arXiv subjects

Andrew D. Bragg

Publications and source records attributed to Andrew D. Bragg.

At least 19 recordsLinked to original sources

arXiv2026

Impact of alignments between fluctuating and mean density gradients on the scale-dependent energetics of stably stratified turbulence

Non-trivial alignments between vorticity and the strain-rate tensor play an important role in the evolution of velocity gradients and the energy cascade in isotropic turbulence. Here we explore how alignments between the fluctuating and mean density gradients impact the mechanisms governing the turbulent kinetic energy (TKE) and available potential energy (APE) across scales in stably stratified turbulence. This is motivated by analytical results that demonstrate a connection between them, and is conducted using direct numerical simulations (DNS) of statistically stationary, stably stratified turbulence for $Pr = 1, 7, 50$ in the strongly stratified regime. After demonstrating how the gradient field alignments depend on scale and $Pr$, we show that the alignments are intimately connected to the reversal of the buoyancy flux at small-scales, and that regions of strong alignment and misalignment correspond to regions where the horizontal TKE inter-scale flux becomes weak. The same is also true of the APE flux, except that at larger scales, regions of strong alignment are associated with an upscale APE flux. The TKE and APE dissipation rates, and the mixing coefficient also show a strong dependence on the alignment, especially for $Pr=1$. Finally, we explore the connection between the local alignment and stability of the flow, and we find a non-trivial relationship, with regions of strong alignment surprisingly occurring most often in stable regions. This demonstrates that the dynamical significance of the alignments on the flow energetics cannot be understood through a simple connection between the local alignments and local stability of the flow.

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arXiv2025

Mechanism generating reverse buoyancy flux at the small scales of stably stratified turbulence

Previous studies have shown that at the small-scales of stably stratified turbulence, the scale-dependent buoyancy flux reverses sign, such that there is a conversion of turbulent potential energy (TPE) back into turbulent kinetic energy (TKE) at these scales. Moreover, the magnitude of the reverse flux becomes stronger with increasing Prandtl number $Pr$. Using a filtering analysis we demonstrate analytically how this flux reversal is connected to the mechanism identified in Bragg \& de Bruyn Kops (JFM 2024 Vol 991 A10) that is responsible for the surprising observation that the TKE dissipation rate increases while the TPE dissipation rate decreases with increasing $Pr$ in stratified turbulence. The mechanism identified by Bragg \& de Bruyn Kops, which is connected to the formation of ramp-cliff structures in the density field, is shown to give the scale-local contribution to the buoyancy flux. At the smallest-scales this local contribution dominates and explains the flux reversal, while at larger scales a non-local contribution is important. Direct numerical simulations (DNS) of 3D statistically stationary, stably stratified turbulence in the strongly stratified regime confirm the theoretical analysis, and indicate that while on average the local contribution only dominates the buoyancy flux at the smallest scales, it remains strongly correlated with the buoyancy flux at all scales. The results show that ramp-cliffs are not only connected to the reversal of the local buoyancy flux but also the non-local part. At the small scales (approximately below the Ozmidov scale), ramp structures contribute exclusively to reverse buoyancy flux events, whereas cliff structures contribute to both forward and reverse buoyancy flux events.

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arXiv2025

Taylor dispersion of bubble swarms rising in quiescent liquid

We study the dispersion of bubble swarms rising in initially quiescent water using 3D Lagrangian tracking of deformable bubbles and tracer particles in an octagonal bubble column. First, we compare the dispersion inside bubble swarms with that for single-bubble cases and find that the horizontal mean squared displacement (MSD) in the swarm cases exhibits oscillations around the asymptotic scaling predicted for a diffusive regime. This occurs due to wake-induced bubble motion, however, the oscillatory behaviour is heavily damped compared to the single-bubble cases due to the presence of bubble-induced turbulence (BIT) and bubble-bubble interactions in the swarm. The vertical MSD in bubble swarms is nearly an order of magnitude faster than the single-bubble cases, due to the much higher vertical fluctuating bubble velocities in the swarms. We also investigate tracer dispersion in BIT and find that concerning the time to transition away from the ballistic regime, larger bubbles with a higher gas void fraction transition earlier than tracers, consistent with Mathai et al. (\textit{Phys. Rev. Lett.} 121, 054501, 2018). However, for bubble swarms with smaller bubbles and a lower gas void fraction, they transition at the same time. This differing behavior is due to the turbulence being more well-mixed for the larger bubble case, whereas for the smaller bubble case the tracer dispersion is highly dependent on the wake fluctuations generated by the oscillating motion of nearby bubbles.

