机械结构的可靠性优化设计外文翻译、中英文翻译、外文文献翻译

英文原文Optimize the reliability of mechanical structure designIt 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 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 optimization 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 second 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, there 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 in the merger of present structural optimization research with reliability-based design philosophy。

In general, an optimization problem can be stated as follows,Minimizesubject to the constraintswhere X is an-dimensional vector called the design vector，f(X) is calledthe objective function and, "k(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 might 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 strength 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 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 computes 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 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 component 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 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 aredescribed by a set of 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 1800'x，The application of differential 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 multipliers became known by the name its inventor, La-grange，Cauchy presented the first application of the steepest descent method to solve minimization problems。