Prediction of sound pressure fluctuations in the start-up phase of thermoacoustic oscillations under external perturbation

摘要

Abstract To suppress excessive thermoacoustic instabilities in the gas turbine, it must be possible to predict pressure changes in the combustion chamber. The time-series data of acoustic pressure fluctuations in the Rijke type burner under external sound source interference were studied combined via nonlinear theory, and a new data-driven model for predicting internal sound pressure fluctuations under such conditions was established. An improved particle swarm optimization (PSO) algorithm was proposed to optimize the parameters of the support vector regression (SVR) model, and the parameter optimization time required for the improved PSO algorithm is only 3/5 of that before the improvement. The results show that at least 0.94?ms ahead, the improved data-driven model can accurately predict sound pressure oscillation signals. The improved PSO-SVR model proved to be more accurate than the Multilayer Perceptron (MLP) model and Gaussian process regression (GPR) model in predicting the fluctuation of sound pressure under variable conditions and can provide effective guidance for predicting and eliminating the thermoacoustic oscillations in the actual combustion chambers.