Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks
To address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposit...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/11/930 |
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| author | Ziying Chu Ji Geng Qian Yang Xian Yi Wei Dong |
| author_facet | Ziying Chu Ji Geng Qian Yang Xian Yi Wei Dong |
| author_sort | Ziying Chu |
| collection | DOAJ |
| description | To address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. Design variables of the hot-air anti-icing cavity are used as inputs of the two models, and the corresponding surface temperature distribution data serve as outputs, and then the performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. The proposed models not only provide faster predictions of anti-icing surface temperature distributions than traditional numerical simulation methods but also ensure acceptable accuracy, which supports the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms. |
| format | Article |
| id | doaj-art-38fbd3db97e845eeb5db844cbf28289c |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-38fbd3db97e845eeb5db844cbf28289c2025-08-20T01:53:48ZengMDPI AGAerospace2226-43102024-11-01111193010.3390/aerospace11110930Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural NetworksZiying Chu0Ji Geng1Qian Yang2Xian Yi3Wei Dong4School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaState Key Laboratory of Aerodynamics, Mianyang 621000, ChinaState Key Laboratory of Aerodynamics, Mianyang 621000, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaTo address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. Design variables of the hot-air anti-icing cavity are used as inputs of the two models, and the corresponding surface temperature distribution data serve as outputs, and then the performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. The proposed models not only provide faster predictions of anti-icing surface temperature distributions than traditional numerical simulation methods but also ensure acceptable accuracy, which supports the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.https://www.mdpi.com/2226-4310/11/11/930hot-air anti-icingtemperature distribution predictionmachine learningneural networksreduced-order model |
| spellingShingle | Ziying Chu Ji Geng Qian Yang Xian Yi Wei Dong Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks Aerospace hot-air anti-icing temperature distribution prediction machine learning neural networks reduced-order model |
| title | Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks |
| title_full | Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks |
| title_fullStr | Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks |
| title_full_unstemmed | Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks |
| title_short | Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks |
| title_sort | prediction of temperature distribution on an aircraft hot air anti icing surface by rom and neural networks |
| topic | hot-air anti-icing temperature distribution prediction machine learning neural networks reduced-order model |
| url | https://www.mdpi.com/2226-4310/11/11/930 |
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