TY - GEN
T1 - Three-Dimensional Temperature Field Prediction in Double-Wall Cooling Structure Using Deep Learning Method
AU - Huang, Junjie
AU - Zhu, Jianqin
AU - Wang, Yanjia
AU - Cheng, Zeyuan
AU - Qiu, Lu
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - The double-walled cooling structure can achieve high cooling efficiency but has a higher risk of failure compared to traditional structures. In order to optimize the structural variables of the structure, a fast and accurate temperature field prediction method is urgently proposed This study establishes a deep learning model using MLPs and SRCNN modules to predict the 3D temperature field of the outer wall of a double-walled cooling structure (DWCS) unit. The model takes in geometric structure variables and working condition variables as inputs. To train the model, a temperature field dataset is generated by CFD numerical simulation. The results demonstrate that the deep learning method can accurately predict the 3D temperature field of the DWCS unit at multiple scales, and the model training can be convergent with the proper design of the model architecture and training strategies. Compared to numerical simulation, the deep learning model can predict the temperature field quickly and be combined with machine learning optimization algorithms for the optimization of DWCS variables.
AB - The double-walled cooling structure can achieve high cooling efficiency but has a higher risk of failure compared to traditional structures. In order to optimize the structural variables of the structure, a fast and accurate temperature field prediction method is urgently proposed This study establishes a deep learning model using MLPs and SRCNN modules to predict the 3D temperature field of the outer wall of a double-walled cooling structure (DWCS) unit. The model takes in geometric structure variables and working condition variables as inputs. To train the model, a temperature field dataset is generated by CFD numerical simulation. The results demonstrate that the deep learning method can accurately predict the 3D temperature field of the DWCS unit at multiple scales, and the model training can be convergent with the proper design of the model architecture and training strategies. Compared to numerical simulation, the deep learning model can predict the temperature field quickly and be combined with machine learning optimization algorithms for the optimization of DWCS variables.
KW - 3D temperature field prediction
KW - Deep learning model
KW - Double-walled cooling structure
UR - https://www.scopus.com/pages/publications/85180625309
U2 - 10.1007/978-3-031-42987-3_2
DO - 10.1007/978-3-031-42987-3_2
M3 - 会议稿件
AN - SCOPUS:85180625309
SN - 9783031429866
T3 - Mechanisms and Machine Science
SP - 11
EP - 23
BT - Computational and Experimental Simulations in Engineering - Proceedings of ICCES 2023—Volume 2
A2 - Li, Shaofan
PB - Springer Science and Business Media B.V.
T2 - 29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023
Y2 - 26 May 2023 through 29 May 2023
ER -