in WoodburyMatrix-class and in solve.Multiple B/U/V/X matrices are also supported;see below UsageWoodburyMatrix(A,B,U,V,X,O,symmetric)ArgumentsA Matrix A in the definition above.B Matrix B in the definition above,or list of matrices.U Matrix U in the definition above,or list of matrices.Defaults to a diagonal matrix/matrices.V Matrix V in the definition above,or list of matrices.Defaults to a diagonal matrix/matrices.X Matrix X in the definition above,or list of matrices.Defaults to a diagonal matrix/matrices.O Optional,precomputed value of O,as defined above.THIS IS NOT CHECKED FOR CORRECTNESS,and this argument is only provided for advanced userswho have precomputed the matrix for other purposes.symmetric Logical value,whether to create a symmetric or general matrix.See Details section for more information.DetailsThe benefit of using an implicit representation is that the inverse of this matrix can be efficiently calculated viaA−AUO−1V Awhere O=B+V AU,and its determinant bydet(O)det(A)−1det(B)−1.These relationships are often called the Woodbury matrix identity and the matrix determinant lemma,respectively.If A and B are sparse or otherwise easy to deal with,and/or when p<n, manipulating the matrices via these relationships rather than forming W directly can have huge advantageous because it avoids having to create the(typically dense)matrixA−1+UB−1Vdirectly.ValueA GWoodburyMatrix object for a non-symmetric matrix,SWoodburyMatrix for a symmetric matrix.Symmetric formWhere applicable,it’s worth using the symmetric form of the matrix.This takes advantage of the symmetry where possible to speed up operations,takes less memory,and sometimes has numerical benefits.This function will create the symmetric form in the following circumstances:•symmetry=TRUE;or•the argument X is provided;or•A and B are symmetric(according to isSymmetric)and the arguments U and V are NOT pro-vided.Multiple B matricesA more general form allows for multipleB matrices:A−1+ni=1U i B−1iV i,and analogously for the symmetric form.You can use this form by providing a list of matrices asthe B(or U,V or X)arguments.Internally,this is implemented by converting to the standard form byletting B=bdiag(...the B matrices...),U=cbind(..the U matrices...),and so on.The B,U,V and X values are recycled to the length of the longest list,so you can,for instance,provide multiple B matrices but only one U matrix(and vice-versa).ReferencesMore information on the underlying linear algebra can be found in Harville,D.A.(1997)<doi:10.1007/b98818>.See AlsoWoodburyMatrix,solve,instantiateExampleslibrary(Matrix)#Example solving a linear system with general matricesA<-Diagonal(100)B<-rsparsematrix(100,100,0.5)W<-WoodburyMatrix(A,B)str(solve(W,rnorm(100)))#Calculating the determinant of a symmetric systemA<-Diagonal(100)B<-rsparsematrix(100,100,0.5,symmetric=TRUE)W<-WoodburyMatrix(A,B,symmetric=TRUE)print(determinant(W))#Having a lower rank B matrix and an X matrixA<-Diagonal(100)B<-rsparsematrix(10,10,1,symmetric=TRUE)X<-rsparsematrix(100,10,1)W<-WoodburyMatrix(A,B,X=X)str(solve(W,rnorm(100)))#Multiple B matricesA<-Diagonal(100)B1<-rsparsematrix(100,100,1,symmetric=TRUE)B2<-rsparsematrix(100,100,1,symmetric=TRUE)W<-WoodburyMatrix(A,B=list(B1,B2))str(solve(W,rnorm(100)))WoodburyMatrix-class