Data-Driven and Equation-Informed Optimal Control of Lagrangian pairs in turbulent flows

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• Chiara Calascibetta
• Monday 26 February 2024, 13:00-14:00
• MR5, CMS.

If you have a question about this talk, please contact Prof. John R. Taylor.

We show how to apply optimal control theory to catch a passive drifting target in a turbulent flow by an autonomous flowing agent with limited maneuverability. For the case of a perfect knowledge of the environment, we show that Optimal Control theory can overcome chaotic dispersion capturing the Lagrangian target in the shortest possible time [1]. We also provide baselines using heuristic policies based on local-only hydrodynamical cues [2]. How to extend this approach to model-free Reinforcement Learning tools is also briefly discussed [3]. Data are open downloadable from TURB Lagr [4], a database of more than 300K three-dimensional trajectories of tracer particles advected by a fully developed homogeneous and isotropic turbulent flow.

(1) Calascibetta et al., Commun. Phys. 6, 256 (2023). (2) Monthiller et al., Phys. Rev. Lett. 129, 064502 (2022). (3) Calascibetta et al., Eur. Phys. J. E 46, 9 (2023). (4) smart-turb.roma2.infn.it

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