CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets
At present, there are increasing applications for rosette diffusers for buoyant jets with a lower density than the ambient water, mainly in the discharge of wastewater from municipal administrations and sea water desalination. It is important to study the mixing effects of wastewater discharge for t...
| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2022-09-01
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| coleção: | Journal of Marine Science and Engineering |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2077-1312/10/10/1383 |
| _version_ | 1827649684468924416 |
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| author | Xiaohui Yan Yan Wang Abdolmajid Mohammadian Jianwei Liu Xiaoqiang Chen |
| author_facet | Xiaohui Yan Yan Wang Abdolmajid Mohammadian Jianwei Liu Xiaoqiang Chen |
| author_sort | Xiaohui Yan |
| collection | DOAJ |
| description | At present, there are increasing applications for rosette diffusers for buoyant jets with a lower density than the ambient water, mainly in the discharge of wastewater from municipal administrations and sea water desalination. It is important to study the mixing effects of wastewater discharge for the benefit of environmental protection, but because the multiport discharge of the wastewater concentration field is greatly affected by the mixing and interacting functions of wastewater, the traditional research methods on single-port discharge are invalid. This study takes the rosette multiport jet as a research subject to develop a new technology of computational fluid dynamics (CFD) modeling and carry out convolutional neural network (CNN) simulation of the concentration field of a multiport buoyant jet. This study takes advantage of CFD technology to simulate the mixing process of a rosette multiport buoyant jet, uses CNNs to construct the machine learning model, and applies RSME, R<sup>2</sup> to conduct evaluations of the models. This work also makes comparisons with the machine learning approach based on multi-gene genetic programming, to assess the performance of the proposed approach. The experimental results show that the models constructed based on the proposed approach meet the accuracy requirement and possess better performance compared with the traditional machine learning method, and they can provide reasonable predictions. |
| first_indexed | 2024-03-09T20:02:07Z |
| format | Article |
| id | doaj.art-9ceaf614a07a4ba2b7c564c698f05658 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T20:02:07Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-9ceaf614a07a4ba2b7c564c698f056582023-11-24T00:43:24ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-09-011010138310.3390/jmse10101383CFD-CNN Modeling of the Concentration Field of Multiport Buoyant JetsXiaohui Yan0Yan Wang1Abdolmajid Mohammadian2Jianwei Liu3Xiaoqiang Chen4School of Water Resources Engineering, Dalian University of Technology, 2 Linggong Road, Dalian 116024, ChinaSchool of Water Resources Engineering, Dalian University of Technology, 2 Linggong Road, Dalian 116024, ChinaDepartment of Civil Engineering, University of Ottawa, Ottawa, ON 999040, CanadaSchool of Water Resources Engineering, Dalian University of Technology, 2 Linggong Road, Dalian 116024, ChinaSchool of Water Resources Engineering, Dalian University of Technology, 2 Linggong Road, Dalian 116024, ChinaAt present, there are increasing applications for rosette diffusers for buoyant jets with a lower density than the ambient water, mainly in the discharge of wastewater from municipal administrations and sea water desalination. It is important to study the mixing effects of wastewater discharge for the benefit of environmental protection, but because the multiport discharge of the wastewater concentration field is greatly affected by the mixing and interacting functions of wastewater, the traditional research methods on single-port discharge are invalid. This study takes the rosette multiport jet as a research subject to develop a new technology of computational fluid dynamics (CFD) modeling and carry out convolutional neural network (CNN) simulation of the concentration field of a multiport buoyant jet. This study takes advantage of CFD technology to simulate the mixing process of a rosette multiport buoyant jet, uses CNNs to construct the machine learning model, and applies RSME, R<sup>2</sup> to conduct evaluations of the models. This work also makes comparisons with the machine learning approach based on multi-gene genetic programming, to assess the performance of the proposed approach. The experimental results show that the models constructed based on the proposed approach meet the accuracy requirement and possess better performance compared with the traditional machine learning method, and they can provide reasonable predictions.https://www.mdpi.com/2077-1312/10/10/1383numerical simulationconvolutional neural networkrosette-type diffusersbuoyant discharges |
| spellingShingle | Xiaohui Yan Yan Wang Abdolmajid Mohammadian Jianwei Liu Xiaoqiang Chen CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets Journal of Marine Science and Engineering numerical simulation convolutional neural network rosette-type diffusers buoyant discharges |
| title | CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets |
| title_full | CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets |
| title_fullStr | CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets |
| title_full_unstemmed | CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets |
| title_short | CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets |
| title_sort | cfd cnn modeling of the concentration field of multiport buoyant jets |
| topic | numerical simulation convolutional neural network rosette-type diffusers buoyant discharges |
| url | https://www.mdpi.com/2077-1312/10/10/1383 |
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