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JACIII Vol.28 No.3 pp. 693-703
doi: 10.20965/jaciii.2024.p0693
(2024)

Research Paper:

3D Ship Hull Design Direct Optimization Using Generative Adversarial Network

Luan Thanh Trinh* ORCID Icon, Tomoki Hamagami*, and Naoya Okamoto**

*Yokohama National University
79-1 Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa 240-0067, Japan

**Japan Marine United Corporation
Yokohama Blue Avenue Building, 4-4-2 Minatomirai, Nishi-ku, Yokohama, Kanagawa 220-0012, Japan

Received:
November 7, 2023
Accepted:
February 19, 2024
Published:
May 20, 2024
Keywords:
ship hull design, design optimization, generative adversarial network
Abstract

The direct optimization of ship hull designs using deep learning algorithms is increasingly expected, as it proposes optimization directions for designers almost instantaneously, without relying on complex, time-consuming, and expensive hydrodynamic simulations. In this study, we proposed a GAN-based 3D ship hull design optimization method. We eliminated the dependence on hydrodynamic simulations by training a separate model to predict ship performance indicators. Instead of a standard discriminator, we applied a relativistic average discriminator to obtain better feedback regarding the anomalous designs. We add two new loss functions for the generator: one restricts design variability, and the other sets improvement targets using feedback from the performance estimation model. In addition, we propose a new training strategy to improve learning effectiveness and avoid instability during training. The experimental results show that our model can optimize the form factor by 5.251% while limiting the deterioration of other indicators and the variability of the ship hull design.

Overview of the proposed method

Overview of the proposed method

Cite this article as:
L. Trinh, T. Hamagami, and N. Okamoto, “3D Ship Hull Design Direct Optimization Using Generative Adversarial Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 693-703, 2024.
Data files:
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Last updated on Jul. 12, 2024