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Simulation of liquid flow with a combination artificial intelligence flow field and Adams–Bashforth method

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dc.contributor.author Babanezhad, Meisam
dc.contributor.author Behroyan, Iman
dc.contributor.author Nakhjiri, Ali Taghvaie
dc.contributor.author Marjani, Azam
dc.contributor.author Shirazian, Saeed
dc.date.accessioned 2020-10-12T09:09:48Z
dc.date.available 2020-10-12T09:09:48Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/10344/9322
dc.description peer-reviewed en_US
dc.description.abstract Direct numerical simulation (DNS) of particle hydrodynamics in the multiphase industrial process enables us to fully learn the process and optimize it on the industrial scale. However, using high resolution computational calculations for particle movement and the interaction between the solid phase and other phases in fine timestep is limited to excellent computational resources. Solving the Eulerian flow field as a source of solid particle movement can be very time-consuming. However, by the revolution of the fast and accurate learning process, the Eulerian domain can be computed by smart modeling in a very short computational time. In this work, using the machine learning method, the flow field in the square shape cavity is trained, and then the Eulerian framework is replaced with a machine learning method to generate the artificial intelligence (AI) flow field. Then the Lagrangian framework is coupled with this AI flow field, and we simulate particle motion through the fully AI framework. The Adams–Bashforth finite element method is used as a conventional CFD method (Eulerian framework) to simulate the flow field in the cavity. After simulating fluid flow, the ANFIS method is used as an AI model to train the Eulerian data-set and represents AI fluid flow (framework). The Lagrangian framework is coupled with the AI method, and the particle freely migrates through this artificial framework. The results reveal that there is a great agreement between Euler-Lagrangian and AI- Lagrangian in the cavity. We also found that there is an excellent agreement between AI overview with the Adams–Bashforth approach, and the new combination of machine learning and CFD method can accelerate the calculation of the flow field in the is a zero-velocity structure in the center of the domain and maximum velocity near the moving walls. en_US
dc.language.iso eng en_US
dc.publisher Nature en_US
dc.relation.ispartofseries Scientific Reports;10,16719
dc.subject hydrodynamics en_US
dc.subject Eulerian flow en_US
dc.title Simulation of liquid flow with a combination artificial intelligence flow field and Adams–Bashforth method en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1038/s41598-020-72602-6
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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