Direct numerical simulation of natural convection based on parameter-input physics-informed neural networks
文献类型：期刊论文
| 作者 | Ye,Shuran; Huang JL(黄剑霖); Zhang, Zhen; Wang YW(王一伟) ; Huang CG(黄晨光)
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| 刊名 | INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
 |
| 出版日期 | 2025-01 |
| 卷号 | 236页码:126379 |
| 关键词 | Natural convection Physics-informed neural networks Parameter-input PINNs Ra number Deep learning |
| ISSN号 | 0017-9310 |
| DOI | 10.1016/j.ijheatmasstransfer.2024.126379 |
| 英文摘要 | Thermal convection is frequently observed in nature and widely used in industry, making it an important subject for many experimental and numerical studies. A well-researched paradigm for comprehending thermal convection is the system of thermally driven square cavities, one of the classical problems of natural convection. With the development of computational resources, methods for solving natural convection problems using deep learning techniques have flourished. In this study, a Physics-informed neural networks (PINNs) method is used to solve the thermal convection problem, with neural networks trained to simulate the velocity and temperature fields of natural convection at various Ra numbers ranging from Ra = 103 to Ra = 108. Furthermore, a parameter-input PINNs model is constructed to further develop this approach. This framework has the advantage of concurrently and rapidly predicting the flow field outcomes for any Ra number scenario in the specified range. Additionally, the flow field outcomes of the parameter-input PINNs model are statistically analyzed to demonstrate the model's generalization performance. |
| 分类号 | 一类 |
| WOS研究方向 | Thermodynamics ; Engineering ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001352998800001 |
| 资助机构 | National Natural Science Foundation of China (NSFC) {12302514, 12202291] |
| 其他责任者 | Wang YW |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/97168]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.【Chenguang, Huang】 Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.【Zhen, Zhang】 Shijiazhuang Tiedao Univ, Sch Civil Engn, Shijiazhuang 050043, Peoples R China 3.【Jianlin, Huang & Yiwei, Wang & Chenguang, Huang】 Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing 100190, Peoples R China 4.【Shuran, Ye】 Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Key Lab Atmospher Opt, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ye,Shuran,Huang JL,Zhang, Zhen,et al. Direct numerical simulation of natural convection based on parameter-input physics-informed neural networks[J]. INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER,2025,236:126379. |
| APA | Ye,Shuran,黄剑霖,Zhang, Zhen,王一伟,&黄晨光.(2025).Direct numerical simulation of natural convection based on parameter-input physics-informed neural networks.INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER,236,126379. |
| MLA | Ye,Shuran,et al."Direct numerical simulation of natural convection based on parameter-input physics-informed neural networks".INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 236(2025):126379. |
入库方式： OAI收割
来源：力学研究所
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