Multiphase Turbulence Modeling Using Sparse Regression and Gene Expression Programming

Turbulence in two-phase flows drives many important natural and engineering processes, from geophysical to nuclear power generation. Strong interphase coupling between the carrier fluid disperse phase precludes use of classical turbulence models developed for single-phase flows. In recent years, there has been an explosion machine learning techniques closure modeling, though rely on augmenting existing models. this work, we propose approach that blends sparse regression gene expression programming (GEP) generate closed-form algebraic simulation data. Sparse is used determine a minimum set functional groups required capture physics, GEP automate formulation coefficients dependencies operating conditions. The framework demonstrated homogeneous turbulent gas-particle which two-way generates sustains carrier-phase turbulence.