基于水动力载荷混合数据集的高精度神经网络代理模型构建  

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

作　　者：敖愈 李云波 李少凡 龚家烨 Ao Yu;Yunbo Li;Shaofan Li;Jiaye Gong(College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China;Department of Civil and Environmental Engineering,University of California,Berkeley,California 94720,USA;College of Ocean Science and Engineering,Shanghai Maritime University,Shanghai 201303,China)

机构地区：[1]College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China [2]Department of Civil and Environmental Engineering,University of California,Berkeley,California 94720,USA [3]College of Ocean Science and Engineering,Shanghai Maritime University,Shanghai 201303,China

出　　处：《哈尔滨工程大学学报（英文版）》2024年第1期49-63,共15页Journal of Marine Science and Application

基　　金：supported by a fellowship from China Scholar Council(No.201806680134).

摘　　要：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.

关 键 词：Deep learning neural network Hybrid dataset Proxy model Ship hull design Machine learning 

分 类 号：U661.3[交通运输工程—船舶及航道工程]