A Neural Network Approach for the Simulation of Real Fluid Two-Phase Combustion Using a Multi-Species (H2/O2) Mechanism

Fully compressible two-phase flow configurations present many challenges for numerical modelling, requiring the development of Real Fluid Models (RFMs) able to simulate flows in subcritical, transcritical and supercritical regimes. Such an RFM has been recently developed at IFPEN based on physical properties lookup tables, mainly for binary and ternary chemical systems. This paper proposes an Artificial Neural Network (ANN) approach to overcome the limitations of lookup tables of thermodynamic properties and to apply RFM to multi-species combustion. A methodology for generating an optimized data set by combining a vapor–liquid equilibrium (VLE) thermodynamic solver and the in situ adaptive tabulation (ISAT) method is developed. It aims to improve the neural network training process for two-phase combustion simulations where many species are present. This ANN methodology has been implemented in the CONVERGE CFD solver and validated using a mixing layer (LOX/GH2) benchmark from the literature relevant to rocket conditions, and an academic gaseous (H2/O2) case relevant to hydrogen combustion. The results show that this ANN approach makes H2 combustion simulation possible when coupled to the RFM framework and using a 10-species kinetic mechanism.