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LOcal Uncertainty Processing (LOUP) method for multidisciplinary robust design optimization

机译：用于多学科鲁棒设计优化的局部不确定性处理（LOUP）方法

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摘要

In this paper, we develop an easy-to-implement approximate method to take uncertainties into account during a multidisciplinary optimization. Multidisciplinary robust design usually involves setting up a full uncertainty propagation within the system, requiring major modifications in every discipline and on the shared variables. Uncertainty propagation is an expensive process, but robust solutions can be obtained more easily when the disciplines affected by uncertainties have a significant effect on the objectives of the problem. A heuristic method based on local uncertainty processing (LOUP) is presented here, allowing approximate solving of specific robust optimization problems with minor changes in the initial multidisciplinary system. Uncertainty is processed within the disciplines that it impacts directly, without propagation to the other disciplines. A criterion to verify a posteriori the applicability of the method to a given multidisciplinary system is provided. The LOUP method is applied to an aircraft preliminary design industrial test case, in which it allowed to obtain robust designs whose performance is more stable than the one of deterministic solutions, relatively to uncertain parameter variations.
机译：在本文中，我们开发了一种易于实现的近似方法，以在多学科优化中考虑不确定性。多学科鲁棒性设计通常涉及在系统内建立完整的不确定性传播，需要在每个学科和共享变量上进行重大修改。不确定性传播是一个昂贵的过程，但是当受不确定性影响的学科对问题的目标产生重大影响时，可以更轻松地获得可靠的解决方案。本文介绍了一种基于局部不确定性处理（LOUP）的启发式方法，该方法可以近似解决特定的鲁棒优化问题，而在最初的多学科系统中只有很小的变化。不确定性是在直接影响的学科内处理的，而不会传播到其他学科。提供了一种验证后验准则的方法，该准则适用于给定的多学科系统。 LOUP方法应用于飞机初步设计工业测试用例，其中相对于不确定的参数变化，它允许获得性能比确定性解决方案之一更稳定的鲁棒设计。

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