Construction High Precision Neural Network Proxy Model for Ship Hull Structure Design Based on Hybrid Datasets of Hydrodynamic Loads

Ao Yu ¹Yunbo Li ²Shaofan Li ³Jiaye Gong⁴

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作者信息

1. College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China;Department of Civil and Environmental Engineering,University of California,Berkeley,California 94720,USA
2. College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China;College of Ocean Science and Engineering,Shanghai Maritime University,Shanghai 201303,China
3. Department of Civil and Environmental Engineering,University of California,Berkeley,California 94720,USA
4. College of Ocean Science and Engineering,Shanghai Maritime University,Shanghai 201303,China
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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.

Key words

Deep learning neural network/Hybrid dataset/Proxy model/Ship hull design/Machine learning

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基金项目

a fellowship from China Scholar Council(201806680134)

出版年

2024

哈尔滨工程大学学报(英文版)

哈尔滨工程大学

哈尔滨工程大学学报(英文版)

CSTPCD

影响因子：0.381

ISSN：1671-9433

参考文献量33

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