外文翻译机械结构的可靠性优化设计.docx

1、外文翻译机械结构的可靠性优化设计外文翻译-机械结构的可靠性优化设计英文原文 Optimize the reliability of mechanical structure design It is now generally recognized that structural and mechanical problems are nondeterministic and, consequently, engineering optimum design must cope with un-certainties，Reliability technology provides tools

2、for formal assessment and analysis of such uncertainties，Thus, the combination of reliability-based design procedure sand optimization promises to provide a practical optimum design solution, i，e，, a de-sign having an optimum balance between cost and risk， However, reliabilty-based structural optimi

3、zation programs have not enjoyed the name popularity as their deterministic counterparts， Some reasons for this are suggested， First, reliability analysis can be complicated even for simple systems， There are various methods for handling the uncertainty in similar situations (e，g，, first order secon

4、d moment methods, full distribution methods)， Lacking a single method, individuals are likely to adopt separate strategies for handling the uncertainty in their particular problems， This suggests the possibility of different reliability predictions in similar structural design situations， Then, ther

5、e are diverging opinions on many basic issues, from the very definition of reliability-based optimization, including the definition of the optimum solution, the objective function and the constraints, to its application in structural design practice， There is a need to formally consider these itess

6、in the merger of present structural optimization research with reliability-based design philosophy。 In general, an optimization problem can be stated as follows,Minimize subject to the constraints where X is an-dimensional vector called the design vector， f(X) is called the objective function and, k

7、(X) and i(X) are, respectively, the inequality and equality constraints， The number of variables n and the number of constraints， L need not be related in any way， Thus, L could be less than, equal to or greater than n in a given mathematical programming problem， In some problems, the value of L mig

8、ht be zero which means there are no constraints on the problem， Such type of problems are called unconstrained optimization problems， Those problems for which L is not equal to zero are known as constrained optimization problems。 Traditionally the designer assumes the loading on an element and the s

9、trength of that element to be a single valued characteristic or design value， Perhaps it is equal to some maximum (or minimum) anticipated or nominal value， Safety is assured by introducing a factor of safety, greater than one, usually applied as a reduction factor to strength。 Probabilistic design

10、is propose: as an alternative to the conventional approach with the promise of producing better engineered systems， each factor in the design process can be defined and treated as a random variable， Using method-ology from probabilistic theory, the designer defines the appropriate limit state and co

11、mputes the probability of failure P of the element， The basic design requirement is that，where p f is the maximum allowable probability of failure。 Advantages of adopting the probabilistic design approach are well documented (Wu, 1984)， Basically the arguments for probabilistic design center around

12、the fact that, relative to the conventional approach, a) risk is a more meaningful index of structural performance, and b) a reliability approach to design of a sys-tom can tend to produce an optimum design by ensuring a uniform risk in all components。 Optimization, which may be considered a compone

13、nt of operations research, is the process of obtaining the best result by finding conditions that produce the maximum or minimum value of a function， Table 1，1 illustrates area of operations research。 Mathematical programming techniques, also known as optimization methods, are useful in finding the

14、minimum (or maximum) of a function of several variables under a prescribed set of constraints， Rao (1979) presented a definition and description of some of the various methods of mathematical programming， Stochas-tic process techniques can be used to analyze problems which are described by a set of

15、random variables， Statistical methods enable one to analyze the experimental data and build empirical models to obtain the most accurate representations of physical behavior。 Origins of optimization theory can be traced to the days of Newton, La-grange and Cauchy in the 1800x， The application of dif

16、ferential calculus to optimization was possible because of the contributions of Newton and Leibnitz， The foundations of calculus of variations were laid by Bernoulli, Euler, Lagrange and Weirstrass， The method of optimization for constrained problems, which involves the addition of unknown multiplie

17、rs became known by the name its inventor, La-grange， Cauchy presented the first application of the steepest descent method to solve minimization problems。 In spite of these early contributions, very little progress was made until the middle of the twentieth Gentry, when high-speed digital computers