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arXiv2025

Kolmogorov scaling in bubble-induced turbulence

Experiments using 3D Lagrangian tracking are used to investigate Kolmogorov scaling below the bubble size in bubble-induced turbulence (BIT). Second and third order structure functions reveal approximate Kolmogorov scaling for homogeneous bubble swarms. A new scaling for the kinetic energy dissipation rate is derived and shown to be in excellent agreement with the data. Using this we predict the scale separation below the bubble size as a function of the parameters and find that a large inertial range is not possible in BIT since bubbles of the required size would quickly break down.

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arXiv2024

Effects of settling on inertial particle slip velocity statistics in wall bounded flows

Developing reduced order models for the transport of solid particles in turbulence typically requires a statistical description of the particle-turbulence interactions. In this work, we utilize a statistical framework to derive continuum equations for the moments of the slip velocity of inertial settling Lagrangian particles in a turbulent boundary layer. Using coupled Eulerian-Lagrangian direct numerical simulations, we then identify the dominant mechanisms controlling the slip velocity variance, and find that for a range of St+, Sv+, and Re, the slip variance is primarily controlled by local differences between the "seen" variance and the particle velocity variance, while terms appearing due to the inhomogeneity of the turbulence are sub-leading until Sv+ becomes large. We also consider several comparative metrics to assess the relative magnitudes of the fluctuating slip velocity and the mean slip velocity, and we find that the vertical mean slip increases rapidly with Sv+, rendering the variance relatively small -- an effect found to be most substantial for Sv+>1. Finally, we compare the results to a model of the acceleration variance Berk and Coletti (2021) based the concept of a response function described in Csanady (1963), highlighting the role of the crossing trajectories mechanism. We find that while there is good agreement for low Sv+, systematic errors remain, possibly due to implicit non-local effects arising from rapid particle settling and inhomogeneous turbulence. We conclude with a discussion of the implications of this work for modeling the transport of coarse dust grains in the atmospheric surface layer.

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arXiv2024

Asymptotic analysis of mixing in stratified turbulent flows, and the conditions for an inertial sub-range

In an important study, Maffioli et al. (J. Fluid Mech., Vol. 794 , 2016) used a scaling analysis to predict that in the weakly stratified flow regime $Fr_h\gg1$ ($Fr_h$ is the horizontal Froude number), the mixing coefficient $Γ$ (defined as the ratio of the dissipation rates of potential to kinetic energy) scales as $Γ\sim O(Fr_h^{-2})$. Direct numerical simulations confirmed this result, and also indicated that for the strongly stratified regime $Fr_h\ll 1$, $Γ\sim O(1)$. Furthermore, the study argued that $Γ$ does not depend on the buoyancy Reynolds number $Re_b$, but only on $Fr_h$. We present an asymptotic analysis to predict theoretically how $Γ$ should behave for $Fr_h\ll1$ and $Fr_h\gg1$ in the limit $Re_b\to\infty$. To correctly handle the singular limit $Re_b\to\infty$ we perform the asymptotic analysis on the filtered Boussinesq-Navier-Stokes equations, and demonstrate the precise sense in which the inviscid scaling analysis of Billant \& Chomaz (Phys. Fluids, vol. 13, 1645-1651, 2001) applies to viscous flows with $Re_b\to\infty$. The analysis yields $Γ\sim O(Fr_h^{-2}(1+Fr_h^{-2}))$ for $Fr_h\gg1$ and $Γ\sim O(1+Fr_h^{2})$ for $Fr_h\ll 1$, providing a theoretical basis for the numerical observation made by Maffioli et al, as well as predicting the sub-leading behavior. Our analysis also shows that the Ozmidov scale $L_O$ does not describe the scale below which buoyancy forces are sub-leading, which is instead given by $O(Fr_h^{1/2} L_O)$, and that the condition for there to be an inertial sub-range when $Fr_h\ll 1$ is not $Re_b\gg1$, but the more restrictive condition $Re_b\gg Fr_h^{-4/3}$.

