Physics simulations are a computational bottleneck in computer-aided design (CAD) optimization processes. Hence, in order to make accurate (computationally expensive) simulations feasible for use in design optimization, one requires either an optimization framework that is highly sample-efficient or fast data-driven proxies (surrogate models) for long running simulations. In this work, we leverage recent advances in optimization and artificial intelligence (AI) to address both of these potential solutions, in the context of designing an optimal unmanned underwater vehicle (UUV). We first investigate and compare the sample efficiency and convergence behavior of different optimization techniques with a standard computational fluid dynamics (CFD) solver in the optimization loop. We then develop a deep neural network (DNN) based surrogate model to approximate drag forces that would otherwise be computed via direct numerical simulation with the CFD solver. The surrogate model is in turn used in the optimization loop of the hull design. 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：物理仿真在计算机辅助设计优化过程中是计算瓶颈。因此，为使精确（计算代价高昂的）仿真能够有效应用于设计优化，需要采用高度样本高效的优化框架或为长时间运行的仿真建立快速数据驱动代理模型。本研究利用优化与人工智能领域的最新进展，在最优无人水下航行器设计背景下，同时探索这两种解决方案。我们首先将不同优化技术与标准计算流体力学求解器集成于优化循环中，系统比较其样本效率与收敛特性。随后，我们构建了基于深度神经网络的代理模型，用以近似原本需通过CFD直接数值模拟计算的阻力。该代理模型进一步被用于壳体设计的优化循环。研究发现，贝叶斯优化下置信区间算法在考察的优化框架中样本效率最高且收敛性最优。基于此，我们验证了深度神经网络代理模型在测试数据上的阻力预测结果与CFD仿真高度吻合，平均绝对百分比误差仅为1.85%。综合上述结果，我们展示了使用代理模型后设计优化过程可实现两个数量级的加速（同时保持可比精度）。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方法应用于无人水下航行器设计优化问题的研究，相关成果已作为开源软件发布。