R. Ranade, S. Alqahtani, A. Farooq, T. Echekki
Fuel, 251, pp. 276-284, (2019)
Chemistry reduction, Artificial neural networks, Hydrocarbon oxidation, Pyrolysis
An extended hybrid chemistry approach for complex hydrocarbons is developed to capture high-temperature fuel chemistry beyond the pyrolysis stage. The model may be constructed based on time-resolved measurements of oxidation species beyond the pyrolysis stage. The species’ temporal profiles are reconstructed through an artificial neural network (ANN) regression to directly extract their chemical reaction rate information. The ANN regression is combined with a foundational C0-C2 chemical mechanism to model high-temperature fuel oxidation. This new approach is demonstrated for published experimental data sets of 3 fuels: n-heptane, n-dodecane and n-hexadecane. Further, a perturbed numerical data set for n-dodecane, generated using a detailed mechanism, is used to validate this approach with homogeneous chemistry calculations. The results demonstrate the performance and feasibility of the proposed approach.