Towards Intelligent Combustion: A Bibliometric and Machine Learning Analysis of Hydrogen-Fueled Gas Turbines

Thermo-acoustic instability poses significant challenges to the safe and efficient operation of hydrogen-fueled gas turbines, particularly in industrial applications. This study conducts a comprehensive bibliometric analysis to explore research trends, identify gaps, and evaluate predictive modeling opportunities for thermo-acoustic instability in hydrogen combustion systems. Using Scopus and Web of Science data analyzed with Bibliometrix (R-package), the study maps the thematic evolution of key research areas. The findings reveal extensive work on performance optimization, flow dynamics, and combustion propagation, yet limited attention to hydrogen-specific thermo-acoustic instability. Additionally, while numerical simulations and active control mechanisms are well-developed, real-time predictive modeling using machine learning (ML) remains underexplored. To bridge these gaps, this study proposes a hybrid AI-CFD framework incorporating neural networks for enhanced instability prediction and control. The insights gained contribute to advancing hydrogen combustion technologies, enabling safer and more efficient gas turbine operations