Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2102.11407

Signatures of elastoviscous buckling in the dilute rheology of stiff polymers

Brato Chakrabarti·Yanan Liu·Olivia du Roure·Anke Lindner·David Saintillan
View original source ↗Read source document ↗Export metadata ↓

Abstract

As a stiff polymer tumbles in shear flow, it experiences compressive viscous forces that can cause it to buckle and undergo a sequence of morphological transitions with increasing flow strength. We use numerical simulations to uncover the effects of these transitions on the steady shear rheology of a dilute suspension of stiff polymers. Our results agree with classic scalings for Brownian rods in relatively weak flows but depart from them above the buckling threshold. Signatures of elastoviscous buckling include enhanced shear thinning and an increase in the magnitude of normal stress differences. We discuss our findings in the light of past work on rigid Brownian rods and non-Brownian elastic fibers and highlight the subtle role of thermal fluctuations in triggering instabilities.

Explore related subjects

physics.flu-dyncond-mat.soft

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brato Chakrabarti, Yanan Liu, Olivia du Roure, Anke Lindner, David Saintillan. 2021-06-15. Signatures of elastoviscous buckling in the dilute rheology of stiff polymers. https://doi.org/10.1017/jfm.2021.383

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

KEEP EXPLORING

Related papers

arXiv2026

Lacunarity in a Two-Dimensional Atomizing Liquid Jet

Atomization stretches and folds the liquid--gas interface before fragmenting it into ligaments and droplets, making fractal measures a natural descriptor of the lacunarity of the final breakup state. We examine this idea in two-dimensional (2D) VOF-DNS of a liquid jet with adaptive mesh refinement in Basilisk. Box-counting of the full resolved interface does not yield a single scale-independent exponent. Instead, two scaling ranges appear, separated by a crossover near the box-counting level \(L_{\mathrm{box}}\simeq7\): coarser boxes measure the folded connected jet envelope, whereas finer boxes increasingly sample ligaments, droplets, and nearly smooth local interface segments. Decomposing the interface into detached droplets, ligaments, and the connected main body shows that the relevant effective dimension is structure dependent. Droplets are most lacunar with dimension below one, ligaments occupy an intermediate level, and the main body carries the largest coarse-scale dimension. This hierarchy persists over $Re_l=100$--$10^4$ at fixed $\We_g=200$. The lacunarity of droplets is reminiscent of that of the object positions in the night sky hypothesized by de Vaucouleurs.

physics.flu-dyn↗
arXiv2026

Generalizable turbulence closures across bluff-body shapes by PINN-based solver-agnostic training

Data-driven turbulence closures are commonly calibrated by embedding a CFD solver within an inverse procedure that requires a converged flow solution at every optimization iteration, which is fragile and costly. We instead formulate closure identification as a PDE-constrained, data-driven inverse problem within a physics-informed neural network (PINN), and subsequently stabilize the inferred closure for deployment in a numerical solver. This separation eliminates the training cost of repeated CFD solves and associated adjoint or ensemble evaluations, providing substantial flexibility to investigate alternative closure forms. Using this flexibility, we develop four closures: three model the Reynolds stress on a realizable tensor basis---a local map, a non-local model transporting the turbulent kinetic energy and recovering the out-of-plane normal stress, and the same with a learned length scale $\ell$---and a fourth models the Reynolds force $\mathbf{F}=-\nabla\cdot\boldsymbolτ$ directly, free of the realizability constraint. All four are trained across six two-dimensional bluff-body wakes at $Re=10^4$ and deployed frozen in a standard finite-element solver, stabilized primarily by input-gradient smoothing. Under a strict leave-one-shape-out (LOSO) protocol, all four reduce the field errors of a steady SST $k$-$ω$ baseline, most strongly on the mean velocity. The learned-length-scale closure is most accurate on the stress fields, while the force model generalizes best on the mean velocity and drag (LOSO drag error $\sim 7\%$). The closures also train efficiently on Particle Image Velocimetry data, enabling geometries intractable for DNS.

physics.flu-dyn↗
arXiv2026

Effect of leading edge geometry on aerodynamic performance of a flat plate

Flow separation around flat plate is influenced by the thickness and leading edge geometry, which creates the shear stresses and pressure distribution manifesting into unsteady aerodynamic forces. Leading edge geometries have been widely reported but the influence of leading edge curvature at low Reynolds numbers (Re) have found less attention. The present study uses two dimensional numerical simulation to investigate the effect of a hemispherical curvature (protuberance) at the leading edge and reports how vortex shedding, shedding frequencies and aerodynamic forces are affected over a range of angles of attack ($0^\circ \leq α\leq 25^\circ$) at Re = 1000. Thicker vortical regions are formed near the plate with curvature at the leading edge. Onset of shedding is noted at angle of attack of $5^\circ$ for the plate with curvature at the leading edge and it gets delayed for simple flat plate. The temporal force coefficients on the plates show multiple frequencies at higher angles of attack. The plate with curvature at the leading edge has a glide ratio of 1 at $0^\circ$ angle of attack and has equivalent glide ratio to that of simple flat plate at higher angles of attack. The simple flat plate has better glide ratios at moderate angles of attack like $5^\circ, 10^\circ$ and $15^\circ$. The results will help in making engineering informed design decisions for smaller unmanned aerial/aquatic vehicles cruising at low Reynolds numbers.

physics.flu-dyn↗