A high fidelity fluid-structure interaction simulation may require many days to run, on hundreds of cores. This poses a serious burden, both in terms of time and economic considerations, when repetitions of such simulations may be required (e.g. for the purpose of design optimization). In this paper we present strategies based on (constrained) Bayesian optimization (BO) to alleviate this burden. BO is a numerical optimization technique based on Gaussian processes (GP) that is able to efficiently (with minimal calls to the expensive FSI models) converge towards some globally working admissible design, as gauged using a black box objective function. In this study we present a principled design evolution that moves from FSI model verification, through a series of Bridge Simulations (bringing the verification case incrementally closer to the application), in order that we may identify material properties for an underwater, unmanned, autonomous vehicle (UUAV) sail plane. We are able to achieve fast convergence towards an working admissible design, using a small number of FSI simulations (a dozen at most), even when selecting over several design parameters, and while respecting optimization constraints.

高保真度的流固耦合仿真可能需要数百天才能在数百个岩心上运行。当可能需要重复这样的仿真时（例如出于设计优化的目的），这在时间和经济方面都构成了沉重的负担。在本文中，我们提出了基于（约束）贝叶斯优化（BO）的策略来减轻这种负担。BO是一种基于高斯过程（GP）的数值优化技术，它能够有效地（对昂贵的FSI模型进行最少的调用）收敛到某些可以在全球范围内使用的可接受设计，如使用黑匣子所衡量的目标函数。在这项研究中，我们提出了从FSI模型验证到一系列桥梁模拟（使验证案例逐渐接近应用程序）的原则化设计演变，以便我们可以识别水下无人驾驶自动驾驶汽车的材料特性（UUAV）帆飞机。即使选择了多个设计参数并且遵守优化约束，我们也可以使用少量的FSI仿真（最多12个），快速朝着可行的设计过渡。