Optimizing Fluid Mixing with Machine Learning

 Interaction between RL and Markov decision method is interesting 

Optimizing Fluid Mixing with Machine Learning

Tokyo University of Science (Japan)

August 29, 2022

Researchers in Japan have proposed a machine learning-based approach for optimizing fluid mixing for laminar flows. The researchers used reinforcement learning (RL), in which intelligent agents perform actions in an environment to maximize the cumulative reward. The team addressed RL's inefficiencies in dealing with systems involving high-dimensional state spaces by describing fluid motion using only a single parameter. Researchers used the Markov decision process to formulate the RL algorithm, and the Tokyo University of Science's Masanobu Inubushi said the program "identified an effective flow control, which culminated in an exponentially fast mixing without any prior knowledge." The RL method also enabled effective transfer learning of the trained "mixer," significantly reducing its time and training cost.