Detection and Identification of Instability and Blow-off/Flashback Precursors in Aeronautical Engines using Deep Learning techniques

Abstract: The evolution of injection processes toward more fuel efficient and less polluting combustion systems tend to make them more prone to critical events such as Thermo-Acoustic Instabilities, Blow-Off and Flash-Back. Moreover, the addition of Di-Hydrogen as a secondary or as the main fuel is in discussion by aeronautical engines manufacturers. It drastically modifies the stability of the system and thus raise several interrogations concerning the multiplicity of its use. Being able to predict critical phenomena becomes a necessity in order to efficiently operate a system without having to pre-test every configuration and without sacrificing the safety of the user. Based on Deep Learning techniques and more specifically Speech Recognition, the following study presents the steps to develop a tool able to successfully detect and translate precursors of instability of an aeronautical grade swirled injector confined in a tubular combustion chamber. The promising results obtained lead to proposals for future transpositions to real-size systems.