Virtual class for Woodbury identity matricesDescriptionThe WoodburyMatrix is a virtual class,contained by both GWoodburyMatrix(for general matrices) and SWoodburyMatrix(for symmetric matrices).See WoodburyMatrix for construction of these classes.The methods available for these classes are described below;see also the solve methods.This class is itself a subclass of Matrix,so basic matrix methods like nrow,ncol,dim and so on also work.Usage##S4method for signature GWoodburyMatrixisSymmetric(object)##S4method for signature SWoodburyMatrixisSymmetric(object)##S4method for signature GWoodburyMatrix,ANYx%*%y##S4method for signature SWoodburyMatrix,ANYx%*%y##S4method for signature GWoodburyMatrixt(x)##S4method for signature SWoodburyMatrixt(x)Argumentsobject WoodburyMatrix objectx WoodburyMatrix objecty Matrix or vectorFunctions•GWoodburyMatrix-class:Sub-class representing a generic matrix.•SWoodburyMatrix-class:Sub-class representing a symmetric matrix.Also subclasses sym-metricMatrix.•isSymmetric,GWoodburyMatrix-method:Check for symmetry of matrix;always returns FALSE.•isSymmetric,SWoodburyMatrix-method:Check for symmetry of matrix;always returns TRUE.•%*%,GWoodburyMatrix,ANY-method:Matrix multiplication(generally fast and•%*%,SWoodburyMatrix,ANY-method:Matrix multiplication(generally fast and•t,GWoodburyMatrix-method:Return the transpose of the matrix as another GWoodburyMa-trix.•t,SWoodburyMatrix-method:Does nothing,just returns x.SlotsA n x n subclass of Matrix(GWoodburyMatrix)or symmetricMatrix(SWoodburyMatrix).B p x p subclass of Matrix(GWoodburyMatrix)or symmetricMatrix(SWoodburyMatrix).U n x p subclass of Matrix(only forV p x m subclass of Matrix(only forX n x p subclass of Matrix(only forO p x p subclass of MatrixSee AlsoWoodburyMatrix for object construction,Matrix(the parent of this class).Index%*%,GWoodburyMatrix,ANY-method(WoodburyMatrix-class),8%*%,SWoodburyMatrix,ANY-method(WoodburyMatrix-class),8 base::determinant,2Cholesky,3determinant,WoodburyMatrix,logical-method, 2dwnorm(normal-distribution-methods),4 GWoodburyMatrix,7GWoodburyMatrix-class(WoodburyMatrix-class),8 instantiate,2,8instantiate,GWoodburyMatrix-method(instantiate),2instantiate,SWoodburyMatrix-method(instantiate),2isSymmetric,7isSymmetric,GWoodburyMatrix-method(WoodburyMatrix-class),8 isSymmetric,SWoodburyMatrix-method(WoodburyMatrix-class),8 mahalanobis,3,3mahalanobis,ANY,ANY,SWoodburyMatrix-method (mahalanobis),3Matrix,5,9,10normal-distribution-methods,4rwnorm(normal-distribution-methods),4 solve,5,6,8,9solve(solve-methods),5solve,GWoodburyMatrix,ANY-method(solve-methods),5solve,GWoodburyMatrix,matrix-method(solve-methods),5solve,GWoodburyMatrix,missing-method (solve-methods),5solve,GWoodburyMatrix,numLike-method (solve-methods),5solve,SWoodburyMatrix,ANY-method(solve-methods),5solve,SWoodburyMatrix,matrix-method(solve-methods),5solve,SWoodburyMatrix,missing-method (solve-methods),5solve,SWoodburyMatrix,numLike-method (solve-methods),5solve-methods,5 SWoodburyMatrix,7 SWoodburyMatrix-class(WoodburyMatrix-class),8 symmetricMatrix,9,10t,GWoodburyMatrix-method(WoodburyMatrix-class),8t,SWoodburyMatrix-method(WoodburyMatrix-class),8 WoodburyMatrix,2–6,6,8–10 WoodburyMatrix-class,6,811。

