Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning
Accurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/17/2/357 |
| _version_ | 1797343132639035392 |
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| author | Hongze Du Qi Xu Lizhe Jiang Yufeng Bu Wenbo Li Jun Yan |
| author_facet | Hongze Du Qi Xu Lizhe Jiang Yufeng Bu Wenbo Li Jun Yan |
| author_sort | Hongze Du |
| collection | DOAJ |
| description | Accurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall thermal load of a box structure. To determine the location and magnitude of the thermal load accurately, the proposed method segments a structure into several subregions and applies a cascade of deep learning models to gradually reduce the solution domain. The generalization ability of the model is significantly enhanced by the inclusion of boundary conditions in the deep learning models. In this study, a large simulated dataset was generated by varying the load application position and intensity for each sample. The input variables encompass a small set of structural displacements, while the outputs include parameters related to the thermal load, such as the position and magnitude of the load. Ablation experiments are conducted to validate the effectiveness of this approach. The results show that this method reduces the identification error of the thermal load parameters by more than 45% compared with a single deep learning network. The proposed method holds promise for optimizing the design and analysis of spacecraft structures, contributing to improved performance and reliability in future space missions. |
| first_indexed | 2024-03-08T10:43:15Z |
| format | Article |
| id | doaj.art-65982e4248ed4af88af471b10d322cea |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-08T10:43:15Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-65982e4248ed4af88af471b10d322cea2024-01-26T17:26:55ZengMDPI AGMaterials1996-19442024-01-0117235710.3390/ma17020357Stepwise Identification Method of Thermal Load for Box Structure Based on Deep LearningHongze Du0Qi Xu1Lizhe Jiang2Yufeng Bu3Wenbo Li4Jun Yan5State Key Laboratory of Structural Analysis for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, ChinaNingbo Research Institute, Dalian University of Technology, Ningbo 315016, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, ChinaAccurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall thermal load of a box structure. To determine the location and magnitude of the thermal load accurately, the proposed method segments a structure into several subregions and applies a cascade of deep learning models to gradually reduce the solution domain. The generalization ability of the model is significantly enhanced by the inclusion of boundary conditions in the deep learning models. In this study, a large simulated dataset was generated by varying the load application position and intensity for each sample. The input variables encompass a small set of structural displacements, while the outputs include parameters related to the thermal load, such as the position and magnitude of the load. Ablation experiments are conducted to validate the effectiveness of this approach. The results show that this method reduces the identification error of the thermal load parameters by more than 45% compared with a single deep learning network. The proposed method holds promise for optimizing the design and analysis of spacecraft structures, contributing to improved performance and reliability in future space missions.https://www.mdpi.com/1996-1944/17/2/357thermal load identificationstepwise identification methoddeep learningboundary condition encoding |
| spellingShingle | Hongze Du Qi Xu Lizhe Jiang Yufeng Bu Wenbo Li Jun Yan Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning Materials thermal load identification stepwise identification method deep learning boundary condition encoding |
| title | Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning |
| title_full | Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning |
| title_fullStr | Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning |
| title_full_unstemmed | Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning |
| title_short | Stepwise Identification Method of Thermal Load for Box Structure Based on Deep Learning |
| title_sort | stepwise identification method of thermal load for box structure based on deep learning |
| topic | thermal load identification stepwise identification method deep learning boundary condition encoding |
| url | https://www.mdpi.com/1996-1944/17/2/357 |
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