A Principled Approach to Design Using High Fidelity Fluid-Structure Interaction Simulations

TL;DR

This paper introduces a Bayesian optimization framework to efficiently find optimal designs in high-fidelity fluid-structure interaction simulations, significantly reducing computational costs for complex engineering applications.

Contribution

It presents a novel, principled approach combining FSI verification, bridge simulations, and Bayesian optimization to rapidly identify optimal designs with minimal expensive simulations.

Findings

Achieved convergence with only a dozen FSI simulations.

Effectively optimized multiple design parameters simultaneously.

Respected all specified optimization constraints.

Abstract

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.