Adjoint-based shape optimization of a ship hull using a Conditional Variational Autoencoder (CVAE) assisted propulsion surrogate model

TL;DR

This paper introduces a machine learning-based surrogate model using CVAE to facilitate efficient adjoint-based shape optimization of ship hulls with complex propulsion systems, achieving significant computational savings and improved resistance reduction.

Contribution

It presents a novel CVAE-assisted surrogate model for propulsion systems that enables practical adjoint-based optimization in complex naval design scenarios.

Findings

Surrogate model maintains accuracy while reducing computational costs.

Optimization with the surrogate yields over 8% resistance reduction.

Ignoring propulsion systems can lead to suboptimal ship designs.

Abstract

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 show that ignoring the propulsion system can yield designs that perform worse than the initial shape. In contrast, the proposed method produces shapes that achieve more than an 8\% reduction in resistance.