Preliminary Study of Method and Experiment on Ignition Critical Conditions of Aero-Engine High Temperature Surface with Artificial Neural Network

To understand the ignition process of leaked combustible liquid from high temperature surface of an aeroengine cabin, this paper summarizes the research status of aeroengine ignition. The effects of different wind speeds and high-temperature surface temperatures on the ignition of leaked combustible liquid are analyzed under ventilation conditions. The ventilation speed and high temperature surface temperature are taken as the main factors. The flow field of combustible liquid is simulated by FLUENT software, and the engine working condition model is constructed. Combined with the artificial neural network, the correlation equations of ignition, ventilation speed and high temperature surface temperature are fitted, and the ignition solution method based on the neural network algorithm is established. The model is verified by small-scale ignition experiments in a high temperature wind tunnel. The model prediction and test results show that the prediction model can effectively predict the ignition critical conditions and probability of combustible liquid on aero-engine high temperature surface. This work provides a theoretical basis for further research on the mechanism and evolution of aero-engine misfire and engine-type forensics.