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Robust Tools For Prediction Of Variability And Optimization In Structural Dynamics

机译：用于预测结构动力学和优化结构的强大工具

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摘要

The aims of this work were to quantify the effects of uncertainties of design parameters on the variability of linear and non-linear behaviour of mechanical structures that we wish to optimize, and to calculate optimal and robust solutions resulting from numerical simulations. We propose a method that takes into account the propagation of uncertainties in finite-element models in a multi-objective optimization procedure. This method is based on coupling the stochastic response surface method (SRSM) and the non-dominated sorting genetic algorithm (NSGA). SRSM is based on application of the stochastic finite-element method via the polynomial chaos expansion method or the modal perturbation method. This strategy avoids the use of Monte Carlo simulation, in which costs can become prohibitive in optimization problems, especially when the finite-element models are large and have a considerable number of design parameters. The robust design described here has been developed to obtain an optimum value that is insensitive to changes of design variables within a feasible range.
机译：这项工作的目的是量化设计参数的不确定性对我们希望优化的机械结构的线性和非线性行为的可变性的影响，并计算数值模拟得出的最优且鲁棒的解决方案。我们提出一种方法，该方法考虑了在多目标优化过程中有限元模型中不确定性的传播。该方法基于随机响应面方法（SRSM）和非支配排序遗传算法（NSGA）的耦合。 SRSM基于通过多项式混沌展开法或模态摄动法的随机有限元方法的应用。这种策略避免了使用蒙特卡洛模拟，因为蒙特卡洛模拟的成本在优化问题中变得过高，特别是当有限元模型较大且具有大量设计参数时。已经开发了这里描述的稳健设计，以获得对可行范围内的设计变量的变化不敏感的最佳值。

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