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 Mohammad Kazem Tahmasebi,Rahim Shamsoddini,Bahador Abolpour.Performances of Different Turbulence Models for Simulating Shallow Water Sloshing in Rectangular Tank[J].Journal of Marine Science and Application,2020,(3):381-387.[doi:10.1007/s11804-020-00162-2]
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Performances of Different Turbulence Models for Simulating Shallow Water Sloshing in Rectangular Tank


Performances of Different Turbulence Models for Simulating Shallow Water Sloshing in Rectangular Tank
Mohammad Kazem Tahmasebi1 Rahim Shamsoddini1 Bahador Abolpour2
Mohammad Kazem Tahmasebi1 Rahim Shamsoddini1 Bahador Abolpour2
1 Department of Mechanical Engineering, Sirjan University of Technology, Sirjan 7813733385, Iran;
2 Department of Chemical Engineering, Sirjan University of Technology, Sirjan 7813733385, Iran
Volume of fluidTurbulence modelsShallow water sloshingFree surfaceOpenFOAMLiquid tanksRenormalization group
Liquid sloshing is a common phenomenon in the transportation of liquid-cargo tanks. Liquid waves lead to fluctuating forces on the tank walls. If these fluctuations are not predicted or controlled, for example, by using baffles, they can lead to large forces and momentums. The volume of fluid (VOF) two-phase numerical model in OpenFOAM open-source software has been widely used to model the liquid sloshing. However, a big challenge for modeling the sloshing phenomenon is selecting a suitable turbulence model. Therefore, in the present study, different turbulence models were studied to determine their sloshing phenomenon prediction accuracies. The predictions of these models were validated using experimental data. The turbulence models were ranked by their mean error in predicting the free surface behaviors. The renormalization group (RNG) k-ε and the standard k-ω models were found to be the best and worst turbulence models for modeling the sloshing phenomena, respectively; moreover, the SST k-ω model and v2-f k-ε results were very close to the RNG k-ε model result.


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Received date:2019-06-12;Accepted date:2020-05-01。
Foundation item:Supported by the National Natural Science Foundation of China under Grant Nos. 51709060 and 51609030
Corresponding author:Rahim Shamsoddini,Shamsoddini@sirjantech.ac.ir
Last Update: 2020-11-21