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Nonintrusive uncertainty quantification for automotive crash problems with VPS/Pamcrash

机译：VPS / PAMCRASH的汽车碰撞问题的非功能性不确定性量化

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摘要

Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex systems, enhancing reliability of engineering simulations. In crashworthiness, having an accurate assessment of the behavior of the model uncertainty allows reducing the number of prototypes and associated costs. Carrying out UQ in this framework is especially challenging because it requires highly expensive simulations. In this context, surrogate models (metamodels) allow drastically reducing the computational cost of Monte Carlo process. Different techniques to describe the metamodel are considered, Ordinary Kriging, Polynomial Response Surfaces and a novel strategy (based on Proper Generalized Decomposition) denoted by Separated Response Surface (SRS). A large number of uncertain input parameters may jeopardize the efficiency of the metamodels. Thus, previous to define a metamodel, kernel Principal Component Analysis (kPCA) is found to be effective to simplify the model outcome description. A benchmark crash test is used to show the efficiency of combining metamodels with kPCA.
机译：不确定性量化（UQ）是复杂系统计算建模的关键学科，提高了工程模拟的可靠性。在持续性上，对模型不确定性的行为进行准确评估，允许减少原型的数量和相关成本。在此框架中进行UQ尤其具有挑战性，因为它需要高度昂贵的模拟。在这种情况下，代理模型（Metamodels）允许大大降低蒙特卡罗工艺的计算成本。描述使用分离响应表面（SRS）表示的普通克里格，多项式响应表面和新策略（基于适当的广义分解）的不同技术。大量不确定的输入参数可能会危及元典的效率。因此，先前为了定义元模型，发现内核主成分分析（KPCA）有效地简化模型结果描述。基准碰撞试验用于展示与KPCA相结合的元典的效率。

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