NEURAL NETWORK ACCELERATION OF NUMERICAL SIMULATION OF METHANE COMBUSTION IN A GAS TURBINE ENGINE
Authors
DOI:
https://doi.org/10.31489/2025N4/53-62Keywords:
neural network, combustion, gas turbine engine, numerical simulationAbstract
Gas turbines are essential for high-power energy generation, but growing demands to reduce NOₓ and CO₂ emissions make traditional combustion chamber design increasingly complex and costly. This work proposes a new modeling paradigm that combines high-fidelity Computational Fluid Dynamics using neural network learning to accelerate emission prediction. A Computational Fluid Dynamics model was developed using the Reynolds-averaged Navier-Stokes equations with the k–ε turbulence model and a non-premixed Probability Density Function approach to simulate turbulent methane combustion. NOₓ emissions were calculated post-simulation using the Zeldovich mechanism. Model validation included varying fuel flow, excess air ratio, and wall heat loss. To speed up evaluations, a multilayer perceptron neural network was trained on Computational Fluid Dynamics results to predict NOₓ and CO₂ emissions based on key inputs (fuel rate, air excess, temperature, pressure, cooling). The model achieved high accuracy with a coefficient of determination (R^2) of 0.998 for NOₓ and 0.956 for CO₂ on an independent test set. Results showed good agreement with both experimental data and a Network of ideal reactors model using detailed kinetic scheme of methane combustion - Mech 3.0. This neural network serves as a fast surrogate model for emissions assessment, enabling rapid optimization of low-emission combustor designs. The approach is suitable for digital twins and combustion control systems and is adaptable to alternative fuels like hydrogen and ammonia.
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