[DragREACT] New strategies for optimal drag reduction via fiber control in turbulence
Ente: European Commission
Scadenza: 2028-09-30
Importo max: 226.420,56 EUR
Paese: EU
Descrizione
Is it possible to control highly out-of-equilibrium turbulent flows by anisotropic particles or slender fibers? Turbulent drag reduction remains one of the biggest challenges in out-of-equilibrium statistical physics and fluid dynamics, with major implications for energy efficiency in industrial and environmental flows. While polymers and other additives have long been known to reduce friction drag, a key question is whether the dynamics of passive, yet anisotropic particles can be harnessed to enhance natural preferential concentration, migration, or alignment, so as to steer turbulence in smarter ways. DragREACT tackles this challenge by developing smart Lagrangian control strategies that exploit the unique translational-rotational dynamics of inertial anisotropic particles, such as rods and rigid fibers, suspended in turbulent channel flows. Leveraging both physical insights and modern data-driven tools (e.g., reinforcement learning (RL)), DragREACT aims to transform passive fibers into mobile flow modifiers. Specifically, DragREACT will: (i) understand how key fibers properties (shape, inertia and mass distribution), govern their translational-rotational dynamics and influence key Lagrangian behaviours, such as near-wall accumulation, preferential alignment, and controlled tumbling/spinning; (ii) optimise these behaviours through external forcing (e.g., magnetic field), using RL algorithms; and (iii) transfer this knowledge into a fully coupled Eulerian-Lagrangian framework to assess how optimised fiber dynamics feed back into the carrier flow and affect drag. By combining direct numerical simulations, physical modelling, and state-of-the-art optimisation methods, DragREACT aims to significantly advance the fundamental understanding of fiber-flow interactions. Furthermore, it will deliver a new Eulerian-Lagrangian framework for turbulence control in particle-laden flows, an ambitious step that paves the way for optimised drag reduction in complex flows.
Settori: Turbulence, turbulent transport, particle-laden flows, drag reduction, channel flows, Lagrangian particles, fibers, optimisation, control theory, reinforcement learning, direct numerical simulations
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