Smart Inertial Particles

TL;DR

This study demonstrates that reinforcement learning enables inertial particles to adaptively target high-vorticity regions in various complex flow environments by actively modulating their size.

Contribution

The paper introduces a novel approach where inertial particles learn to optimize their size for targeting vortical structures using reinforcement learning in different flow scenarios.

Findings

Particles successfully target high-vorticity regions across flow types

Reinforcement learning enables particles to adaptively modulate size

Effective in 2D and 3D flow configurations

Abstract

We performed a numerical study to train smart inertial particles to target specific flow regions with high vorticity through the use of reinforcement learning algorithms. The particles are able to actively change their size to modify their inertia and density. In short, using local measurements of the flow vorticity, the smart particle explores the interplay between its choices of size and its dynamical behaviour in the flow environment. This allows it to accumulate experience and learn approximately optimal strategies of how to modulate its size in order to reach the target high-vorticity regions. We consider flows with different complexities: a two-dimensional stationary Taylor-Green like configuration, a two-dimensional time-dependent flow, and finally a three-dimensional flow given by the stationary Arnold-Beltrami-Childress helical flow. We show that smart particles are able to learn how to reach extremely intense vortical structures in all the tackled cases.