|Table of Contents|

 Hassan Saghi,Mohammad Reza Sarani Nezhad,Reza Saghi,et al.Comparison of Artificial Neural Networks and Genetic Algorithms for Predicting Liquid Sloshing Parameters[J].Journal of Marine Science and Application,2024,(2):292-301.[doi:10.1007/s11804-024-00413-6]
Click and Copy

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


Comparison of Artificial Neural Networks and Genetic Algorithms for Predicting Liquid Sloshing Parameters
Hassan Saghi1 Mohammad Reza Sarani Nezhad2 Reza Saghi3 Sepehr Partovi Sahneh4
Hassan Saghi1 Mohammad Reza Sarani Nezhad2 Reza Saghi3 Sepehr Partovi Sahneh4
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
Sloshing loads|Fluid structure interactions|Potential flow analysis|Artificial neural network|Genetic algorithm
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.


Ahn Y, Kim Y, Kim SY (2019) Database of model-scale sloshing experiment for LNG tank and application of artificial neural network for sloshing load prediction. Marine Structures 66:66-82. https://doi.org/10.1016/j.marstruc.2019.03.005
Cetin EC, Lee J, Kim S, Kim Y (2018) Prediction of extreme sloshing pressure using different statistical models. Journal of Advanced Research in Ocean Engineering 4(4):185-194
Chen J, Lin Y, Zhou HJ, Xia ZM, Zhuo SJ (2010) Optimization of ship’s subdivision arrangement for offshore sequential ballast water exchange using a non-dominated sorting genetic algorithm. Ocean Engineering 37:978-988. https://doi.org/10.1016/j.oceaneng.2010.03.012
Chen X, Diez M, Kandashamy M, Zhang Z, Campana E, Stern F (2014) High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm. Engineering Optimization; Taylor & Francis:Abingdon, UK 473-494. https://doi.org/10.1080/0305215X.2014.895340
Forrest S (1993) Genetic Algorithms:principles of natural selection applied to computation. Science 261
Gran S (1981) Statistical distributions of local impact pressures. Norweg Marit Res 8(2):2-13
Graupe D (2007) Principles of Artificial Neural Networks. World Scientific Publisher, Advanced Series on Circuits and Systems 6. https://doi.org/10.1142/8868
Grazcyk M, Moan T (2008) A probabilistic assessment of design sloshing pressure time histories in LNG tanks. Ocean Engineering 35:834-855. https://doi.org/10.1016/j.oceaneng.2008.01.020
Harries S, Abt C (2019) CAESES-The HOLISHIP platform for process integration and design optimization. A Holistic Approach to Ship Design; Springer:Berlin/Heidelberg, Germany 276-291
Jin Y, Liu X, Qiu W, Hou C (2008) Time-varying sliding mode controls in rigid spacecraft attitude tracking. Chinese Journal of Aeronautics 21:352-360. https://doi.org/10.1016/S1000-9361(08) 60046-1
Ketabdari MJ, Saghi H (2012) Numerical analysis of trapezoidal storage tank due to liquid sloshing phenomenon. Iranian Journal of Marine Science and Technology 18(61):33-39
Ketabdari MJ, Saghi H (2013a) Parametric study for optimization of storage tanks considering sloshing phenomenon using coupled BEM-FEM. Applied Mathematics and Computation 224:123-139. https://doi.org/10.1016/j.amc.2013.08.036
Ketabdari MJ, Saghi H (2013b) Numerical study on behavior of the trapezoidal storage tank due to liquid sloshing impact. International journal of Computational Methods 10(6):1-22. https://doi.org/10.1142/S0219876213500461
Kuzniatsova M, Shimanovsky A (2016) Definition of rational form of lateral perforated baffle for road tanks. Procedia Engineering 134:72-79. https://doi.org/10.1016/j.proeng.2016.01.041
Li HT, Jing L, Zong Z, Chen Z (2014) Numerical studies on sloshing in rectangular tanks using a tree-based adaptive solver and experimental validation. Ocean Engineering 82:20-31. https://doi.org/10.1016/j.oceaneng.2014.02.011
Mizumura K (1984) Application of Kalman filtering to ocean data. Journal of Waterway, Port, Coastal. And Ocean. Engineering, ASCE l10(3):334-343. https://doi.org/10.1061/(ASCE)0733-950X (1984)110:3(334)
Nakayama T, Washizu K (1984) Boundary element analysis of nonlinear sloshing problems. Published in Developments in Boundary Element Method-3, Bauerjee PK, Mukherjee S, Elsevier Applied Science Publishers, New York
Núñez J, Cruchaga M, Tampier G (2022) Wave analysis based on genetic algorithms using data collected from laboratories at different scales. European Journal of Mechanics-B/Fluids 95:231-239. https://doi.org/10.1016/j.euromechflu.2022.05.006
Saghi H (2016) The pressure distribution on the rectangular and trapezoidal storage tanks’ perimeters due to liquid sloshing phenomenon. International Journal of Naval Architecture and Ocean Engineering 8(12):153-168. https://doi.org/10.1016/j.ijnaoe.2015.12.001
Saghi H, Lakzian E (2017) Optimization of the rectangular storage tanks for the sloshing phenomena based on the entropy generation minimization. Energy 128:564-574. https://doi.org/10.1016/j.energy.2017.04.075
Saghi H, Mikkola T, Hirdaris S (2021) The influence of obliquely perforated dual baffles on sway induced tank sloshing dynamics. Proceedings of the institution of Mechanical Engineerings, Part M:Journal of Engineering for the Maritime Environment 235(4):905-920. https://doi.org/10.1177/1475090220961920
Saltari F, Pizzoli M, Gambioli F, Jetzschmann C, Mastroddi F (2022) Sloshing reduced-order model based on neural networks for aeroelastic analyses. Aerospace Science and Technology 127:107708. https://doi.org/10.1016/j.ast.2022.107708
Sclavounos P, Yu M (2018) Artificial Intelligence machine Learning in marine Hydrodynamics. Proceedings of the International Conference on Ocean, Offshore and Arctic Engineering, Madrid, Spain Talebitooti R, Shojaeefard MH, Yarmohammadisatri S (2015) Shape design optimization of cylindrical tank using b-spline curves. Computers & Fluids 109:100-112. https://doi.org/10.1016/j.compfluid.2014.12.004
Volpi S, Gaul N, Diez M, Song H, Iema U, Campana E, Choi K, Stern F (2015) Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification. Struct. Multidiscip Optim 51:347-368
Wu CH, Faltinsen OM, Chen BF (2012) Numerical study of sloshing liquid in tanks with baffles by time-independent finite difference and fictitious cell method. Computers & Fluids 63:9-26. https://doi.org/10.1016/j.compfluid.2012.02.018
Yen PH, Jan CD, Lee YP, Lee HF (1991) Application of Kalman filter to short-term tide level prediction. Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE 122(5):226-231
Zhang C (2015) Application of an improved semi-Lagrangian procedure to fully nonlinear simulation of sloshing in non-wallsided tanks. Applied Ocean Research 51:74-92. https://doi.org/10.1016/j.apor.2015.03.001
Zhao Y, Chen HC (2015) Numerical simulation of 3D sloshing flow in partially filled LNG tank using a coupled level-set and volumeof-fluid method. Ocean Engineering 104:10-30. https://doi.org/10.1016/j.oceaneng.2015.04.083


Received date: 2023-04-26;Accepted date: 2023-12-01。
Corresponding author: Hassan Saghi,E-mail:hasansaghi1975@gmail.com
Last Update: 2024-05-28