arXiv subjects

Sofía Angriman

Publications and source records attributed to Sofía Angriman.

9 recordsLinked to original sources

arXiv2026

Evolution of inertial particle clustering in decaying turbulence

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.

physics.flu-dyn↗

arXiv2026

Ice melting in an oscillatory flow

We investigate the melting dynamics of an ice disk subjected to an external oscillatory flow using two-dimensional direct numerical simulations, in the absence of buoyancy, varying the flow amplitude and its oscillation frequency. We identify two distinct regimes governed by the interplay between advection and diffusion within the boundary layer. For slow oscillations, the melting process is well described by an effective steady flow, where a description based on classical forced convection is applicable. For fast oscillations the melting time increases significantly and approaches the diffusion limit regime, as a result of the oscillatory flow being unable to renew the fluid within the oscillating boundary layer, causing cold meltwater to accumulate near the interface and reducing heat transfer.

physics.flu-dyn↗

arXiv2025

Collective effects of neighbouring melting ice objects

We present a study on the melting dynamics of neighbouring ice bodies by means of idealised simulations, focusing on collective effects, with the goal of obtaining fundamental insight into how collective interactions influence the melting of ice. Two neighbouring (vertically or horizontally aligned), square-shaped, and equally sized ice objects (size on the order of centimetres) are immersed in quiescent fresh water at a temperature of 20°C. By performing two-dimensional direct numerical simulations, and using the phase-field method to model the phase change, the collective melting of these objects is studied. When the objects are horizontally aligned, no significant influence of the neighbouring object on the melting time is observed. On the other hand, when vertically aligned, though the melting of the upper object is mostly unaffected, the melting time and the morphology of the lower ice body strongly depends on the initial inter-object distance. We report that the melting of the bottom object can be enhanced by more than 10%, or delayed more than 20%, displaying a non-monotonic dependence on the initial object size. We show that this behaviour results from a non-trivial competition between layering of cold fluid, which lowers the heat transfer, and convective flows, which favour mixing and heat transfer. For this melting in mixed convection, we were able to collapse our data onto a single curve.

physics.flu-dyn↗

arXiv2024

Active grid turbulence anomalies through the lens of physics informed neural networks

Active grids operated with random protocols are a standard way to generate large Reynolds number turbulence in wind and water tunnels. But anomalies in the decay and third-order scaling of active-grid turbulence have been reported. We combine Laser Doppler Velocimetry and hot-wire anemometry measurements in a wind tunnel, with machine learning techniques and numerical simulations, to gain further understanding on the reasons behind these anomalies. Numerical simulations that incorporate the statistical anomalies observed in the experimental velocity field near the active grid can reproduce the experimental anomalies observed later in the decay. The results indicate that anomalies in experiments near the active grid introduce correlations in the flow that can persist for long times.

physics.flu-dyn↗

arXiv2024

Turbulence unsteadiness drives extreme clustering

We show that the unsteadiness of turbulence has a drastic effect on turbulence parameters and in particle cluster formation. To this end we use direct numerical simulations of particle laden flows with a steady forcing that generates an unsteady large-scale flow. Particle clustering correlates with the instantaneous Taylor-based flow Reynolds number, and anti-correlates with its instantaneous turbulent energy dissipation constant. A dimensional argument for these correlations is presented. In natural flows, unsteadiness can result in extreme particle clustering, which is stronger than the clustering expected from averaged inertial turbulence effects.

physics.flu-dyn↗

arXiv2022

Clustering in laboratory and numerical turbulent swirling flows

We study the three-dimensional clustering of velocity stagnation points, of nulls of the vorticity and of the Lagrangian acceleration, and of inertial particles in turbulent flows at fixed Reynolds numbers, but under different large-scale flow geometries. To this end, we combine direct numerical simulations of homogeneous and isotropic turbulence and of the Taylor-Green flow, with particle tracking velocimetry in a von Kármán experiment. While flows have different topologies (as nulls cluster differently), particles behave similarly in all cases, indicating that Taylor-scale neutrally buoyant particles cluster as inertial particles.

physics.flu-dyn↗

arXiv2022

Multi-time structure functions and the Lagrangian scaling of turbulence

We define and characterize multi-time Lagrangian structure functions using data stemming from two swirling flows with mean flow and turbulent fluctuations: A Taylor-Green numerical flow, and a von Kármán laboratory experiment. Data is obtained from numerical integration of tracers in the former case, and from three-dimensional particle tracking velocimetry measurements in the latter. Multi-time statistics are shown to decrease the contamination of large scales in the inertial range scaling. A time scale at which contamination from the mean flow becomes dominant is identified, with this scale separating two different Lagrangian scaling ranges. The results from the multi-time structure functions also indicate that Lagrangian intermittency is not a result of large-scale flow effects. The multi-time Lagrangian structure functions can be used without prior knowledge of the forcing mechanisms or boundary conditions, allowing their application in different flow geometries.

physics.flu-dyn↗

arXiv2021

Broken mirror symmetry of tracer's trajectories in turbulence

Topological properties of physical systems play a crucial role in our understanding of nature, yet their experimental determination remains elusive. We show that the mean helicity, a dynamical invariant in ideal flows, quantitatively affects trajectories of fluid elements: the linking number of Lagrangian trajectories depends on the mean helicity. Thus, a global topological invariant and a topological number of fluid trajectories become related, and we provide an empirical expression linking them. The relation shows the existence of long-term memory in the trajectories: the links can be made of the trajectory up to a given time, with particles positions in the past. This property also allows experimental measurements of mean helicity.

physics.flu-dyn↗

arXiv2020

Velocity and acceleration statistics in particle-laden turbulent swirling flows

We present a comparison of different particles' velocity and acceleration statistics in two paradigmatic turbulent swirling flows: the von Kármán flow in a laboratory experiment, and the Taylor-Green flow in direct numerical simulations. Tracers, as well as inertial particles, are considered. Results indicate that, in spite of the differences in boundary conditions and forcing mechanisms, scaling properties and statistical quantities reveal similarities between both flows, pointing to new methods to calibrate and compare models for particles dynamics in numerical simulations, as well as to characterize the dynamics of particles in simulations and experiments.

physics.flu-dyn↗