Statistical Properties of Turbulence Under a Smart Lagrangian Forcing

ORAL

Abstract

We investigate how turbulence is reshaped by the presence of externally forced light particles, using high-resolution direct numerical simulations with four-way coupling. The particles are subject to an oscillatory force that in turn locally affects the fluid flow through momentum exchange at the position of the particle. Since the light particles preferentially concentrate in high vorticity regions, this leads to an intricate preferential turbulence modulation. By systematically varying forcing amplitude, frequency, and particle volume fraction, we show that this "smart" Lagrangian forcing suppresses small-scale turbulence intermittency - a central feature of turbulent flows linked to extreme events and anomalous scaling laws. Our results demonstrate a resonant modulation mechanism: intermittency reduction is most effective when the forcing frequency aligns with the Kolmogorov timescale. Particle collisions, modeled through hard-sphere interactions enforcing volume exclusion, play a key role by amplifying feedback when particles fill coherent structures. Experiments with different Stokes numbers highlight that this effect is non-trivial, manifesting only when preferential concentration occurs (St ≈ 1), and not in the tracer or heavy-particle limits.

*This research was supported by European Union's HORIZON MSCA Doctoral Networks programme under Grant Agreement No. 101072344, project AQTIVATE (Advanced computing, QuanTum algorIthms and data-driVen Approaches for science, Technology and Engineering), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme Smart-TURB (Grant Agreement No. 882340). This presentation is part of the project "Shaping turbulence with smart particles" with Project No. OCENW.GROOT.2019.031 of the research program Open Competitie ENW XL which is (partly) financed by the Dutch Research Council (NWO).

Presenters

  • Andre Freitas

    • University of Rome "Tor Vergata"

Authors

  • Andre Freitas

    • University of Rome "Tor Vergata"
  • Xander M de Wit

    • Eindhoven University of Technology
  • Ziqi Wang

    • Eindhoven University of Technology
  • Luca Biferale

    • University of Rome Tor Vergata and INFN
  • Federico Toschi

    • Eindhoven University of Technology