18、made the implementation of optimization procedures possible and stimulate, d further research on new methods， Spectacular advances followed, producing a m;sssive literature on optimization techniques， This advancement also resulted in the emergence of several well-defined new areas in optimization t

19、heory。 It is interesting to note that major developments in the area of numerical methods of unconstrained optimization have been made in the TTnited Kingdom only in the 1960x， The development of the simplex method by Dantzig (1947) for linear programming and the annunciation of the principle of opt

20、imality by Bellman (195?) for dynamic programming problems paved the wa，; f= development of the methods of constrained optimization， The work by Kuhn and Tucker (1951) on necessary and xuflicient conditions for the optimal xolution of programming problems laid foundations for later research in nonli

21、near programming, the optimization area of this thesis。 Although no single technique has been found to be universally applicable for nonlinear programming, the works by Cacrol (1961)and Fiacco and McCormic (1968) suggested practical solutions by employing well-known techniques of uncon xtrained opti

22、mization， Geometric programming was developed by Dufhn, Zener and Peterson (1960)， Gomory (1963) pioneered work in integer programming, which is at this time an exciting and rapidly developing area of optimization research， Many real-world applications can be cast in this category of problem， Dantzi

23、g (1955) and Charnel and Cooper (1959) developed stochastic programming techniques and solved problems by assuming design parameters to be independent and normally distributed。 Techniques of nonlinear programming, employed in this study, can be categorized 1， one-dimensional minimization method 2， u

24、nconstrained multivariable minimization A， gradient based method B， nongradient based method 3， constrained multivariable minimization A， gradient based method B，gradient based method The gradient based methods require function and derivative evaluations while the non gradient based methods require

25、function evaluations only， In general, one would expect the gradient methods to be more effecti;re, due to the added information provided， However, if analytical derivatives are available, the question of whether a search technique should be used at all is presented， If numerical derivative approxim

26、ations are utilized, the efficiency of the gradient based methods should be approximately the same as that of nongradient based methods， Gradient based methods incorporating numerical derivatives would be expected to present some numerical problems in the vicinity of the optimum, i，e，, approximation

27、s to slopes would become small， Fig， 1，1 shows the $ow chart of general iterative scheme of optimization (Rao, 1979)， No claim is made that some methods are better than any others， A method works well on one problem may perform very poorly on another problem of the same general type， Only after much

28、 experience using all the methods can one judge which method would be better for a particular problem (Kuester snd Mize, 1973). First attempts to apply probabilistic and statistical concepts in structural analysis date back to the beginning of this century， However, the subject aid not receive much

29、attention until after the World War II， In October 1945, a historic paper written by A， M， Freudenthal entitled The Safety of Structures appeared in the proceedings of the American Society of Civil Engineers， The publication of this paper marked the genesis of structural reliability in the U，S，A， Pr

30、ofessor F:eudenthal continued for many years to be in the forefront of structural reliability and risk analysis， During the 1960s there was rapid growth of academic interest in struc-total reliability theory， Classical theory became well developed and widely known through a few influential publicati

31、ons such as that of Freudenthal, Garrelts, and Shi-nouzuka (1966), Pugsley (1966), Kececioglu and Cormier (1964), Ferry-Borges and Castenheta (1971, and Haugen (1968)， However, professional acceptance was low for several reasons， Probabilistic design seemed cumbersome, the theory, particularly syste

32、m analysis, seemed mathematically intractible， Little data were available, and modeling error was an issue which needed to be addressed， But there were early efforts to circumvent these limitations， Turkstra(l070) Yrnted structural design as a problem of decision making under uncertainty and risk， Lind, Turkstra, and Wright (1965) defined the problem of rational design of a code as finding a set of best values of the load and resistance factors， Cornell (1967) suggested the use of a second moment format, and subsequently it