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arXiv2023

Investigating the parametric dependence of the impact of two-way coupling on inertial particle settling in turbulence

Tom et al.\ (J.\ Fluid Mech.\ 947, A7, 2022) investigated the impact of two-way coupling (2WC) on particle settling in turbulence. For the limited parameter choices explored, it was found that 2WC substantially enhances particle settling compared to the one-way coupled (1WC) case, even at low mass loading $Φ_m$. Moreover, contrary to previous claims, it was demonstrated that preferential sweeping remains the mechanism responsible for the particles settling faster than the Stokes settling velocity in 2WC flows. However, crucial questions remain: 1) how small must $Φ_m$ be for the effects of 2WC on particle settling to be negligible? 2) does the preferential sweeping mechanism remain relevant in 2WC flows as $Φ_m$ is increased? To answer these, we explore a much broader portion of the parameter space, and our simulations cover cases where the impact of 2WC on the global fluid statistics ranges from negligible to strong. We find that even for $Φ_m=7.5\times 10^{-3}$, 2WC can noticeably increase the settling for some choices of the Stokes and Froude numbers. We also demonstrate that even when $Φ_m$ is large enough for the global fluid statistics to be strongly affected by the particles, preferential sweeping is still the mechanism responsible for the enhanced particle settling. The difference between the 1WC and 2WC cases is that, in the latter the particles are not merely swept around the downward-moving side of vortices, but they also drag the fluid with them as they move down.

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arXiv2023

Understanding the effect of Prandtl number on momentum and scalar mixing rates in neutral and stably stratified flows using gradient field dynamics

Recently, direct numerical simulations (DNS) of stably stratified turbulence have shown that as the Prandtl number ($Pr$) is increased from 1 to 7, the mean turbulent potential energy dissipation rate (TPE-DR) drops dramatically, while the mean turbulent kinetic energy dissipation rate (TKE-DR) increases significantly. Through an analysis of the equations governing the fluctuating velocity and density gradients we provide a mechanistic explanation for this surprising behavior and test the predictions using DNS. We show that the mean density gradient gives rise to a mechanism that opposes the production of fluctuating density gradients, and this is connected to the emergence of ramp-cliffs. The same term appears in the velocity gradient equation but with the opposite sign, and is the contribution from buoyancy. This term is ultimately the reason why the TPE-DR reduces while the TKE-DR increases with increasing $Pr$. Our analysis also predicts that the effects of buoyancy on the smallest scales of the flow become stronger as $Pr$ is increased, and this is confirmed by our DNS data. A consequence of this is that the standard buoyancy Reynolds number does not correctly estimate the impact of buoyancy at the smallest scales when $Pr$ deviates from 1, and we derive a suitable alternative parameter. Finally, an analysis of the filtered gradient equations reveals that the mean density gradient term changes sign at sufficiently large scales, such that buoyancy acts as a source for velocity gradients at small scales, but as a sink at large scales.