一阶旋转倒立摆输出反馈控制

一阶旋转倒立摆输出反馈控制

实验技术与管理Experimental Technology and Management 第37卷第3期2020年3月Vol.37No.3Mar.2020ISSN1002-4956CN11-2034/TDOI:10.16791/ki.sjg.2020.03.037一阶旋转倒立摆输出反馈控制于树友煜,褚建新2,王银敏$(1.吉林大学汽车仿真与控制国家重点实验室,吉林长春130022;2.吉林大学控制科学与工程系,吉林长春130012)摘要:通过Lagrange方程建立倒立摆系统的模型,在平衡点处对模型线性化;由于摆杆及旋转臂的角速度不可测量,研究了一阶旋转倒立摆在不稳定平衡点处的状态观测器设计问题;分别采川极点配置和线性二次型最优控制策略设计了平衡控制器;采用能量控制策略设计了一阶旋转倒立摆的起摆控制器采用Matlab/SimulinkT具完成了仿真环节,并在实验中成功实现了对倒立摆的平衡控制和起摆控制,验证了平衡控制器和起摆控制器的有效性,该实验应用于自动控制相关课程教学,方便高效,具有较好的实验演示效果关键词:一阶旋转倒立摆;观测器;平衡控制;起摆控制;输出反馈中图分类号:TP273文献标识码:A文章编号:1002-4956(2020)03-0165-06Output feedback control of first order rotary inverted pendulumYU Shuyou1'2,CHU Jianxin2,WANG Yinmin2(1.State key laboratory of Automotive Simulation and Control,Jilin University,Changchun130022,China;2.Department of Control Science and Engineering.Jilin University,Changchun130012,China)Abstract:The model of inverted pendulum system is established by Lagrange equation,and the model islinearized at the equilibrium point.Because the angular velocity of the swing bar and rotating arm is notmeasurable,the design on the state observer of the first order inverted pendulum at the unstable equilibrium pointis studied.Pole assignment and linear quadratic optimal control strategies are used to design the balance controller,and by using the energy control strategy,the swing up controller of the first order rotary inverted pendulum isdesigned.Matlab/Simulink is used to complete the simulation,and the balanee control and swing-up control of theinverted pendulum are successfully realized in the experiment,which verifies the effectiveness of the balancecontroller and swing-up controlle匚This experiment is applied to the teaching of the automatic control relatedcourses,which is convenient and efficient,and has better experimental demonstration effect.Key words:first order rotary inverted pendulum;observer;balance control;swing-up control;output feedback倒立摆系统具有多变量、强耦合、非线性且不稳定等特性E,所有重心在上支点在下的问题都可以用倒立摆问题来概括。

科学文献

科学文献

Presented at the NATO Advanced Study Institute on Numerical Linear Algebra, Digital Signal Processing, and Parallel Algorithms, Leuven, Belgium, August, 1988. This research was supported in part by the National Science Foundation under grant DMS-8704196, and by the Foundation Research Program of the Naval Postgraduate School. This paper appears in: Numerical Linear Algebra, Digital Signal Processing and Parallel Algorithms, G.H. Golub and P. Van Dooren, eds., NATO ASI Series, Vol. F70, Springer-Verlag, Berlin, 1991, pp. 385{395. Minor corrections incorporated, October 1993.
Constructing a Unitary Hessenberg Matrix from Spectral Data
Gregory Ammar1 , William Gragg2, Lothar Reichel3
In memory of Peter Henrici
Abstract We consider the numerical construction of a unitary Hessenberg matrix from spectral data using an inverse QR algorithm. Any unitary upper Hessenberg matrix H with nonnegative subdiagonal elements can be represented by 2n ? 1 real parameters. This representation, which we refer to as the Schur parameterization of H; facilitates the development of e cient algorithms for this class of matrices. We show that a unitary upper Hessenberg matrix H with positive subdiagonal elements is determined by its eigenvalues and the eigenvalues of a rank-one unitary perturbation of H: The eigenvalues of the perturbation strictly interlace the eigenvalues of H on the unit circle. AMS(MOS) Subject Classi cation: 15A18, 65F15. Keywords: inverse eigenvalue problem, unitary matrix, orthogonal polynomial.

