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 optimal 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 optimal design, using a small number of FSI simulations (a dozen at most), even when selecting over several design parameters, and while respecting optimization constraints.

中文翻译：

---

使用高保真流体-结构相互作用模拟进行设计的原则方法

高保真流固耦合仿真可能需要在数百个核心上运行数天。当可能需要重复这种模拟时（例如为了设计优化），这在时间和经济考虑方面都造成了严重的负担。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（以最少的调用昂贵的 FSI 模型）收敛到一些全局最优设计，如使用黑盒目标函数衡量的那样。在这项研究中，我们提出了一个原则性的设计演变，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），以便我们可以确定水下无人自动驾驶飞行器 (UUAV) 帆船的材料特性。我们能够使用少量 FSI 模拟（最多十几个）快速收敛到最佳设计，即使在选择多个设计参数时也是如此，同时尊重优化约束。