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[2602.14907] Adjoint-based Shape Optimization, Machine Learning based Surrogate Models, Conditional Variational Autoencoder (CVAE), Voith Schneider propulsion (VSP), Self-propelled Ship, Propulsion Model, Hull Optimization
Summary
This paper presents a machine learning-assisted framework for optimizing ship hull designs using adjoint-based methods, addressing challenges in complex propulsion systems.
Why It Matters
The integration of machine learning with traditional optimization techniques offers significant computational savings and improved design efficiency in naval architecture, particularly for vessels with complex propulsion systems. This work is crucial for advancing ship design methodologies and enhancing performance metrics like resistance reduction.
Key Takeaways
- Adjoint-based shape optimization can significantly reduce ship resistance.
- Machine learning models can replace complex propulsion simulations, saving computational resources.
- Ignoring propulsion system effects can lead to suboptimal designs.
- The proposed method achieved over 8% reduction in resistance for optimized hull shapes.
- The study highlights the importance of integrating machine learning in naval architecture.
Physics > Fluid Dynamics arXiv:2602.14907 (physics) [Submitted on 16 Feb 2026] Title:Adjoint-based Shape Optimization, Machine Learning based Surrogate Models, Conditional Variational Autoencoder (CVAE), Voith Schneider propulsion (VSP), Self-propelled Ship, Propulsion Model, Hull Optimization Authors:Moloud Arian Maram, Georgios Bletsos, Thanh Tung Nguyen, Ahmed Hassan, Michael Palm, Thomas Rung View a PDF of the paper titled Adjoint-based Shape Optimization, Machine Learning based Surrogate Models, Conditional Variational Autoencoder (CVAE), Voith Schneider propulsion (VSP), Self-propelled Ship, Propulsion Model, Hull Optimization, by Moloud Arian Maram and 5 other authors View PDF HTML (experimental) 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 Autoenco...
