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首页> 外文会议>2001 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference Vol.2: 27th Design Automation Conference, 2001, 2001 >A DIRECT STATISTICAL FORMULATION TO ACCOUNT FOR UNCERTAINTY IN MECHANICAL DESIGN: PROOF-OF-CONCEPT
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A DIRECT STATISTICAL FORMULATION TO ACCOUNT FOR UNCERTAINTY IN MECHANICAL DESIGN: PROOF-OF-CONCEPT

机译：机械设计不确定性的直接统计公式：概念验证

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In recent years there has been an increased need to account for uncertainty in design as industry strives to reduce the amount of physical prototyping in favor of faster and cheaper computer simulations. Traditionally, incorporation of uncertainty in design models has been achieved mainly through either Monte Carlo simulations or Mean Value (MV) approximations. The former, while relatively accurate, can be computationally prohibitive. MV approximations on the other hand, trade accuracy for higher computational speed. The proposed direct statistical formulation is a Corrected Mean Value (CMV) approximation. The CMV exploits the correlation between the parameter coefficients of variation and the fractional error incurred using the MV approach to determine appropriate correction factors. Multiplication of the MV approximation by the correction term significantly reduces the error, without sacrificing computational speed. The approach therefore presents a more accurate approximation of the expected value of a function. This paper presents a numerical proof-of-concept of the CMV formulation. Several examples are presented to illustrate the efficacy of the proposed methodology.
机译：近年来，随着工业界努力减少物理原型设计的数量，以支持更快，更便宜的计算机仿真，越来越需要解决设计中的不确定性。传统上，将不确定性纳入设计模型的方法主要是通过蒙特卡洛模拟或均值（MV）近似实现的。前者虽然相对准确，但在计算上可能会令人望而却步。另一方面，MV近似值可以换取更高的计算速度。提议的直接统计公式是校正均值（CMV）近似值。 CMV利用MV方法利用参数变异系数和分数误差之间的相关性来确定适当的校正因子。 MV近似值乘以校正项会显着减少误差，而不会牺牲计算速度。因此，该方法提供了函数期望值的更精确近似值。本文提出了CMV公式的数字概念验证。列举了几个例子来说明所提出方法的有效性。

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