哈尔滨工程大学学报(英文版)2024,Vol.23Issue(2) :292-301.DOI:10.1007/s11804-024-00413-6

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

Hassan Saghi Mohammad Reza Sarani Nezhad Reza Saghi Sepehr Partovi Sahneh
哈尔滨工程大学学报(英文版)2024,Vol.23Issue(2) :292-301.DOI:10.1007/s11804-024-00413-6

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

Hassan Saghi 1Mohammad Reza Sarani Nezhad 2Reza Saghi 3Sepehr Partovi Sahneh4
扫码查看

作者信息

  • 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
  • 折叠

Abstract

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 ηmax and Fmax.

Key words

Sloshing loads/Fluid structure interactions/Potential flow analysis/Artificial neural network/Genetic algorithm

引用本文复制引用

出版年

2024
哈尔滨工程大学学报(英文版)
哈尔滨工程大学

哈尔滨工程大学学报(英文版)

CSTPCD
影响因子:0.381
ISSN:1671-9433
参考文献量29
段落导航相关论文