BlackLitterman模型的初步介绍及应用精品PPT课件

BlackLitterman模型的初步介绍及应用精品PPT课件

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1.63% 12.16%
1.80% -7.87% 0.23% 95.40%
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Black-Litterman模型的 初步介绍及应用
BL模型的初步介绍及应用
应用范围 马可威茨模型 BL模型介绍 BL模型应用
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We propose a unitary matrix model as a regularization of the IIB matrix model of Ishibashi-Kawai-Kitazawa-Tsuchiya IKKT. The fermionic part is incorporated using the overlap formalism in order to avoid unwanted doublers" while preserving the global gauge invariance. This regularization, unlike the one adopted by IKKT, has manifest U110 symmetry, which corresponds to the ten-dimensional translational invariance of the space time. We calculate one-loop e ective action around some typical BPSsaturated con gurations in the weak coupling limit. We also discuss a possible scenario for the dynamical generation of the four-dimensional space time through spontaneous breakdown of the U110 symmetry in the double scaling limit.
Abstract
y
Hale Waihona Puke E-mail address : kitsune@eken.phys.nagoya-u.ac.jp E-mail address : nisimura@eken.phys.nagoya-u.ac.jp
1 Introduction
String theory has been studied as a natural candidate of the uni ed theory including quantum gravity. As the theory of everything, it should explain all the details of the standard model as low energy physics, such as the structure of the gauge group, the three generations of the matter, and even the space-time dimension. Perturbative study of string theory in the eighties revealed, however, that there are in nitely many perturbatively stable vacuua, and that we cannot make any physical prediction as to our present world unless we understand the nonperturbative e ects. It is natural to expect that just as the con nement in QCD was understood only after lattice gauge theory appeared as a constructive de nition of gauge theories 1 , so must the true vacuum of superstring theory be understood once a constructive de nition of string theories could be obtained. Recently, such a constructive de nition of superstring theory using large N matrix model 2, 3 has been proposed. Solitonic objects known as D-branes 4, 5 , which was focused in the context of string duality, plays an important role here. The basic idea is to quantize the lowest dimensional D-brane nonperturbatively, instead of string itself. It has been shown 6 that 1=N expansion of the model proposed by Ishibashi-Kawai-Kitazawa-Tsuchiya IKKT 3 gives the perturbation theory of type IIB superstring by reproducing the light-cone string eld theory through Schwinger-Dyson equation. Above all, the way in which one should take the double scaling limit has been explicitly identi ed at least for su ciently small string coupling constant. We may say that we are now at the stage to extract nonperturbative physics of superstring through this model. As one of the most fundamental issues, let us consider how we can get the space-time dimension. Since the eigenvalues of the bosonic hermitian matrices of the IKKT model are interpreted as space-time coordinates of the D-objects, one possible scenario for the dynamical generation of the space time should be that in an appropriate double scaling limit the distribution of the eigenvalues degenerates to a four-dimensional hypersurface. Note here that the model before regularization possesses a symmetry under a transformation which shifts all the hermitian matrices by the identity matrix times a constant, which corresponds to the 10D translational invariance of the eigenvalue space. The degeneracy of the eigenvalue distribution, therefore, implies the spontaneous breakdown of the translational invariance. The translational invariance is also essential in reproducing the string perturbation theory 6 . A regularization, however, adopted by IKKT 3 in order to make the integration over the 1
DPNU-97-32 July 1997
Unitary IIB Matrix Model and the Dynamical Generation of the Space Time
Naofumi Kitsunezaki and Jun Nishimuray
Department of Physics, Nagoya University, Chikusa-ku, Nagoya 464-01, Japan
bosonic hermitian matrices well-de ned by requiring that the magnitude of the eigenvalues should be less than =a, clearly violates the 10D translational invariance at the boundary of the eigenvalue space. A hope 6 might be that this does not cause any serious problem, since we take the cuto a to zero in the end anyway, but it is certainly a aw of this model. We therefore consider in this paper, a regularization which preserves manifest 10D translational invariance. A natural candidate is to replace the bosonic Hermitian matrices by unitary matrices 1 . It is now the phases of the eigenvalues that are interpreted as the space-time coordinates. Thus the space time is naturally compacti ed to a ten-dimensional torus. The unitary matrix model can be considered as being reduced from a 10D lattice gauge theory. An obvious drawback of this kind of model, therefore, is that the continuous rotational invariance is broken down to the discrete one. Moreover a problem related to lattice fermion arises here. Namely when we consider the fermionic part naively, 210 = 1024 doublers will come out. Since the fermionic matrices are Majorana-Weyl spinor as a representation of the Lorentz group, we cannot easily decouple the unwanted doublers as is the case with ordinary lattice chiral gauge theories. Here we use the overlap formalism 8 , which has been developed to deal with lattice chiral gauge theories. We note that although the overlap formalism as a regularization of ordinary lattice chiral gauge theories has a subtle problem with the local gauge invariance not being preserved on the lattice, its application to the present case is completely safe regarding this, since we do not have the local gauge invariance to take care of. The global gauge invariance, on the other hand, which is indeed one of the important symmetry of the model, is manifestly preserved within the formalism. The unitary matrix model thus de ned has the U110 symmetry, which corresponds to the 10D translational invariacne of the space time. Another important symmetry of the IKKT model is the N =2 supersymmetry regarding the eigenvalues of the bosonic Hermitian matrices as the space-time coordinates 3 . Note that the 10D translational invariance mentioned above gives a subgroup of the supersymmetry. This N =2 supersymmetry comes from 1 the supersymmetry of 10D super Yang-Mills theory, combined with 2 the symmetry under constant shifts of the fermionic matrices. When we consider the unitary matrix model with the overlap formalism, we have 2 but not 1 unfortunately, and therefore, the N =2 supersymmetry is not manifest. However, we adopt a similar spirit as in Ref. 9 and expect the N =2 supersymmetry to be restored in
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