Evolution of inertial particle clustering in decaying turbulence

Abstract

Inertial particle dynamics in turbulence are commonly interpreted in terms of the instantaneous particle Stokes number, as supported by studies in statistically stationary homogeneous isotropic turbulence. But in freely decaying turbulence the Kolmogorov time scale evolves continuously, causing the effective Stokes number, St, to vary even though particle properties remain unchanged. Whether preferential concentration remains uniquely determined by this instantaneous St or depends on the flow's previous evolution has remained largely unexplored. We investigate inertial-particle clustering during the decay of homogeneous isotropic turbulence using direct numerical simulations of heavy point particles in the one-way coupling limit. Clustering is quantified through 3D Voronoï tessellations for populations evolving either from statistically stationary clustered states or initially random distributions. We consider both conventionally forced homogeneous isotropic turbulence and velocity fields reconstructed using physics-informed neural networks. We show that preferential concentration cannot be described solely by the instantaneous St. While particle slip velocity follows the classical steady-state dependence on the instantaneous St, clustering statistics retain a measurable history dependence throughout decay. Existing stationary scaling laws remain applicable to cluster-size evolution, but only through history-dependent prefactors. Particle distributions evolving from different initial conditions rapidly converge towards similar large-scale spatial organisation, indicating that the carrier flow determines the geometry of preferential concentration while finer clustering statistics preserve imprints of the previous evolution. These results demonstrate that preferential concentration in non-stationary turbulence is governed jointly by the instantaneous flow state and its initial state.

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BibTeX RIS

BibTeX

@misc{obligado2026evolution,
  title = {{Evolution of inertial particle clustering in decaying turbulence}},
  author = {Martín Obligado and Sofía Angriman},
  howpublished = {arXiv},
  year = {2026},
  month = aug,
  doi = {10.48550/arxiv.2608.30545},
  eprint = {2608.30545},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.30545},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Evolution of inertial particle clustering in decaying turbulence
AU  - Martín Obligado
AU  - Sofía Angriman
PY  - 2026
DA  - 2026/08/31
PB  - arXiv
DO  - 10.48550/arxiv.2608.30545
UR  - https://arxiv.org/abs/2608.30545
AB  - Inertial particle dynamics in turbulence are commonly interpreted in terms of the instantaneous particle Stokes number, as supported by studies in statistically stationary homogeneous isotropic turbulence. But in freely decaying turbulence the Kolmogorov time scale evolves continuously, causing the effective Stokes number, St, to vary even though particle properties remain unchanged. Whether preferential concentration remains uniquely determined by this instantaneous St or depends on the flow's previous evolution has remained largely unexplored. We investigate inertial-particle clustering during the decay of homogeneous isotropic turbulence using direct numerical simulations of heavy point particles in the one-way coupling limit. Clustering is quantified through 3D Voronoï tessellations for populations evolving either from statistically stationary clustered states or initially random distributions. We consider both conventionally forced homogeneous isotropic turbulence and velocity fields reconstructed using physics-informed neural networks. We show that preferential concentration cannot be described solely by the instantaneous St. While particle slip velocity follows the classical steady-state dependence on the instantaneous St, clustering statistics retain a measurable history dependence throughout decay. Existing stationary scaling laws remain applicable to cluster-size evolution, but only through history-dependent prefactors. Particle distributions evolving from different initial conditions rapidly converge towards similar large-scale spatial organisation, indicating that the carrier flow determines the geometry of preferential concentration while finer clustering statistics preserve imprints of the previous evolution. These results demonstrate that preferential concentration in non-stationary turbulence is governed jointly by the instantaneous flow state and its initial state.
ER  -

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