A review of recent advances in the application of machine learning algorithms for gas turbine combustion

Abstract

With the rapid advancement of computing power and significant progress in machine learning (ML) algorithms, ML has shown immense potential across a wide range of fields, particularly in simulation, experimental analysis, and condition monitoring. In the realm of combustion science and engineering, ML techniques have driven substantial advancements, particularly in gas turbine engine combustion. The integration of intelligent algorithms in combustion modelling and analysis has led to more accurate predictions, enhanced performance, and the potential for more efficient and environmentally friendly combustion processes. This paper provides a comprehensive review of the current state of research on intelligent algorithms applied to combustion chemical reaction kinetics, combustion simulation, performance prediction, combustion state, and instability monitoring. The review highlights the progress made and offers valuable insights for improving the performance of gas turbine engines. Additionally, the paper discusses the challenges and prospects of applying intelligent algorithms in combustion research, including issues related to data quality, model interpretability, and computational complexity, while identifying avenues for future development and innovation.

Keywords

Machine learning / Chemical reaction kinetics / Instability prediction / PINN

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