A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow

Abstract

The prediction of interfacial structure in two-phase flow systems is difficult and challenging. In this paper, a novel physics-informed reinforcement learning-aided framework (PIRLF) for the interfacial area transport is proposed. A Markov Decision Process that describes the bubble transport is established by assuming that the development of two-phase flow is a stochastic process with Markov property. The framework aims to capture the complexity of two-phase flow using the advantage of reinforcement learning (RL) in discovering complex patterns with the help of the physical model (Interfacial Area Transport Equation) as reference. The details of the framework design are described including the design of the environment and the algorithm used in solving the RL problem. The performance of the PIRLF is tested through experiments using the experimental database for vertical upward bubbly air-water flows. The result shows a good performance of PIRLF with rRMSE of 6.556%. The case studies on the PIRLF performance also show that the type of reward function that is related to the physical model can affect the framework performance. Based on the study, the optimal reward function is established. The approaches to extending the capability of PIRLF are discussed, which can be a reference for the further development of this methodology.