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arXiv2023

Comparing phase-space and phenomenological modeling approaches for Lagrangian particles settling in a turbulent boundary layer

Under the right circumstances, inertial particles (such as sand or dust) settling through the atmospheric boundary layer can experience a net enhancement in their average settling velocity due to their inertia. Since this enhancement arises due to their interactions with the surrounding turbulence it must be modelled at coarse scales. Models for the enhanced settling velocity (or deposition) of the dispersed phase that find practical use in mesoscale weather models are often ad hoc or are built on phenomenological closure assumptions, meaning that the general deposition rate of particle is a key uncertainty. Instead of taking a phenomenological approach, exact phase space methods can be used to model the physical mechanisms responsible for the enhanced settling, and a more general parameterization of the enhanced settling of inertial particles can be built. In this work, we use direct numerical simulations (DNS) and phase space methods to evaluate the efficacy of phenomenological modelling approaches for the enhanced settling velocity of inertial particles with varying friction Stokes numbers and settling velocity parameters. We use the DNS data to estimate profiles of a drift-diffusion based parameterization of the fluid velocity sampled by the particles, which is key for determining the settling velocity behaviour of particles with low to moderate Stokes number. We find that by increasing the settling velocity parameter at moderate friction Stokes number, the magnitude of preferential sweeping is modified, and this behaviour is explained by the drift component. We then use these profiles to argue that the eddy-diffusivity-like closure used in phenomenological models is incomplete, relying on inadequate empirical corrections. Finally, we discuss opportunities for reconciling exact phase space approaches with simpler phenomenological approaches for use in coarse-scale weather models.

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arXiv2023

Fate of bubble clusters rising in a quiescent liquid

We use experiments to study the evolution of bubble clusters in a swarm of freely rising, deformable bubbles. A new machine learning-aided algorithm allows us to identify and track bubbles in clusters and measure the cluster lifetimes. The results indicate that contamination in the carrier liquid can enhance the formation of bubble clusters and prolong the cluster lifetimes. The mean bubble rise velocities conditioned on the bubble cluster size are also explored, and we find a positive correlation between the cluster size and the rise speed of the bubbles in the cluster, with clustered bubbles rising up to $20\%$ faster than unclustered bubbles.

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arXiv2022

Logarithmic scaling of higher-order temperature moments in the atmospheric surface layer

A generalized logarithmic law for high-order moments of passive scalars is proposed for turbulent boundary layers. This law is analogous to the generalized log law that has been proposed for high-order moments of the turbulent longitudinal velocity and is derived by combining the random sweeping decorrelation hypothesis with a spectral model informed by the attached eddy hypothesis. The proposed theory predicts that the high-order moments of passive scalar fluctuations within the inertial sublayer will vary logarithmically with wall-normal distance ($z$). The proposed theory is evaluated using high frequency time-series measurements of temperature and streamwise velocity fluctuations obtained in the first meter of the atmospheric surface layer (ASL) under near-neutral thermal stratification. The logarithmic dependence with $z$ within the inertial sublayer is observed in both the air temperature and velocity moments, with good agreement to the predictions from the proposed theory. Surprisingly, the proposed theory appears to be as, if not more, valid for transported passive scalars than for the longitudinal velocity.

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arXiv2022

Effects of surfactants on bubble-induced turbulence

We use experiments to explore the effect of surfactants on bubble-induced turbulence (BIT) at different scales, considering how the bubbles affect the flow kinetic energy, anisotropy and extreme events. To this end, high-resolution Particle Shadow Velocimetry measurements are carried out in a bubble column in which the flow is generated by bubble swarms rising in water for two different bubble diameters ($3$ mm $\&$ $4$ mm) and moderate gas volume fractions ($0.5\%\sim1.3\%$). To contaminate the flow, different amounts of 1-Pentanol were added to the flow, leading to different bubble shapes and surface boundary conditions. The results reveal that with increasing surfactant concentration, the BIT generated increases in strength, even though bubbles of a given size rise more slowly with surfactants. We also find that the level of anisotropy in the flow is enhanced with increasing surfactant concentration for bubbles of the same size, and that for the same surfactant concentration, smaller bubbles generate stronger anisotropy in the flow. Concerning the intermittency quantified by the normalized probability density functions of the fluid velocity increments, our results indicate that extreme values in the velocity increments become more probable with decreasing surfactant concentration for cases with smaller bubbles and low gas void fraction, while the effect of the surfactant is much weaker for cases with larger bubble and higher void fractions.

