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Machine Learning-Based Recursive Prediction and Application of Green’s Function of Water-Wave Radiation and Diffraction

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Pubblicato in:Journal of Marine Science and Engineering vol. 13, no. 8 (2025), p. 1488-1506
Autore principale: Zheng Minmin
Altri autori: Fan Xinsheng, Li, Chuanqing, Li, Jianpeng, He Duolun, Zhu Renchuan
Pubblicazione:
MDPI AG
Soggetti:
Accuracy
Hydrodynamics
Free surfaces
Green's function
Approximation
Machine learning
Container ships
Numerical methods
Radiation
Learning algorithms
Efficiency
Velocity
Wave diffraction
Predictions
Cargo ships
Decimals
Green's functions
Mathematical models
Derivatives
Bulk carriers
Environmental
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Abstract:The frequency-domain free-surface Green’s function method is widely used in solving ship hydrodynamic problems, with its core challenge lying in the computation of the Green’s function and its partial derivatives. This study analyzes the relationship between the free-surface Green’s function and its derivatives, proposing a machine learning-based recursive prediction method termed the pulsating source recursive prediction method. The accuracy and efficiency of this method under various parameter settings are investigated, and its application to the hydrodynamic calculations of container ship S175 and a bulk carrier is demonstrated. Results show that the predicted Green’s function achieves an accuracy of 3–6 decimals, with computational efficiency surpassing numerical methods and matching analytical approaches. The hydrodynamic results are reliable, confirming the method’s practical value.
ISSN:2077-1312
DOI:10.3390/jmse13081488
Fonte:Engineering Database