DNS and physics-informed surrogate models of surfactant-laden dispersed flows

This study seeks to elucidate the fundamental physics governing surfactant-laden liquid-liquid dispersion processes under industrially relevant scenarios (i.e., static mixing). Using a DNS approach, we explore different surfactant physicochemical parameters (i.e., elasticity, desorption, and adsorption kinetics), where we compare relevant metrics (i.e., droplet count, size distribution) and interrelate them with the underlying physics captured in each case. We explicitly account for the role of Marangoni stresses during deformation and breakage. The rich data fields extracted from DNS are used to train surrogate models that can provide inexpensive, yet accurate, physics-informed predictions of key dispersion performance metrics calculated through DNS. We explore the application of deep convolutional recurrent autoencoders (CAE) to construct a low-dimensional representation of the dynamics obtained through DNS, and subsequently train neural networks with Long Short-Term Memory (LSTM) units to reconstruct the full physics and predict the dynamical evolution of metrics such as droplet count, size and local interfacial tension.