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Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible region can be found according to the prediction error in constraint responses. In deterministic optimizations, balancing the predicted constraint and metamodeling uncertainty, expected violation (EV) criterion can be used to explore the design space and add samples to adaptively improve the fitting accuracy of the constraint boundary. However in robust design problems, the predicted error of a robust design constraint cannot be represented by the metamodel prediction uncertainty directly. The conventional EV-based sequential sampling method cannot be used in robust design problems. In this paper, by investigating the effect of metamodeling uncertainty on the robust design responses, an extended robust expected violation (REV) function is proposed to improve the prediction accuracy of the robust design constraints. To validate the benefits of the proposed method, a crashworthiness-based lightweight design example, i.e. a highly nonlinear constrained robust design problem, is given. Results show that the proposed method can mitigate the prediction error in robust constraints and ensure the feasibility of the robust solution. 相似文献
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文章以某发动机罩正向开发为例,通过将头碰工况转化为静态加载的方法,结合刚度模态工况,对发动机罩进行多学科拓扑优化及概念设计。建立全参数化模型,快速设计并验证了发动机罩初始方案。自行开发的二次开发工具包实现了大样本点生成、提交计算及提取结果的自动化。运用基于试验设计与近似模型的参数优化技术,研究了影响发动机罩各项性能的关键因素,同时平衡发动机罩各项性能与重量的关系,得到优化设计方案。通过对Pareto前沿的研究,定量寻找性能之间的走势关系,并寻找性能效率的拐点,从而提高了发动机罩的正向开发能力。 相似文献
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