2021 AdG GRANT Smart-TURB
A Physics-Informed Machine-Learning Platform for
Smart Lagrangian Harness and Control of TURBulence
Where is it difficult to control, predict and model a flowing system? to search and navigate inside it? to be prepared against extreme events? to tame them? It is in turbulent flows.
Turbulence is ubiquitous and unsolved from the point of view of out-of-equilibrium fundamental physics, uncontrollable from the engineering aspects, and a deadlock for brute-force numerical and experimental investigations. Indeed, progress by using conventional methods has been slow.
In this project, I propose to explore new avenues crossing the boundaries between Theoretical Engineering and Applied Physics using algorithms from Artificial Intelligence (AI)  to study and control turbulence in an innovative way using smart Lagrangian objects in a vast array of flows. I am committed to: (i) develop original applications of AI algorithms to track and harness moving coherent structures and/or statistical turbulent fluctuations, (ii) optimise flow navigation of buoyant objects and active surface drifter, (iii) invent collective search protocols to locate emissions from fixed or floating sources,  (iv) minimise turbulent dispersion of a swarm of autonomous underwater explorer and (v) perform new in-silico experiments for data-assimilation, to predict extreme-events, or to control turbulent fluctuations by novel Lagrangian injection/adsorption mechanisms.
The unifying fil-rouge of my project is to gain a Deep Understanding of turbulence by performing cutting-edge Lagrangian numerical studies. The project is both methodology oriented, with the grand challenge of developing fully unconventional applications of (Deep) Reinforcement Learning for fluid dynamics, and problem driven, delivering a series of specific efficient control strategies for important realistic flowset-ups and applications to the geophysical fields .