Version 1
: Received: 23 October 2024 / Approved: 23 October 2024 / Online: 23 October 2024 (19:48:17 CEST)
How to cite:
Chu, Z.; Geng, J.; Yang, Q.; Yi, X.; Dong, W. Prediction of Temperature Distribution on Aircraft Hot-air Anti-icing Surface by ROM and Neural Networks. Preprints2024, 2024101860. https://doi.org/10.20944/preprints202410.1860.v1
Chu, Z.; Geng, J.; Yang, Q.; Yi, X.; Dong, W. Prediction of Temperature Distribution on Aircraft Hot-air Anti-icing Surface by ROM and Neural Networks. Preprints 2024, 2024101860. https://doi.org/10.20944/preprints202410.1860.v1
Chu, Z.; Geng, J.; Yang, Q.; Yi, X.; Dong, W. Prediction of Temperature Distribution on Aircraft Hot-air Anti-icing Surface by ROM and Neural Networks. Preprints2024, 2024101860. https://doi.org/10.20944/preprints202410.1860.v1
APA Style
Chu, Z., Geng, J., Yang, Q., Yi, X., & Dong, W. (2024). Prediction of Temperature Distribution on Aircraft Hot-air Anti-icing Surface by ROM and Neural Networks. Preprints. https://doi.org/10.20944/preprints202410.1860.v1
Chicago/Turabian Style
Chu, Z., Xian Yi and Wei Dong. 2024 "Prediction of Temperature Distribution on Aircraft Hot-air Anti-icing Surface by ROM and Neural Networks" Preprints. https://doi.org/10.20944/preprints202410.1860.v1
Abstract
This study aims to address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs by introducing reduced order models (ROM) and machine learning techniques to predict anti-icing surface temperature distributions. This study compares several classic neural networks, ultimately proposing two models: POD-AlexNet and multi-CNNs with GRU (MCG). Design variables of the hot-air anti-icing cavity are used as inputs, and the corresponding surface temperature distribution data serve as outputs. 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%. Both models significantly outperform traditional numerical simulation methods, delivering faster predictions. The proposed models achieve fast prediction of anti-icing surface temperature distribution while ensuring acceptable prediction accuracy. These models greatly enhance the prediction efficiency over existing numerical simulation approaches, contributing to the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.
Keywords
Hot-air anti-icing; Temperature distribution prediction; Machine learning; Neural network; Reduced order model
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
