A hybrid CFD-neural network framework for the early prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled gas turbine combustors

This study introduces a hybrid Computational Fluid Dynamics–Neural Network (CFD–NN) framework for real-time prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled gas turbine combustors—critical for future clean energy systems. A high-fidelity two-dimensional CFD model simulated hydrogen combustion and provided time-resolved pressure and heat release data. The Local Rayleigh Index (LRI) identified probe X 2 as a strong instability zone (LRI ≈ 10⁷ Pa·W), while X 1 , X 4 , and X 6 showed stable behavior. A feedforward neural network trained on early-stage data from probe X3 achieved high prediction accuracy (R² = 0.9998, Root Mean Square Error (RMSE = 924)) and delivered predictions ∼676× faster than CFD (∼334 predictions/s). By combining physics-based modeling with machine learning, this hybrid method enables real-time, physics-informed diagnostics, supporting smart combustor design and closed-loop control in next-gen hydrogen turbines. • A hybrid CFD–FNN framework is proposed for early prediction of thermoacoustic instability in hydrogen-fueled gas turbines. • High-fidelity 2D CFD extracted pressure and heat-release data at six probes, using the Rayleigh Index to identify instability. • Probe X3 enabled earliest instability detection, while X5 captured transitional dynamics supporting mid-stage prediction. • An FNN trained on probe X3 achieved R² = 0.9998, low RMSE, and delivered a 676× speed-up over CFD for real-time prediction. • The framework enables physics-informed real-time diagnostics, supporting closed-loop combustion control in hydrogen turbines.