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：

---

使用高保真流固相互作用模拟进行设计的原理方法

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（以最少的代价调用昂贵的FSI模型）收敛到某些使用黑匣子目标函数衡量的全局最优设计。在这项研究中，我们提出了从FSI模型验证到一系列Bridge Simulation（使验证案例逐渐接近应用程序）的原则化设计演变，为了确定水下，无人驾驶自动驾驶（UUAV）航行飞机的材料特性。即使选择了多个设计参数且遵守优化约束，我们也可以使用少量FSI仿真（最多12个）快速朝着最佳设计收敛。