Prediction of ignition delay times of Jet A-1/hydrogen fuel mixture using machine learning

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To control the global warming trends, carbon footprint of all the human activities needs to be restricted, including the aviation industry. Mixing hydrogen with commercial kerosene jet fuels appears as a promising alternative fuel to reduce the carbon dioxide emissions of aviation engines. The addition of hydrogen can significantly impact the auto-ignition process of aviation fuels, which is a key ingredient of engine reliability. However, accurate calculations or measurements of ignition delay times (IDTs) over a wide range of pressures, temperatures and fuel blending ratios are complicated and time-consuming. To achieve real-time prediction of ignition delay time for hydrogen-blended jet fuels under various operating conditions, machine learning methods are introduced to build a data-driven proxy model in this work. First, the ignition delay times of Jet A-1/hydrogen fuel mixture are simulated using the well-known HyChem combustion reaction mechanism under different pressures, temperatures, equivalence ratios and blending molar ratios of hydrogen. After some validation against experimental results, an artificial neural network (ANN) model is trained using the database of ignition delay times. Furthermore, a sub-ANN is nested to the original ANN model as an improvement on certain local conditions. The results show that the single original ANN model leads to a large local relative error for IDT <103μs, generally referring to high temperatures and pressures conditions. With the help of the nested sub-ANN approach, the improved model achieves a significantly better accuracy for very fast ignition. Compared with other machine learning models such as random forest, the nested sub-ANN model is more efficient to predict the ignition delay times of Jet A-1/hydrogen fuel mixture, still mitigating computational cost. The sensitivity analysis shows how the nested sub-ANN model is less sensitive to the uncertainties of the input parameters than the original ANN model.