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Hydrodynamic Shape Optimization of a Naval Destroyer by Machine Learning Methods.
- Source :
- Journal of Marine Science & Engineering; Nov2024, Vol. 12 Issue 11, p1979, 18p
- Publication Year :
- 2024
-
Abstract
- This paper explores the integration of advanced machine learning (ML) techniques within simulation-based design optimization (SBDO) processes for naval applications, focusing on the hydrodynamic shape optimization of the DTMB 5415 destroyer model. The use of unsupervised learning for design-space dimensionality reduction, combined with supervised learning through active learning-based multi-fidelity surrogate modeling, allows for significant improvements in computational efficiency while addressing complex, high-dimensional design spaces. By applying these ML techniques to both single- and multi-objective optimizations, aimed at minimizing resistance and enhancing seakeeping performance, the proposed framework demonstrates its practical value in hydrodynamic design. This approach provides a scalable and efficient solution, reducing the reliance on high-fidelity simulations while accelerating the optimization process, without substantial modifications to existing toolchains. A design-space dimensionality reduction of approximately 70% is achieved, reducing the design variables from 22 to 7 while retaining 95% of the original geometric variance. Additionally, computational cost reductions of 65% to 98% are observed, compared to using the full design space and high-fidelity simulations only. [ABSTRACT FROM AUTHOR]
- Subjects :
- SHIP hydrodynamics
STRUCTURAL optimization
SUPERVISED learning
SEAKEEPING
Subjects
Details
- Language :
- English
- ISSN :
- 20771312
- Volume :
- 12
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- Journal of Marine Science & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 181166241
- Full Text :
- https://doi.org/10.3390/jmse12111979