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
魏亚博 1汪杨骏 2万德成1
作者信息
- 1. 上海交通大学 船海计算水动力学研究中心,上海 200240;上海交通大学 船舶海洋与建筑工程学院,上海 200240
- 2. 国防科技大学 前沿交叉学科学院,江苏 南京 210000
- 折叠
摘要
[目的]为了提高优化效率并获得更好的优化结果,将不同精度数据进行有机融合,利用多保真深度神经网络开展船型优化设计.[方法]基于多源数据融合和迁移学习思想,构建了一种多保真深度神经网络.通过将大量低保真数据与少量高保真数据融合学习,构建与高保真数据之间的线性项和非线性项,得到高保真近似模型.基于此方法开展针对DTMB 5415 船静水阻力的优化设计.分别采用势流和黏流样本点阻力进行评估,势流计算结果作为低保真数据,黏流计算结果作为高保真数据,构建多保真深度神经网络近似模型.借助遗传算法获得优化解并与只使用单一高保真数据构建的Kriging近似模型的优化结果进行对比.[结果]基于多保真神经网络方法,DTMB 5415 阻力减少了 6.73%.基于Kriging模型,DTMB 5415 阻力减少了 5.59%.[结论]多保真深度神经网络近似模型可以兼顾效率和精度,可以用于优化求解,且由其得到的优化船型阻力优化效果更为显著.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据