Machine-Learning-Enabled Prediction of Spatiotemporal Boundary Conditions in Multiphase Flow Simulations

Simulating fuel injection and spray breakup remains a computational challenge due to the multi-physics and multi-scale nature of the problem. Static coupling approaches have been adopted whereby simulation of the internal flow is replaced with a spatiotemporal boundary condition that defines the injection profile at the injector orifice exit. However, the generation of these data is a computationally expensive task due to fine temporal and spatial resolution requirements. This presentation summarizes our work in developing a machine-learning-based emulator to learn efficient surrogate models for spatiotemporal boundary conditions. An interpretable Bayesian learning strategy is employed to understand the effect of design parameters on the learned spatiotemporal fields. Autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process (GP) models are then used to predict the spatiotemporal flowfields at the injector exit for test design conditions not seen during training. The emulation framework can predict the spatiotemporal boundary conditions within a few seconds, thus achieving a speed-up factor of up to 38 million over the traditional simulation-based approach.