By blending physics-based simulations with neural networks, researchers have unveiled a powerful new tool for predicting combustion instabilities in hydrogen-fueled turbines - faster, smarter, and cleaner.
Study: A hybrid CFD-neural network framework for the early prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled gas turbine combustors. Image Credit: Audio und werbung/Shutterstock.com
In a recent article published in Next Energy, researchers introduced a novel framework that combines computational fluid dynamics (CFD) with neural networks (NN) to predict longitudinal thermo-acoustic instabilities (TAIs) in hydrogen-fueled gas turbine combustors. The goal: to improve the safety and efficiency of combustion systems while supporting the shift toward cleaner energy solutions.
Hydrogen’s Double-Edge Sword: Clean Energy with Combustion Risks
As the energy sector pivots toward sustainability, hydrogen is emerging as a promising alternative fuel for gas turbines. Its appeal lies in its high efficiency, fast flame speed, and zero carbon emissions. However, these benefits come with challenges - chief among them are TAIs, which can undermine performance, compromise safety, and reduce the overall reliability of combustion systems.
Traditionally, analyzing TAIs relies on high-fidelity CFD simulations. While accurate, these simulations are computationally intensive and impractical for real-time monitoring. Enter machine learning. Neural networks, especially when trained on CFD-generated data, can recognize combustion patterns and predict instability events with far greater efficiency - bridging the gap between accuracy and speed.
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Why Traditional CFD Falls Short - and How Neural Networks Step In
To overcome the limitations of conventional modeling, the research team developed a hybrid framework that fuses CFD with a feedforward neural network (FNN) to enable early prediction of TAIs. The model uses a simplified two-dimensional (2D) planar combustor geometry based on the SGT-100 model - a setup designed to balance computational efficiency with physical accuracy while generating high-quality training data.
Through CFD simulations, the team modeled hydrogen combustion and extracted time-resolved pressure and heat-release data from several monitoring points inside the combustor. To identify instability zones, they applied the Local Rayleigh Index (LRI), a proven method that quantifies the interaction between acoustic pressure and heat-release fluctuations.
The neural network itself consisted of one input layer, a single hidden layer with 10 neurons, and a linear output layer. The team applied Bayesian regularization to optimize performance and minimize overfitting, enabling robust real-time predictions based on early-stage combustion data.
A Smarter Model: How the CFD-FNN Framework Works
The hybrid model demonstrated strong predictive capabilities. Using LRI analysis, the team identified clear variations in acoustic behavior across probe locations. Probe X2 stood out as the most unstable, showing consistently positive LRI values that signaled thermo-acoustic amplification. In contrast, probes X1, X4, and X6 returned negative values, indicating stable regions and effective damping.
During validation, the FNN delivered impressive results: a correlation coefficient (R²) of 0.9998 and a root mean square error (RMSE) of just 924. Beyond accuracy, the model’s computational efficiency was striking - it generated predictions approximately 676 times faster than traditional CFD methods, making it an ideal candidate for real-time diagnostics and control.
Real-Time Predictions, Real-World Impact
The implications of this research are wide-ranging. The CFD-FNN framework provides a powerful tool for designing and optimizing hydrogen-fueled combustion systems across multiple sectors, ranging from power generation to transportation.
Early detection of TAIs enhances operational safety and reliability, while also facilitating the integration of hydrogen combustion into existing energy infrastructure. By enabling real-time monitoring and control, the model makes hydrogen a more practical and scalable fuel choice for industries shifting toward low-emission technologies. The approach also holds promise for adaptation across other fuel types and combustion systems.
Paving the Way for Cleaner, Safer Hydrogen Combustion
At its core, this study marks a meaningful step forward in hydrogen combustion technology. By merging high-fidelity simulation with machine learning, the research team created a framework that’s both accurate and fast - ideal for real-world implementation.
The model enhances our ability to predict and manage combustion instabilities, while also supporting the broader mission of sustainable energy development. With its speed and reliability, it positions hydrogen as a safer, more viable fuel in the clean energy transition.
What’s Next: Scaling Up and Strengthening the Model
Looking ahead, the next phase of development involves extending the model to three-dimensional (3D) configurations, which would better capture complex flow dynamics like turbulence and azimuthal instabilities. Incorporating experimental data alongside CFD-generated datasets could also strengthen the model’s accuracy and generalizability under varying real-world conditions.
This research highlights the increasing importance of advanced data-driven tools in addressing combustion challenges. As hydrogen gains momentum as a clean energy carrier, innovations like the CFD-FNN framework will be key to ensuring its safe and efficient use.
Journal Reference
Agonga, O, F., Othman, N., & Yasin, M, F, M. 2025. A hybrid CFD-neural network framework for the early prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled gas turbine combustors. Next Energy, 100497 (10). DOI: 10.1016/j.nxener.2025.100497, https://www.sciencedirect.com/science/article/pii/S2949821X25002601
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