A FENN-TL Approach for Reliability Analysis of a Primary Ice Detection System
Publication: Journal of Aerospace Engineering
Volume 36, Issue 6
Abstract
Solving the reliability problem of primary ice detection systems is of great significance to support the design of anti-icing systems. In this paper, an efficient method employing a feature-enhanced neural network (FENN)–transfer learning (TL) surrogate model was developed to process two types of features (flight and aircraft parameters). A FENN was established with an autoencoder, and TL was implemented with 15 new points. A new loss function was designed and combined with FENN to control the direction of prediction error. The determination coefficient was 0.993 in the holding state and 0.997 in the local area near the dangerous state. Based on 1 million predicted results of Common Research Model (CRM) airfoil, the primary ice detection system is most likely to have reliability problems at a low angle of attack and low-speed flight state, and angle of attack has the greatest influence. FENN-TL proved a flexible and efficient method for reliability analysis of primary ice detection systems. This method and the obtained CRM results can be further used to support the design and airworthiness certification of large aircraft.
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Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We thank the Center for High Performance Computing of SJTU for providing the supercomputer to support this research. This work is financially supported by the National Key R&D Program of China, Project No. 2020YFA0712000.
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Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 36 • Issue 6 • November 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Apr 7, 2022
Accepted: Nov 23, 2022
Published online: Jul 31, 2023
Published in print: Nov 1, 2023
Discussion open until: Dec 31, 2023
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