Prediction of acoustic pressure of thermoacoustic combustion instability based on Elman neural network

Abstract

Accurate prediction of thermoacoustic instability is a prerequisite for thermoacoustic control to avoid the damage of combustion chamber, however, this problem has not been completely solved yet. This paper proposes a data-driven method based on the Elman neural network (ENN) to predict the value of acoustic pressure of combustion instability. As a comparison, a model based on support vector machine (SVM) was built. It is proved that ENN has better prediction performance with a certain predicted time horizon compared to the SVM method. What is more, the prediction model based on ENN can adapt to time-varying characteristics of the transition scenario which is characterized by amplitude modulation, multiple frequencies, and irregular bursts. ENN model still maintains enough prediction accuracy for various input training sets, indicating that ENN can fully mine the features of data and has a strong feature extraction ability in combustion oscillation prediction. Hence, it is demonstrated that ENN is a promising prediction tool for thermoacoustic instability under various combustion conditions. These findings are of great significance for the accurate prediction and control of thermoacoustic instability.

Active control of thermoacoustic oscillation based on the x-nlms algorithm

“…These oscillations arise due to the coupling between heat release and pressure fluctuations within the combustion chamber, creating a feedback loop that sustains the instability. As a result, researchers have been exploring different control strategies to mitigate thermoacoustic oscillations and improve the performance and safety of combustion systems [3].…”

Monitoring of Thermoacoustic Combustion Instability via Recurrence Quantification Analysis and Optimized Deep Belief Network

“…Monitoring thermoacoustic oscillations is a crucial aspect of ensuring the stability, efficiency, and safety of combustion systems [9,10]. Several methods have been developed to detect and analyze thermoacoustic oscillations in real time [11].…”