Adjoint-based shape optimization of a ship hull using a Conditional Variational Autoencoder (CVAE) assisted propulsion surrogate model
Citation Link: https://doi.org/10.15480/882.19215
Publikationstyp
Journal Article
Date Issued
2026-12-15
Sprache
English
TORE-DOI
Journal
Volume
321
Issue
C
Article Number
107284
Citation
Computers and Fluids 321: 107284 (2026)
Publisher DOI
Publisher
Elsevier
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. A CVAE-based surrogate model captures VSP–hull interaction in adjoint optimization. The max. relative error of predicted velocity fields remains below 1.6%. CFD-validated, propeller-aware optimization achieves over 8% resistance reduction.
Subjects
Adjoint-based shape optimization
Machine learning based surrogate models
Conditional Variational Autoencoder (CVAE)
Voith Schneider propulsion (VSP)
Self-propelled ship
Propulsion model
Hull optimization
DDC Class
623.8: Naval Architecture; Shipbuilding
530.42: Fluid Physics
006.31: Machine Learning
Publication version
publishedVersion