Multiphase turbulence modeling using sparse regression and gene expression programming

TL;DR

This paper introduces a novel approach combining sparse regression and gene expression programming to develop algebraic turbulence models for multiphase flows, addressing limitations of existing machine learning methods in complex two-way coupled systems.

Contribution

It presents a new framework that automatically generates closed-form turbulence models from data for multiphase flows, improving modeling accuracy in complex systems.

Findings

Successfully modeled gas-solid flow turbulence with the proposed method.

Demonstrated the framework's ability to capture two-way coupling effects.

Enhanced understanding of multiphase turbulence modeling.

Abstract

In recent years, there has been an explosion of machine learning techniques for turbulence closure modeling, though many rely on augmenting existing models. While this has proven successful in single-phase flows, it breaks down for multiphase flows, particularly when the system dynamics are controlled by two-way coupling between the phases. In this work, we propose an approach that blends sparse regression and gene expression programming (GEP) to generate closed-form algebraic models from simulation data. Sparse regression is used to determine a minimum set of functional groups required to capture the physics and GEP is used to automate the formulation of the coefficients and dependencies on operating conditions. The framework is demonstrated on a canonical gas--solid flow in which two-way coupling generates and sustains fluid-phase turbulence.