R. Ranade, S. Alqahtani, A. Farooq, T. Echekki
Fuel, 241, pp. 625-636, (2019)
Chemistry reduction, Artificial neural networks, Hydrocarbon oxidation, Pyrolysis
The oxidation chemistry of complex hydrocarbons involves large mechanisms with hundreds or thousands of chemical species and reactions. For practical applications and computational ease, it is desirable to reduce their chemistry. To this end, high-temperature fuel oxidation for large carbon number fuels may be described as comprising two steps, fuel pyrolysis and small species oxidation. Such an approach has recently been adopted as ‘hybrid chemistry’ or HyChem to handle high-temperature chemistry of jet fuels by utilizing time-series measurements of pyrolysis products. In the approach proposed here, a shallow Artificial Neural Network (ANN) is used to fit temporal profiles of fuel fragments to directly extract chemical reaction rate information. This information is then correlated with the species concentrations to build an ANN-based model for the fragments’ chemistry during the pyrolysis stage. Finally, this model is combined with a C0-C4 chemical mechanism to model high-temperature fuel oxidation. This new hybrid chemistry approach is demonstrated using homogeneous chemistry calculations of n-dodecane (n-C12H26) oxidation. The experimental uncertainty is simulated by introducing realistic noise in the data. The comparison shows a good agreement between the proposed ANN hybrid chemistry approach and detailed chemistry results.