  Physics simulations like computational fluid dynamics (CFD) are a
computational bottleneck in computer-aided design (CAD) optimization processes.
To overcome this bottleneck, one requires either an optimization framework that
is highly sample-efficient, or a fast data-driven proxy (surrogate model) for
long-running simulations. Both approaches have benefits and limitations.
Bayesian optimization is often used for sample efficiency, but it solves one
specific problem and struggles with transferability; alternatively, surrogate
models can offer fast and often more generalizable solutions for CFD problems,
but gathering data for and training such models can be computationally
demanding. In this work, we leverage recent advances in optimization and
artificial intelligence (AI) to explore both of these potential approaches, in
the context of designing an optimal unmanned underwater vehicle (UUV) hull. Our
study finds that the Bayesian Optimization-Lower Condition Bound (BO-LCB)
algorithm is the most sample-efficient optimization framework and has the best
convergence behavior of those considered. Subsequently, we show that our
DNN-based surrogate model predicts drag force on test data in tight agreement
with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%.
Combining these results, we demonstrate a two-orders-of-magnitude speedup (with
comparable accuracy) for the design optimization process when the surrogate
model is used. To our knowledge, this is the first study applying Bayesian
optimization and DNN-based surrogate modeling to the problem of UUV design
optimization, and we share our developments as open-source software.
