Low-Rank Modeling of Primary Atomization

Improving primary atomization of a liquid jet in a gas environment by active control can potentially benefit several engineering systems. We propose a low-rank method to reconstruct and predict the multiphase field from time histories of volume-of-fluid data. The method combines elements from image processing, dynamic mode decomposition, and optical flow to form a low-rank model that can retain a sharp interface with complex topological features, like ligaments and drops. The method is applied to volume-of-fluid data acquired from the simulation of the primary atomization of a water jet and used to develop a reduce-order controller to improve the jet's atomization.