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arXiv2022

Lagrangian model for passive scalar gradients in turbulence

The equation for the fluid velocity gradient along a Lagrangian trajectory immediately follows from the Navier-Stokes equation. However, such an equation involves two terms that cannot be determined from the velocity gradient along the chosen Lagrangian path: the pressure Hessian and the viscous Laplacian. A recent model handles these unclosed terms using a multi-level version of the recent deformation of Gaussian fields (RDGF) closure (Johnson \& Meneveau, Phys.~Rev.~Fluids, 2017). This model is in remarkable agreement with DNS data and works for arbitrary Taylor Reynolds numbers $\Rey_λ$. Inspired by this, we develop a Lagrangian model for passive scalar gradients in isotropic turbulence. The equation for passive scalar gradients also involves an unclosed term in the Lagrangian frame, namely the scalar gradient diffusion term, which we model using the RDGF approach. However, comparisons of the statistics obtained from this model with direct numerical simulation (DNS) data reveal substantial errors due to erroneously large fluctuations generated by the model. We address this defect by incorporating into the closure approximation information regarding the scalar gradient production along the local trajectory history of the particle. This modified model makes predictions for the scalar gradients, their production rates, and alignments with the strain-rate eigenvectors that are in very good agreement with DNS data. However, while the model yields valid predictions up to around $\Rey_λ\approx 500$, beyond this, the model breaks down.

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arXiv2022

A stochastic model for the residence time of solid particles in turbulent Rayleigh-Bénard Flow

The Pi Chamber, located at Michigan Technological University, generates moist turbulent Rayleigh-Bénard flow in order to replicate steady-state cloud conditions. We take inspiration from this setup and consider a particle-laden, convectively-driven turbulent flow using direct numerical simulation (DNS). The aim of our study is to develop a simple stochastic model that can accurately describe the residence times of the particles in the flow, this time being determined by the complex competition between the gravitational settling of the particles, and the interaction of the particles with the turbulent structures in the flow. A simple conceptual picture underlies the stochastic model, namely that the particles take repeated trips between the top and bottom boundaries, driven by the convective cells that occur in Rayleigh-Bénard turbulence, and that their residence times are determined by the time it takes to complete one of these trips, which varies from one trip to another, and the probability of falling out to the bottom boundary after each trip. Despite the simplicity of the model, it yields quantitatively accurate predictions of the distribution of the particle residence times in the flow. We independently vary the Stokes numbers and settling velocities in order to shed light on the independent roles that gravity and inertia play in governing these residence times.

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arXiv2022

The effect of tilt on turbulent thermal convection for a heated soap bubble

We use direct numerical simulation (DNS) to explore the effect of tilt on two-dimensional turbulent thermal convection on a half-soap bubble that is heated at its equator.In the DNS, the bubble is tilted by an angle $δ\in[0^{\circ},90^{\circ}]$, the Rayleigh number is varied between $Ra\in[3\times10^6, 3\times10^9]$, and the Prandlt number is fixed at $Pr=7$.The DNS reveals two qualitatively different flow regimes: the dynamic plume regime (DPR) and the stable plume regime (SPR).In the DPR, small dynamic plumes constantly emerge from random locations on the equator and dissipate on the bubble.In the SPR, the flow is dominated by a single large and stable plume rising from the lower edge of the bubble.The scaling behaviour of the Nusselt number $Nu$ and Reynolds number $Re$ are different in these two regimes,with $Nu\propto Ra^{0.3}$ for the DPR and $Nu\propto Ra^{0.24}$ for the SPR. Concerning $Re$, the scaling in the DPR lies between $Re\propto Ra^{0.48}$ and $Re\propto Ra^{0.53}$ depending on $Ra$ and $δ$,while in the SPR, the scaling lies between $Re\propto Ra^{0.44}$ and $Re\propto Ra^{0.45}$ depending on $δ$.The turbulent thermal and kinetic energy dissipation rates ($ε_{T^{\prime}}$ and $ε_{u^{\prime}}$, respectively) are also very different in the DPR and SPR.The probability density functions (PDF) of the normalized $\logε_{T^{\prime}}$ and $\logε_{u^{\prime}}$ are close to a Gaussian PDF for small fluctuations, but deviate considerably from a Gaussian at large fluctuations in the DPR.

