Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results
Abstract Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-79913-8 |
| _version_ | 1818427584192446464 |
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| author | Meisam Babanezhad Iman Behroyan Azam Marjani Saeed Shirazian |
| author_facet | Meisam Babanezhad Iman Behroyan Azam Marjani Saeed Shirazian |
| author_sort | Meisam Babanezhad |
| collection | DOAJ |
| description | Abstract Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd’s variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS. |
| first_indexed | 2024-12-14T14:48:03Z |
| format | Article |
| id | doaj.art-39a2e879ba9145ed9a76b6040f4b4d79 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T14:48:03Z |
| publishDate | 2021-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-39a2e879ba9145ed9a76b6040f4b4d792022-12-21T22:57:13ZengNature PortfolioScientific Reports2045-23222021-01-0111111110.1038/s41598-020-79913-8Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD resultsMeisam Babanezhad0Iman Behroyan1Azam Marjani2Saeed Shirazian3Institute of Research and Development, Duy Tan UniversityFaculty of Mechanical and Energy Engineering, Shahid Beheshti UniversityDepartment for Management of Science and Technology Development, Ton Duc Thang UniversityLaboratory of Computational Modeling of Drugs, South Ural State UniversityAbstract Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd’s variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.https://doi.org/10.1038/s41598-020-79913-8 |
| spellingShingle | Meisam Babanezhad Iman Behroyan Azam Marjani Saeed Shirazian Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results Scientific Reports |
| title | Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results |
| title_full | Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results |
| title_fullStr | Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results |
| title_full_unstemmed | Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results |
| title_short | Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results |
| title_sort | velocity prediction of nanofluid in a heated porous pipe defis learning of cfd results |
| url | https://doi.org/10.1038/s41598-020-79913-8 |
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