arXiv subjects

Jessica K. Shang

Publications and source records attributed to Jessica K. Shang.

7 recordsLinked to original sources

arXiv2024

Radiation and Heat Transport in Divergent Shock-Bubble Interactions

Shock-bubble interactions (SBI) are important across a wide range of physical systems. In inertial confinement fusion, interactions between laser-driven shocks and micro-voids in both ablators and foam targets generate instabilities that are a major obstacle in achieving ignition. Experiments imaging the collapse of such voids at high energy densities (HED) are constrained by spatial and temporal resolution, making simulations a vital tool in understanding these systems. In this study, we benchmark several radiation and thermal transport models in the xRAGE hydrodynamic code against experimental images of a collapsing mesoscale void during the passage of a 300 GPa shock. We also quantitatively examine the role of transport physics in the evolution of the SBI. This allows us to understand the dynamics of the interaction at timescales shorter than experimental imaging framerates. We find that all radiation models examined reproduce empirical shock velocities within experimental error. Radiation transport is found to reduce shock pressures by providing an additional energy pathway in the ablation region, but this effect is small ($\sim$1\% of total shock pressure). Employing a flux-limited Spitzer model for heat conduction, we find that flux limiters between 0.03 and 0.10 produce agreement with experimental velocities, suggesting that the system is well-within the Spitzer regime. Higher heat conduction is found to lower temperatures in the ablated plasma and to prevent secondary shocks at the ablation front, resulting in weaker primary shocks. Finally, we confirm that the SBI-driven instabilities observed in the HED regime are baroclinically driven, as in the low energy case.

physics.plasm-ph↗

arXiv2022

Effective Drift Velocity from Turbulent Transport by Vorticity

We highlight the differing roles of vorticity and strain in the transport of coarse-grained scalars at length-scales larger than $\ell$ by smaller scale (subscale) turbulence. %subscale flux/stress which appear in the evolution of coarse-grained (resolved) scalars/momentum account for the effect of (subgrid) scales smaller than the coarse-graining length $\ell$. We use the first term in a multiscale gradient expansion due to Eyink \cite{Eyink06a}, which exhibits excellent correlation with the exact subscale physics when the partitioning length $\ell$ is any scale smaller than that of the spectral peak. We show that unlike subscale strain, which acts as an anisotropic diffusion/anti-diffusion tensor, subscale vorticity's contribution is solely a conservative advection of coarse-grained quantities by an eddy-induced non-divergent velocity, $\bv_*$, that is proportional to the curl of vorticity. Therefore, material (Lagrangian) advection of coarse-grained quantities is accomplished not by the coarse-grained flow velocity, $\OL\bu_\ell$, but by the effective velocity, $\OL\bu_\ell+\bv_*$, the physics of which may improve commonly used LES models.

physics.flu-dyn↗

arXiv2021

Scaling of Turbulent Viscosity and Resistivity: Extracting a Scale-dependent Turbulent Magnetic Prandtl Number

Turbulent viscosity $ν_t$ and resistivity $η_t$ are perhaps the simplest models for turbulent transport of angular momentum and magnetic fields, respectively. The associated turbulent magnetic Prandtl number $Pr_t\equiv ν_t/η_t$ has been well recognized to determine the final magnetic configuration of accretion disks. Here, we present an approach to determining these ''effective transport'' coefficients acting at different length-scales using coarse-graining and recent results on decoupled kinetic and magnetic energy cascades [Bian & Aluie 2019]. By analyzing the kinetic and magnetic energy cascades from a suite of high-resolution simulations, we show that our definitions of $ν_t$, $η_t$, and $Pr_t$ have power-law scalings in the ''decoupled range.'' We observe that $Pr_t\approx1 \text{~to~}2$ at the smallest inertial-inductive scales, increasing to $\approx 5$ at the largest scales. However, based on physical considerations, our analysis suggests that $Pr_t$ has to become scale-independent and of order unity in the decoupled range at sufficiently high Reynolds numbers (or grid-resolution), and that the power-law scaling exponents of velocity and magnetic spectra become equal. In addition to implications to astrophysical systems, the scale-dependent turbulent transport coefficients offer a guide for large eddy simulation modeling.

