基于伴随法的船舶船体形状优化：使用条件变分自编码器（CVAE）辅助的推进代理模型
Adjoint-based shape optimization of a ship hull using a Conditional Variational Autoencoder (CVAE) assisted propulsion surrogate model
AI总结：
本文提出利用条件变分自编码器构建推进代理模型，通过优化船舶船体形状，实现阻力降低超过8%。
摘要：
基于伴随法的船舶船体形状优化是解决船舶设计中高维问题的强大工具，特别是在最小化船舶阻力方面。然而，将其应用于采用复杂推进系统的船舶会引入显著挑战。这些挑战源于需要长时间的瞬态模拟，使用小时间步长，以及原始和伴随解的反向时间传播。这些挑战对所需存储和计算能力提出了巨大要求，这显著阻碍了伴随法在工业中的应用。为了解决这个问题，我们提出了一种机器学习辅助的优化框架，该框架采用基于条件变分自编码器（CVAE）的推进系统代理模型。代理模型复制了由Voith Schneider螺旋桨引起的时均流场，并用数据驱动的近似替代几何上和时间分辨的螺旋桨。原始流验证示例表明，代理模型在保持解析螺旋桨必要精度的同时实现了显著的计算节省。优化研究显示，忽略推进系统可能会产生比初始形状更差的性能。相反，所提出的方法产生出阻力减少超过8%的形状。
英文摘要：
Adjoint-based shape optimization of ship hulls is a powerful tool for addressing high-dimensional design problems in naval architecture, particularly in minimizing the ship resistance. However, its application to vessels that employ complex propulsion systems introduces significant challenges. They arise from the need for transient simulations extending over long periods of time with small time steps and from the reverse temporal propagation of the primal and adjoint solutions. These challenges place considerable demands on the required storage and computing power, which significantly hamper the use of adjoint methods in the industry. To address this issue, we propose a machine learning-assisted optimization framework that employs a Conditional Variational Autoencoder-based surrogate model of the propulsion system. The surrogate model replicates the time-averaged flow field induced by a Voith Schneider Propeller and replaces the geometrically and time-resolved propeller with a data-driven approximation. Primal flow verification examples demonstrate that the surrogate model achieves significant computational savings while maintaining the necessary accuracy of the resolved propeller. Optimization studies demonstrate that neglecting the propulsion system can result in hull designs whose performance is inferior to that of the initial shape when subsequently validated using a numerically resolved propulsor. In contrast, the proposed method produces shapes that actually achieve more than an 8% reduction in resistance.
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