  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.
