Abstract
In this work, we constructed a neural network proxy model (NNPM) to estimate the hydrodynamic resistance in the ship hull structure design process, which is based on the hydrodynamic load data obtained from both the potential flow method (PFM) and the viscous flow method (VFM). Here the PFM dataset is applied for the tuning, pre-training, and the VFM dataset is applied for the fine-training. By adopting the PFM and VFM datasets simultaneously, we aim to construct an NNPM to achieve the high-accuracy prediction on hydrodynamic load on ship hull structures exerted from the viscous flow, while ensuring a moderate data-acquiring workload. The high accuracy prediction on hydrodynamic loads and the relatively low dataset establishment cost of the NNPM developed demonstrated the effectiveness and feasibility of hybrid dataset based NNPM achieving a high precision prediction of hydrodynamic loads on ship hull structures. The successful construction of the high precision hydrodynamic prediction NNPM advances the artificial intelligence-assisted design (AIAD) technology for various marine structures.
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Acknowledgement
Yu Ao is supported by a fellowship from China Scholar Council (No. 201806680134), and this support is greatly appreciated.
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Article Highlights
• Developed a data-driven method for constructing proxy models to predict ship hull hydrodynamic loads.
• Utilized a combination of viscous flow and potential flow methods to create hybrid datasets for ship resistance loads.
• Introduced pre-training and fine-training strategies of neural networks to synergistically leverage the strengths of diverse data types.
• Employed the hybrid datasets to significantly reduce the construction costs of the proxy model while ensuring high prediction precision.
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Yu, A., Li, Y., Li, S. et al. Construction High Precision Neural Network Proxy Model for Ship Hull Structure Design Based on Hybrid Datasets of Hydrodynamic Loads. J. Marine. Sci. Appl. 23, 49–63 (2024). https://doi.org/10.1007/s11804-024-00388-4
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DOI: https://doi.org/10.1007/s11804-024-00388-4