An artificial intelligence-aided design (AIAD) of ship hull structures

作     者：Yu Ao Yunbo Li Jiaye Gong Shaofan Li Yu Ao;Yunbo Li;Jiaye Gong;Shaofan Li

作者机构：College Of Shipbuilding EngineeringHarbin Engineering UniversityChina Department of Civil and Environmental EngineeringUniversity of CaliforniaBerkeleyCA 94720USA College of Ocean Science and EngineeringShanghai Maritime UniversityChina 

出 版 物：《Journal of Ocean Engineering and Science》 (海洋工程与科学（英文）)

年 卷 期：2023年第8卷第1期

页      面：15-32页

核心收录：

学科分类：08[工学] 0824[工学-船舶与海洋工程] 082401[工学-船舶与海洋结构物设计制造] 

基　　金：supported by a fellowship from China Scholar Council(No.201806680134) this support is greatly appreciated 

主　　题：Artificial intelligence Deep learning neural network Hull deformation Machine learning Ship hull design Total resistance 

摘      要：Ship-hull design is a complex process because the any slight local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a *** find the optimum hull shape under the design requirements,the state-of-art of ship hull design combines computational fluid dynamics computation with geometric ***,this process is very computationally intensive,which is only suitable at the final stage of the design *** narrow down the design parameter space,in this work,we have developed an AI-based deep learning neural network to realize a real-time prediction of the total resistance of the ship-hull structure in its initial design *** this work,we have demonstrated how to use the developed DNN model to carry out the initial ship hull *** validation results showed that the deep learning model could accurately predict the ship hull’s total resistance accurately after being trained,where the average error of all samples in the testing dataset is lower than 4%.Simultaneously,the trained deep learning model can predict the hip’s performances in real-time by inputting geometric modification parameters without tedious preprocessing and calculation *** machine learning approach in ship hull design proposed in this work is the first step towards the artificial intelligence-aided design in naval architectures.