中国舰船研究2024，Vol.19Issue(6)：74-81,8.DOI:10.19693/j.issn.1673-3185.04062
基于多保真深度神经网络的船型优化
Hull form optimization based on multi-fidelity deep neural network
摘要
Abstract
[Objective]To improve hull optimization design efficiency and obtain better optimization res-ults,different fidelity data is organically integrated and a multi-fidelity deep neural network is applied.[Methods]A multi-fidelity deep neural network is constructed based on the idea of multi-source data fu-sion and transfer learning.By fusing a large amount of low-fidelity data with a small amount of high-fidelity data,the linear and nonlinear terms between the high-fidelity data are constructed to obtain a high-fidelity sur-rogate model.Based on this method,the optimization design of the resistance of a DTMB 5415 ship is carried out.The potential flow and viscous flow are used to evaluate the resistance of the sample points respectively.The potential flow calculation results are used as low-fidelity data,while the viscous flow calculation results are used as high-fidelity data.A multi-fidelity deep neural network surrogate model is then constructed.The optimal solution is obtained by genetic algorithm and compared with the optimal solution of the Kriging mod-el constructed by high-fidelity data.[Results]Based on the multi-fidelity deep neural network method,the resistance of DTMB 5415 is reduced by 6.73%.Based on the Kriging model,the resistance of DTMB 5415 is reduced by 5.59%.[Conclusions]The multi-fidelity deep neural network surrogate model can take into ac-count both efficiency and accuracy,which can be used for optimization.The optimized hull form obtained by it has a more significant resistance optimization effect.关键词
船舶设计/人工智能/减阻/船型优化/多保真深度神经网络/数据融合/迁移学习Key words
naval architecture/artificial intelligence/drag reduction/hull form optimization/multi-fidelity deep neural network/data fusion/transfer learning分类
交通工程引用本文复制引用
魏亚博,汪杨骏,万德成..基于多保真深度神经网络的船型优化[J].中国舰船研究,2024,19(6):74-81,8.基金项目
国家自然科学基金资助项目(52131102) （52131102）
