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.

翻译：计算流体动力学（CFD）等物理仿真是计算机辅助设计（CAD）优化过程中的计算瓶颈。为突破这一瓶颈，需要两种途径：一是构建高度样本高效的优化框架，二是为长周期仿真建立快速数据驱动代理（代理模型）。两种方法各有优劣。贝叶斯优化通常用于提升样本效率，但其仅针对特定问题，迁移性较差；而代理模型虽能为CFD问题提供快速且通常更具泛化性的解决方案，但收集数据并训练此类模型计算成本高昂。本研究在无人水下航行器（UUV）壳体优化设计的背景下，利用优化与人工智能领域的最新进展，探索了上述两种潜在方法。研究发现，贝叶斯优化-下置信界（BO-LCB）算法在样本效率方面表现最优，且收敛性最佳。随后，我们验证了基于深度神经网络的代理模型对测试数据阻力的预测结果与CFD仿真高度吻合，平均绝对百分比误差（MAPE）仅为1.85%。综合上述结果，当采用代理模型时，设计优化流程实现了两个数量级的加速（精度相当）。据我们所知，这是首次将贝叶斯优化与深度神经网络代理建模应用于UUV设计优化问题的研究，相关成果已以开源软件形式发布。