Data-driven propagation prediction of subsonic and supersonic turbulent jets by combining self-similarity analysis model and artificial neural network

Abstract

Previous self-similarity analysis models for turbulent jet flames (TJF) have inherent limitations in the flame tip location and velocity prediction based on experimental data. A novel model (BP-TJF) to predict the propagation process of subsonic and supersonic TJF is proposed by combining developed self-similarity analysis modeling and back propagation neural network (BPNN). The BP-TJF model is trained from three datasets of initial temperature, initial pressure, and oxygen content. The results show that the pressure difference prediction error was only 0.46 % for subsonic jets and 5 % for supersonic jets. The overall correlation coefficients (R) and mean squared errors (MSE) range from 0.95–0.97 and 0.01–0.1, respectively. The model optimized by genetic algorithm (GA) significantly improved the prediction stability. However, there is scope for improvement in the overpressure peak prediction. Due to the small-scale datasets and parameter errors of self-similar model for jet propagation, the model cannot provide real-time feedback on the interaction between the shock wave and the flame front. Jet tip locations and velocities obtained from the BP-TJF model and experiments are generally consistent in magnitude and overall trends. Without considering the flame structure, the prediction framework developed in this paper can calculate the jet tip propagation characteristics with little difference from experiments and CFD, which is a great advantage, especially in the calculation of subsonic jets.