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

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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.

Sloshing loadsFluid structure interactionsPotential flow analysisArtificial neural networkGenetic algorithm

Hassan Saghi、Mohammad Reza Sarani Nezhad、Reza Saghi、Sepehr Partovi Sahneh

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Department of Civil Engineering,Hakim Sabzevari University,Sabzevar,Iran

Department of Electrical and Computer Engineering,University of Birjand,Birjand,Iran

State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian,116024,China

Department of Marine Engineering,Amirkabir University of Technology,Tehran,Iran

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2024

10.1007/s11804-024-00413-6

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
年,卷(期)：2024.23(2)
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