人工神经网络与遗传算法预测液体晃荡参数的比较  

Comparison of Artificial Neural Networks and Genetic Algorithms for Predicting Liquid Sloshing Parameters

作　　者：Hassan Saghi Mohammad Reza Sarani Nezhad Reza Saghi Sepehr Partovi Sahneh 

机构地区：[1]Department of Civil Engineering,Hakim Sabzevari University,Sabzevar,Iran [2]Department of Electrical and Computer Engineering,University of Birjand,Birjand,Iran [3]State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian,116024,China [4]Department of Marine Engineering,Amirkabir University of Technology,Tehran,Iran

出　　处：《哈尔滨工程大学学报（英文版）》2024年第2期292-301,共10页Journal of Marine Science and Application

摘　　要：This paper develops a numerical code for modelling liquid sloshing.The coupled boundary element-finite element method was used to solve the Laplace equation for inviscid fluid and nonlinear free surface boundary conditions.Using Nakayama and Washizu’s results,the code performance was validated.Using the developed numerical mode,we proposed artificial neural network(ANN)and genetic algorithm(GA)methods for evaluating sloshing loads and comparing them.To compare the efficiency of the suggested methods,the maximum free surface displacement and the maximum horizontal force exerted on a rectangular tank’s perimeter are examined.It can be seen from the results that both ANNs and GAs can accurately predict η_(max) and F_(max).

关 键 词：Sloshing loads Fluid structure interactions Potential flow analysis Artificial neural network Genetic algorithm 

分 类 号：U661[交通运输工程—船舶及航道工程]