Accurate estimation of scramjet inlet parameters (including Mach number and air mass flow rate) is essential for hypersonic flight control. These critical scramjet inlet parameters could be obtained by estimating the air data parameters through the inertial navigation system, but they have large errors. The flush air data sensing system is mainly used for post-flight analysis. This paper proposes an estimation method for scramjet inlet parameters based on deep learning. Accurate estimations of air data are not needed. Instead, the measurements of the transducers on the inlet wall are directly used as the input of the artificial neural network, and then the scramjet inlet parameter (Mach number or air mass flow rate) is output. The results show that the estimation accuracy of the scramjet inlet parameters has been greatly improved. This work provides a new idea for the estimation of the scramjet inlet parameters.
An Estimation Method for Scramjet Inlet Mach Number and Mass Flow Rate Based on Deep Learning
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 24 ; 225-238
2022-03-18
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Mach 4 testing of scramjet inlet models
AIAA | 1991
|Mach 4 testing of scramjet inlet models
AIAA | 1989
|Predictor–Corrector Method for Scramjet Inlet Air Mass Flow Rate Measurement
Online Contents | 2017
|Predictor–Corrector Method for Scramjet Inlet Air Mass Flow Rate Measurement
Online Contents | 2017
|