Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.13796

Particle dynamics in a microfluidic T-junction vortex flow: the role of history force

Matteo Chiatto·Luigi de Luca·Howard A. Stone·Francesco Grasso
View original source ↗Read source document ↗Export metadata ↓

Abstract

The motion of inertial particles suspended in a carrier fluid, together with the dynamics of the underlying vortical structures, is investigated in a square microfluidic T-junction. The coherent structures forming near the branching strongly affect the particle distribution and may promote particle trapping around the T-region. Single-phase numerical simulations of the unperturbed base flow are combined with integration of the particle force balance equation, which, unlike most previous studies, includes the Basset-Boussinesq (BB) history force. The flow topology is analysed for $Re=500$ and $570$, while particle properties are varied through the Stokes number $St$ and the particle-to-fluid density ratio $ρ$. At $Re=500$ the base flow exhibits a steady vortex pair, whereas at $Re=570$ a Hopf bifurcation gives rise to a helical oscillation of the primary vortex pair characterized by periodic breathing. The BB history force has a non-negligible impact over the whole range of $(ρ, St)$ pairs investigated. It acts as an additional viscous force which, together with the drag of the relative motion, opposes the pressure-gradient force, widening particle spiral trajectories radially and elongating them axially. Its inclusion reduces the basin of attraction, indicating that memory effects promote particle displacement away from the vortex core and weaken trapping. At $Re=570$, spectral proper orthogonal decomposition characterizes the breathing of the primary vortex pair. Particles injected at a given location interact with the vortical structure at different phases of the cycle depending on the injection time, producing substantially different trajectories. This phase dependence is reflected in the shape and extent of the basin of attraction, quantified through the trapping probability over the inlet section and the probability density functions of the force components.

Explore related subjects

physics.flu-dyn

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matteo Chiatto, Luigi de Luca, Howard A. Stone, Francesco Grasso. 2026-10-09. Particle dynamics in a microfluidic T-junction vortex flow: the role of history force. https://arxiv.org/abs/2610.13796

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Fast localized urban microclimate emulator: wind and temperatures in unseen urban morphologies

Urban microclimate, encompassing wind and temperature fields shaped by building geometry, strongly affects building energy use, pedestrian wind comfort, pollutant dispersion, the urban heat island, and public health. Conventional Computational Fluid Dynamics (CFD) is computationally prohibitive for rapid assessments, while many deep learning approaches require extensive training data and struggle with generalization to unseen configurations. In this work, we present the Fast Localized Urban Microclimate Emulator (FLUME), a rapid, data-efficient, and geometry-aware framework based on a Fourier neural operator for predicting three-dimensional wind and temperature fields. FLUME assumes that, under prescribed inflow and thermal forcing, much of the spatial variation of these mean fields is controlled by surrounding building geometry. The framework introduces a Multi-Directional Distance Feature (MDDF), which captures geometry over adaptive, visibility-defined spatial scales. By decomposing simulations of only 23 urban blocks into smaller 3D patches enriched with precomputed MDDF information, FLUME enables learning from limited CFD data. The model achieves mean absolute errors of 0.2 m/s for streamwise velocity and 0.19$^{\circ}$C for temperature on unseen urban configurations. A deep ensemble provides a practical proxy for epistemic uncertainty, and conformal prediction transforms these estimates into calibrated prediction intervals. Ensemble spread is positively related to local prediction error, with greater uncertainty in roof-level shear layers, building wakes, and vertical velocity near the ground. These results indicate FLUME's potential for rapid scenario assessment in urban design and urban climate studies.

physics.flu-dyn↗
arXiv2026

An Omni-Temporal Framework for Hydrodynamic Dispersion and Reaction in Porous Media

Many applications involve porous-media transport driven on time scales comparable to the pore-scale diffusion time, for which classical pseudo-steady dispersion models based on time-invariant transport coefficients may become inaccurate. While frequency-dependent dispersion has been studied in non-reactive porous media, a corresponding framework that simultaneously accounts for advection, diffusion, and heterogeneous interfacial reaction remains lacking. We address this gap by introducing an omni-temporal framework based on volume averaging of the frequency-domain pore-scale advection-diffusion equation subject to a Robin boundary condition spanning the full range of interfacial reactivity. The resulting upscaled formulation yields four frequency-dependent transfer functions: a dispersion tensor, an advection-suppression vector, a spectral Sherwood number, and a reactivity-bias vector, with the reactivity-bias vector introduced here for the first time. Analytical solutions for fully developed flow between parallel plates and through circular tubes reveal distinct kinetics- and diffusion-limited regimes, including capacitive and semi-infinite Warburg-type frequency responses. Comparisons of transient breakthrough curves with direct numerical simulations demonstrate substantial improvement over conventional pseudo-steady dispersion, particularly for short-duration pulses. Application to a periodic array of square rods under cross flow further demonstrates the frameworks applicability beyond canonical geometries.

physics.flu-dyn↗
arXiv2026

Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach

A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the subfilter-scale (SFS) stress tensor can be computed. A data-driven super-resolution generative adversarial network (SR-GAN) is employed to upsample the grid-filtered velocity fields by a factor of two, enabling the evaluation of both scale-similarity and the dynamic Smagorinsky contributions. A priori analyses of forced homogeneous isotropic turbulence (HIT) show that the SR-GAN accurately reconstructs flow features and generalizes well across different filter sizes and higher Reynolds numbers within the same flow configuration, even for unseen input fields. The DMSRM reproduces SFS stresses and energy dissipation more accurately than the traditional DMM. A posteriori LES calculations further confirm that DMSRM predicts the energy spectrum and intermittency more accurately than DMM, even for different LES grid-scale resolutions and higher Reynolds numbers than those used for training within the same forced HIT flow configuration. Unlike DMM, DMSRM yields realistic backscatter and physically consistent SFS energy dissipation. These improvements arise from the physically accurate super-resolved fields generated by the SR-GAN, from which SFS stresses are directly computed. The result is a closure that accurately reproduces stress magnitudes and dissipation while reducing reliance on additional dissipation from the dynamic term. The DMSRM formulation achieves a balance of physical fidelity, robustness, and computational efficiency, offering a promising alternative to traditional DMMs for turbulence LES modeling.

physics.flu-dyn↗