Towards a new paradigm in intelligence-driven computational fluid dynamics simulations
Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2407005 |
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| author | Xinhai Chen Zhichao Wang Liang Deng Junjun Yan Chunye Gong Bo Yang Qinglin Wang Qingyang Zhang Lihua Yang Yufei Pang Jie Liu |
| author_facet | Xinhai Chen Zhichao Wang Liang Deng Junjun Yan Chunye Gong Bo Yang Qinglin Wang Qingyang Zhang Lihua Yang Yufei Pang Jie Liu |
| author_sort | Xinhai Chen |
| collection | DOAJ |
| description | Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing complexity of computational tasks. To meet the burgeoning requirements of contemporary physical sciences, in recent years, the coupling of traditional scientific computing techniques with promising deep learning techniques well-known from computer science have emerged as a new research paradigm. This paradigm aims to create automated, intelligent tools for obtaining valuable insights as well as being able to categorize, predict, and make evidence-based decisions in novel ways. These tools can be used to reduce the reliance on expert experience and laborious computations inherent in existing numerical theories and methods. In this paper, we delve into the essence of science paradigms, the evolution of computing intelligence, and provide a comprehensive overview of the key applications driving the development of a new intelligence paradigm in CFD simulations. In addition, we outline a prototype platform for CFD simulations within this new paradigm. Based on this platform, three intelligent workflows are proposed, anticipating to serve as a reference source for future research and foster the emergence of innovative applications in the field of CFD.Highlights Deep learning techniques emerged as a new method to create automated, intelligent tools for CFD simulations.A review of deep learning methods for mesh pre-processing.A review of deep learning methods for numerical solving.A review of deep learning methods for post-processing visualization.A prototype platform for CFD simulations within the new paradigm.Perspectives on challenges and future directions. |
| format | Article |
| id | doaj-art-dfec77ee1f8249ac9ccef82f89e6b3d0 |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-dfec77ee1f8249ac9ccef82f89e6b3d02024-12-09T09:43:45ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2024-12-0118110.1080/19942060.2024.2407005Towards a new paradigm in intelligence-driven computational fluid dynamics simulationsXinhai Chen0Zhichao Wang1Liang Deng2Junjun Yan3Chunye Gong4Bo Yang5Qinglin Wang6Qingyang Zhang7Lihua Yang8Yufei Pang9Jie Liu10Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaChina Aerodynamics Research and Development Center, Mianyang, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaChina Aerodynamics Research and Development Center, Mianyang, People's Republic of ChinaScience and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, People's Republic of ChinaComputational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing complexity of computational tasks. To meet the burgeoning requirements of contemporary physical sciences, in recent years, the coupling of traditional scientific computing techniques with promising deep learning techniques well-known from computer science have emerged as a new research paradigm. This paradigm aims to create automated, intelligent tools for obtaining valuable insights as well as being able to categorize, predict, and make evidence-based decisions in novel ways. These tools can be used to reduce the reliance on expert experience and laborious computations inherent in existing numerical theories and methods. In this paper, we delve into the essence of science paradigms, the evolution of computing intelligence, and provide a comprehensive overview of the key applications driving the development of a new intelligence paradigm in CFD simulations. In addition, we outline a prototype platform for CFD simulations within this new paradigm. Based on this platform, three intelligent workflows are proposed, anticipating to serve as a reference source for future research and foster the emergence of innovative applications in the field of CFD.Highlights Deep learning techniques emerged as a new method to create automated, intelligent tools for CFD simulations.A review of deep learning methods for mesh pre-processing.A review of deep learning methods for numerical solving.A review of deep learning methods for post-processing visualization.A prototype platform for CFD simulations within the new paradigm.Perspectives on challenges and future directions.https://www.tandfonline.com/doi/10.1080/19942060.2024.2407005Computational fluid dynamicsdeep learningscience paradigmprototype platformintelligent workflow |
| spellingShingle | Xinhai Chen Zhichao Wang Liang Deng Junjun Yan Chunye Gong Bo Yang Qinglin Wang Qingyang Zhang Lihua Yang Yufei Pang Jie Liu Towards a new paradigm in intelligence-driven computational fluid dynamics simulations Engineering Applications of Computational Fluid Mechanics Computational fluid dynamics deep learning science paradigm prototype platform intelligent workflow |
| title | Towards a new paradigm in intelligence-driven computational fluid dynamics simulations |
| title_full | Towards a new paradigm in intelligence-driven computational fluid dynamics simulations |
| title_fullStr | Towards a new paradigm in intelligence-driven computational fluid dynamics simulations |
| title_full_unstemmed | Towards a new paradigm in intelligence-driven computational fluid dynamics simulations |
| title_short | Towards a new paradigm in intelligence-driven computational fluid dynamics simulations |
| title_sort | towards a new paradigm in intelligence driven computational fluid dynamics simulations |
| topic | Computational fluid dynamics deep learning science paradigm prototype platform intelligent workflow |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2407005 |
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