A multi-fidelity model management framework for multi-objective aerospace design optimisation

Ben Parsonage, C. Maddock
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引用次数: 1

Abstract

This paper presents a multi-fidelity meta-modelling and model management framework designed to efficiently incorporate increased levels of simulation fidelity from multiple, competing sources into early-stage multidisciplinary design optimisation scenarios. Phase specific/invariant low-fidelity physics-based subsystem models are adaptively corrected via iterative sampling of high(er)-fidelity simulators. The correction process is decomposed into several distinct parametric/non-parametric stages, each leveraging alternate aspects of the available model responses. Globally approximating surrogates are constructed at each degree of fidelity (low, mid, and high) via an automated hyper-parameter selection and training procedure. The resulting hierarchy drives the optimisation process, with local refinement managed according to a confidence-based multi-response adaptive sampling procedure, with bias given to global parameter sensitivities. An application of this approach is demonstrated via the aerodynamic response prediction of a parametrized re-entry vehicle, subjected to a static/dynamic parameter optimisation for three separate single-objective problems. It is found that the proposed data correction process facilitates increased efficiency in attaining a desired approximation accuracy relative to a single-fidelity equivalent model. When applied within the proposed multi-fidelity management framework, clear convergence to the objective optimum is observed for each examined design optimisation scenario, outperforming an equivalent single-fidelity approach in terms of computational efficiency and solution variability.
面向多目标航天设计优化的多保真度模型管理框架
本文提出了一个多保真度元建模和模型管理框架,旨在有效地将来自多个竞争来源的仿真保真度水平提高到早期多学科设计优化场景中。相位特定/不变低保真度物理子系统模型通过高保真度模拟器的迭代采样自适应校正。校正过程被分解为几个不同的参数/非参数阶段,每个阶段利用可用模型响应的替代方面。通过自动超参数选择和训练程序,在每个保真度(低、中、高)上构建全局近似代理。由此产生的层次结构驱动优化过程,根据基于置信度的多响应自适应采样过程进行局部细化管理,并对全局参数灵敏度进行偏差。通过一个参数化再入飞行器的气动响应预测,对三个独立的单目标问题进行了静态/动态参数优化,证明了该方法的应用。发现所提出的数据校正过程有助于提高相对于单保真等效模型获得所需近似精度的效率。当在提出的多保真度管理框架内应用时,观察到每个检查的设计优化场景明显趋同于目标优化,在计算效率和解决方案可变性方面优于等效的单保真度方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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