physics.flu-dyn↗

arXiv2020

Peristaltic pumping in thin, non-axisymmetric, annular tubes

Two-dimensional laminar flow of a viscous fluid induced by peristalsis due to a moving wall wave has been studied previously for a rectangular channel, a circular tube, and a concentric circular annulus. Here we study peristaltic flow in a non-axisymmetric annular tube, where the flow is three dimensional, with azimuthal motions. This geometry is motivated by experimental observations of cerebrospinal fluid flow along perivascular spaces (PVSs) surrounding arteries in the brain, which is at least partially driven by peristaltic pumping. These PVSs are well matched, in cross-section, by an adjustable model consisting of an inner circle (arterial wall) and an outer ellipse (outer edge of the PVS), not necessarily concentric. We use this model, which may have other applications, as a basis for numerical simulations of peristaltic flow. We use a finite-element scheme to compute the flow driven by a propagating sinusoidal radial displacement of the inner wall. Unlike peristaltic flow in a concentric circular annulus, the flow is fully three-dimensional, with streamlines wiggling in both the radial and axial directions. We examine the dependence of the flow on the elongation of the outer elliptical wall and on the eccentricity of the configuration. We find that time-averaged volumetric flow decreases with increasing ellipticity or eccentricity. Azimuthal pressure variations, caused by the wall wave, drive an oscillatory azimuthal flow in and out of the narrower gaps. The additional shearing motion in the azimuthal direction will enhance Taylor dispersion in these flows, an effect that might have practical applications.

physics.flu-dyn↗

arXiv2019

Robust Principal Component Analysis for Modal Decomposition of Corrupt Fluid Flows

Modal analysis techniques are used to identify patterns and develop reduced-order models in a variety of fluid applications. However, experimentally acquired flow fields may be corrupted with incorrect and missing entries, which may degrade modal decomposition. Here we use robust principal component analysis (RPCA) to improve the quality of flow field data by leveraging global coherent structures to identify and replace spurious data points. RPCA is a robust variant of principal component analysis (PCA), also known as proper orthogonal decomposition (POD) in fluids, that decomposes a data matrix into the sum of a low-rank matrix containing coherent structures and a sparse matrix of outliers and corrupt entries. We apply RPCA filtering to a range of fluid simulations and experiments of varying complexities and assess the accuracy of low-rank structure recovery. First, we analyze direct numerical simulations of flow past a circular cylinder at Reynolds number 100 with artificial outliers, alongside similar PIV measurements at Reynolds number 413. Next, we apply RPCA filtering to a turbulent channel flow simulation from the Johns Hopkins Turbulence database, demonstrating that dominant coherent structures are preserved in the low-rank matrix. Finally, we investigate PIV measurements behind a two-bladed cross-flow turbine that exhibits both broadband and coherent phenomena. In all cases, we find that RPCA filtering extracts dominant coherent structures and identifies and fills in incorrect or missing measurements. The performance is particularly striking when flow fields are analyzed using dynamic mode decomposition, which is sensitive to noise and outliers.

physics.flu-dyn↗

arXiv2018

A Light-weight Vibrational Motor Powered Recoil Robot that Hops Rapidly Across Granular Media

A 1 cm coin vibrational motor fixed to the center of a 4 cm square foam platform moves rapidly across granular media (poppy seeds, millet, corn meal) at a speed of up to 30 cm/s, or about 5 body lengths/s. Fast speeds are achieved with dimensionless acceleration number, similar to a Froude number, up to 50, allowing the light-weight 1.4 g mechanism to remain above the substrate, levitated and propelled by its kicks off the surface. The mechanism is low cost and moves without any external moving parts. With 2 s exposures we photograph the trajectory of the mechanism using an LED blocked except for a pin-hole and fixed to the mechanism. Trajectories can exhibit period doubling phenomena similar to a ball bouncing on a vibrating table top. A two dimensional numerical model gives similar trajectories, though a vertical drag force is required to keep the mechanism height low. We attribute the vertical drag force to aerodynamic suction from air flow below the mechanism base and through the granular substrate. Our numerical model suggests that speed is maximized when the mechanism is prevented from jumping high off the surface. In this way the mechanism resembles a galloping or jumping animal whose body remains nearly at the same height above the ground during its gait.

cond-mat.soft↗

arXiv2014

Toward compressed DMD: spectral analysis of fluid flows using sub-Nyquist-rate PIV data

Dynamic mode decomposition (DMD) is a powerful and increasingly popular tool for performing spectral analysis of fluid flows. However, it requires data that satisfy the Nyquist-Shannon sampling criterion. In many fluid flow experiments, such data are impossible to capture. We propose a new approach that combines ideas from DMD and compressed sensing. Given a vector-valued signal, we take measurements randomly in time (at a sub-Nyquist rate) and project the data onto a low-dimensional subspace. We then use compressed sensing to identify the dominant frequencies in the signal and their corresponding modes. We demonstrate this method using two examples, analyzing both an artificially constructed test dataset and particle image velocimetry data collected from the flow past a cylinder. In each case, our method correctly identifies the characteristic frequencies and oscillatory modes dominating the signal, proving the proposed method to be a capable tool for spectral analysis using sub-Nyquist-rate sampling.

physics.flu-dyn↗