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arXiv2022

How two-way coupling modifies the multiscale preferential sweeping mechanism

For one-way coupled (1WC) flows, Tom & Bragg (J. Fluid Mech., 871, pp. 244-270, 2019) advanced the analysis of Maxey (J. Fluid Mech., 174, pp. 441-465, 1987), which applied to weakly inertial particles, to particles of arbitrary inertia, and the new theoretical result revealed the role that different scales play in the preferential sweeping mechanism that leads to enhanced particle settling in turbulent flows. Monchaux & Dejoan (Phys. Rev. Fluids, 2, 104302, 2017) showed using direct numerical simulations (DNS) that while for low particle loading the effect of two-way coupling (2WC) on the global flow statistics is weak, 2WC enables the particles to drag the fluid in their vicinity down with them, significantly enhancing their settling, and they argued that 2WC suppresses the preferential sweeping mechanism. We explore this further by considering the impact of 2WC on the contribution made by eddies of different sizes on the particle settling. In agreement with Monchaux & Dejoan, we show that even for low loading, 2WC strongly enhances particle settling, and we show how 2WC modifies the contribution from different flow scales. However, contrary to their study, we show that preferential sweeping remains important in 2WC flows. In particular, for both 1WC and 2WC flows, the settling enhancement due to turbulence is dominated by contributions from particles in straining regions of the flow, but for the 2WC case, the particles in these regions also drag the fluid down with them, leading to an enhancement of their settling compared to the 1WC case.

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arXiv2022

A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow

Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a physics-informed Deep Learning technique based on vector quantization to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows. The deep learning framework is composed of convolutional layers and incorporates physical constraints on the flow, such as preserving incompressibility and global statistical characteristics of the velocity gradients. The accuracy of the model is assessed using statistical, comparison-based similarity and physics-based metrics. The training data set is produced from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow. The performance of this lossy data compression scheme is evaluated not only with unseen data from the stationary, isotropic turbulent flow, but also with data from decaying isotropic turbulence, and a Taylor-Green vortex flow. Defining the compression ratio (CR) as the ratio of original data size to the compressed one, the results show that our model based on vector quantization can offer CR $=85$ with a mean square error (MSE) of $O(10^{-3})$, and predictions that faithfully reproduce the statistics of the flow, except at the very smallest scales where there is some loss. Compared to the recent study based on a conventional autoencoder where compression is performed in a continuous space, our model improves the CR by more than $30$ percent, and reduces the MSE by an order of magnitude. Our compression model is an attractive solution for situations where fast, high quality and low-overhead encoding and decoding of large data are required.

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arXiv2021

Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models

We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques. The deep learning framework incorporates physical constraints on the flow, such as preserving incompressibility and global statistical invariants of velocity gradient tensor. The accuracy of the model is assessed using statistical and physics-based metrics. The data set comes from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow in a cubic box. Since the size of the dataset is memory intensive, we first generate a low-dimensional representation of the velocity data, and then pass it to a sequence prediction network that learns the spatial and temporal correlations of the underlying data. The dimensionality reduction is performed via extraction using Vector-Quantized Autoencoder (VQ-AE), which learns the discrete latent variables. For the sequence forecasting, the idea of Transformer architecture from natural language processing is used, and its performance compared against more standard Recurrent Networks (such as Convolutional LSTM). These architectures are designed and trained to perform a sequence to sequence multi-class classification task in which they take an input sequence with a fixed length (k) and predict a sequence with a fixed length (p), representing the future time instants of the flow. Our results for the short-term predictions show that the accuracy of results for both models deteriorates across predicted snapshots due to autoregressive nature of the predictions. Based on our diagnostics tests, the trained Conv-Transformer model outperforms the Conv-LSTM one and can accurately, both quantitatively and qualitatively, retain the large scales and capture well the inertial scales of flow but fails at recovering the small and intermittent fluid motions.

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