arXiv subjects

James J. Riley

Publications and source records attributed to James J. Riley.

4 recordsLinked to original sources

arXiv2026

Turbulent diffusivity and effective rise velocity of buoyant particles in a free-surface boundary layer

Predicting the transport of buoyant particles in a free-surface boundary layer is important to the study of many environmental systems, including microplastics in the upper ocean. Current transport models, adapted from sediment transport theory, typically rely on assumptions of a quiescent rise velocity and gradient diffusion with an uncertain turbulent Schmidt number $Sc_t$. Here, we test this type of model against experiments by studying the vertical mixing of near-neutrally buoyant, finite-size spheres, rods, and disks in a wind-driven, wavy free-surface flow. We measure particle diffusivity directly from Lagrangian trajectories and compare against Eulerian concentration-based estimates. Overall, we find that particle buoyancy is the main control on the diffusivity, and that the diffusivity decreases as particle rise velocity grows relative to the turbulent fluctuations. These observations we find to be consistent with the crossing-trajectories theory, even in the presence of waves. In addition, we find that inferring the diffusivity from concentration profiles with an assumed quiescent rise velocity overestimates the diffusivity by up to a factor of $5$, consistent with effective rise velocities up to $80\%$ lower than the corresponding quiescent values. We also directly measure $Sc_t \approx 1$ for the neutrally-buoyant particles and $Sc_t > 1$ for the buoyant particles. Together, these experimental results demonstrate how standard model closures may be biased in both their diffusivities and rise velocities when applied to buoyant particles at the ocean surface.

physics.flu-dyn↗

arXiv2026

Evolution of passive scalar mixing layers in stratified and unstratified homogeneous turbulence

High-resolution large-eddy simulations of decaying stratified and unstratified homogeneous turbulence are used to understand the mixing of passive scalars in stably stratified flows. Two passive scalar mixing layers, one in the vertical direction and the other in the transverse direction, are a model for a plume that is very large relative to the length scale of the velocity. In the transverse direction, the evolution of the passive scalar is broadly similar in the stratified and unstratified cases, although it does spread slightly faster when stratified. Also, the intensity of the scalar fluctuations is higher in the stratified case, and the turbulent/non-turbulent interface is more intermittent. In the vertical direction, though, the stratified case has almost no mixing because the stratification prevents large-scale stirring. Initially, the stratified passive layer grows until its width is proportional to the vertical integral length of the horizontal velocity, which is itself constrained to maintain the vertical Froude number order one. After this early growth, there is little additional spreading of the passive scalar. Modelling of the stratified scalar flux in the transverse direction is done effectively with a one-constant model if the mean profile is known, and a two-constant model if the profile shape must be assumed. In the latter case, the model is good only if the scalar is in quasi-equilibrium with the velocity field such that the length scale of the scalar can be scaled from the kinetic energy. In this study, the Prandtl number of the active and passive scalars is 0.7. It is anticipated that the reverse buoyancy flux resulting from higher Prandtl numbers will affect the passive scalar mixing.

physics.flu-dyn↗

arXiv2024

Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying cases. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the URANS equations. Rather than attempting to model each term separately, it is attractive to explore the capability of machine learning to model groups of terms, i.e., to directly model the force balances. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: Long Short-Term Memory (LSTM) and Neural Ordinary Differential Equation (NODE). Both models perform accurately and are numerically stable in a posteriori tests. Furthermore, we explore the data requirements of the ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of higher-dimensional complex SST flows.

physics.flu-dyn↗

arXiv2015

Lagrangian coherent structures and inertial particle dynamics

In this work we investigate the dynamics of inertial particles using finite-time Lyapunov exponents (FTLE). In particular, we characterize the attractor and repeller structures underlying preferential concentration of inertial particles in terms of FTLE fields of the underlying carrier fluid. Inertial particles that are heavier than the ambient fluid (aerosols) attract onto ridges of the negative-time fluid FTLE. This negative-time FTLE ridge becomes a repeller for particles that are lighter than the carrier fluid (bubbles). We also examine the inertial FTLE (iFTLE) determined by the trajectories of inertial particles evolved using the Maxey-Riley equations with non-zero Stokes number and density ratio. Finally, we explore the low-pass filtering effect of Stokes number. These ideas are demonstrated on two-dimensional numerical simulations of the unsteady double gyre flow.

physics.flu-dyn↗