Development of an Artificial Intelligence Model to Predict Combustion Properties, with a Focus On Auto-ignition Delay

Abstract

Abstract Hydrogen-compatible gas turbines are one way to decarbonize electricity production. Burning and handling hydrogen is not trivial because of its tendency to detonate. Mandatory safety parameters can be estimated thanks to predictive detailed kinetic models, but with significant calculation times that limit coupling with fluid mechanic codes. An auto-ignition prediction tool was developed based on an artificial intelligence (AI) model for fast computations and an implementation into an explosion model. A dataset of ignition delay times was generated automatically using a recent detailed kinetic modelselected from the literature. Generated data covers a wide operating range and different compositions of fuels. Clustering problems in sample points were avoided by a quasi-random Sobol sequence, which covers uniformly the entire input parameter space. The different algorithms were trained, cross-validated and tested using a database of more than 70'000 ignitions cases of Natural Gas/Hydrogen blends calculated with the full kinetic model by using a common split of 70/30 for training, testing. The AI model shows a high degree of robustness. For both the training and testing datasets, the average value of the correlation coefficient was above 99.91%, the Mean Absolute Error (MAE) and the Mean Square Error (MSE) around 0.03 and lower than 0.04 respectively. Tests showed the robustness of the AI model outside the ranges of pressure, temperature, and equivalence ratio of the data set. A deterioration is however observed with increasing amounts of large alkanes in the natural gas.