Artificial Neural Network Aided Vapor-Liquid Equilibrium Model for Multi-Component High-Pressure Transcritical Flows with Phase Change

With ever increasing demand for high performance combustors, increasing the chamber pressure is one often sought after option. This leads to the working conditions to overlap with the transcritical/ supercritical regime of the reactants. The CFD modeling of transcritical flows with phase change is very challenging. Such modeling can be achieved by using the first-principled vapor-liquid equilibrium (VLE) theory coupled with a real-fluid equation of state (EOS). However, two major problems exist with VLE calculations - robustness and speed. In order to tackle the second problem, in-situ adaptive tabulation (ISAT) has been used recently to provide significant computational speed-ups, but does not guarantee robustness. This work attempts to tackle both the issues by introducing a plug-and-play Artificial Neural Network (ANN) aided VLE model to couple with CFD. Training is performed on Python and inference speeds are optimized using Open Neural Network Exchange (ONNX). The model is validated against the results generated by both direct VLE calculation as well as ISAT for a high-pressure shock-droplet interaction case. The extendability of the model is also shown by low-pressure shock-droplet interaction cases, validated by experimental results. Finally, the computational speeds and parallel scaling achieved by the method are presented.