A Physics-Informed Machine Learning Approach for Predicting Atomized Drop Distributions in Liquid Jet Simulations

A key goal for simulations of liquid jet atomization is the accurate prediction of the size distribution and number density of atomized drops. The multi-scale nature of these flows makes it nearly impossible to capture all scales within a single simulation. Specifically, the breakup processes producing the smallest drops through secondary atomization often necessitate resolutions far below the Kolmogorov scale. Existing physics-based and stochastic breakup models fail to account for the local and instantaneous flow field and drop geometry. We present a physics-informed machine learning model for predicting the distribution of daughter drops generated during the breakup of under-resolved drops. We showcase proof-of-concept results from simplified configurations of 3D Taylor-Green vortex flows and homogeneous isotropic turbulence. By training on high-resolution simulations, the model can predict the result of breakup from severely under-resolved input fields. Compared to low-resolution simulations or phenomenological methods, our approach achieves superior accuracy in predicting drop size distribution and quantities of interest including surface area distribution and breakup probability.