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Smart inertial particles

Simona Colabrese1,*, Kristian Gustavsson1,2, Antonio Celani3, and Luca Biferale1

  • 1Department of Physics and INFN, University of Rome “Tor Vergata,” Via della Ricerca Scientifica 1, 00133, Rome, Italy
  • 2Department of Physics, University of Gothenburg, Origovägen 6 B, 41296, Göteborg, Sweden
  • 3Quantitative Life Sciences, Abdus Salam International Centre for Theoretical Physics, Strada Costiera 11, 34151, Trieste, Italy

  • *simona.colabrese@roma2.infn.it
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Phys. Rev. Fluids 3, 084301 – Published 6 August, 2018

DOI: https://doi.org/10.1103/PhysRevFluids.3.084301

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 behavior 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 (ABC) helical flow. We show that smart particles are able to learn how to reach extremely intense vortical structures in all the tackled cases.

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Phys. Rev. Fluids 3, 084301– Published 6 August, 2018
  • Received 15 December 2017
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