arXiv subjects

Michael Wilczek

Publications and source records attributed to Michael Wilczek.

At least 19 recordsLinked to original sources

arXiv2026

Statistical field theory for a passive vector model with spatially linear advection

One challenge in developing a statistical field theory of turbulence is the analysis of the functional equations that govern the complete statistics of the flow field. Simplified models of turbulence may help to develop such a statistical framework. Here, we consider the advection and stretching of an incompressible passive vector field by a spatially linear stochastic field as a model for small-scale turbulence. The model encompasses non-Gaussian statistics due to an intermittent energy flux from large scales to small scales, thereby displaying hallmark features of turbulence. We explore this model using the Hopf functional formalism, which naturally leads to a decomposition of the complex non-Gaussian statistics into Gaussian sub-ensembles based on different realizations of the advecting field. We then characterize intermittency of the model using a numerical implementation, which takes advantage of this statistical decomposition.

physics.flu-dyn↗

arXiv2026

Exact formulas for arbitrary order velocity-gradient moments in isotropic turbulence

Statistical moments of velocity gradients provide fundamental information on the small-scale properties of turbulence. In this work, we propose a systematic method to derive exact expressions for statistical moments of arbitrary order for both longitudinal and transverse velocity gradients in isotropic turbulence. The approach is applicable to both compressible and incompressible flows and expresses the moments in terms of invariants of the velocity gradient tensor. The derivation combines isotropic tensor theory, orientational averaging, and an algorithmic implementation, enabling the computation of high-order moments in a unified framework. We show that longitudinal velocity gradient moments of order higher than three depend not only on $\mathrm{tr}(\boldsymbol{S}^2)$, which is proportional to the dissipation rate, but also on $\mathrm{tr}(\boldsymbol{S}^3)$, which reflects strain self-amplification, where $\boldsymbol{S}$ denotes the strain-rate tensor. The resulting theoretical expressions are validated through comparisons with existing theoretical results and direct numerical simulations.

physics.flu-dyn↗

arXiv2026

Effects of correlated collisions and intermittency on the growth of lucky droplets

To trigger precipitation, water droplets in warm clouds need to attain a sufficient size. Theoretical estimates based on condensation and gravitational collisions alone fail to explain the observed timescales for the onset of precipitation for a range of droplet sizes. This suggests the involvement of collisional growth mediated by turbulence to resolve the so-called ``size-gap problem''. For the onset of rain, it is sufficient that statistical outliers, coined ``lucky droplets'', cross the size gap. In this study, we explore the influence of turbulence on droplet growth, focusing on correlated collisions and intermittency. Using direct numerical simulations of droplets in turbulent flow, we constrain a non-Markovian stochastic framework that allows us to assess memory effects on the droplet-size distribution arising from correlations between consecutive collisions. Using our framework, we find that correlated collisions accelerate the initial growth of lucky droplets but have sub-leading effect at later stages. Consequently, we neglect correlations from collisions and model an ensemble of cloud parcels representing fluctuations in the volume-averaged dissipation rate. Here, the distribution of droplet sizes in each parcel is described by a linear master equation with a time-dependent collision rate according to the volume-averaged dissipation rate. Our analyses of this toy model show that intermittency can significantly reduce the time required by lucky droplets to cross the size gap.

physics.flu-dyn↗

arXiv2025

Models for active particles: common features and differences

Systems of active particles can show a large variety of collective behavior. In theory, two aspects determine the collective behavior: the model at the particle level and the parameter regime. While many studies consider a single model and study its parameter regime, here, we focus on the former aspect. Motivated by experiments that study dilute suspensions of Chlamydomonas reinhardtii in a self-generated oxygen gradient, we compare various models with external field-dependent motility to understand how the collective behavior changes between models. We vary the particle-particle interaction from no interactions to steric interactions, the particle shape from round disks to dumbbells, the self-propulsion mechanism from constant speed to rocking motion, and the particle's center of mass from the geometric center to off-center. We find that changes in the model of the active agents can lead to similar statistics in the dilute regime and different collective behavior in the dense regime. We conclude that active particle models do not easily generalize for different real active agents, but instead require a clear understanding of the agents' microscopic properties.

cond-mat.soft↗

arXiv2025

An ensemble of Gaussian fields with multifractal statistics for turbulence

Obtaining accurate field statistics continues to be one of the major challenges in turbulence theory and modeling. From the various existing modeling approaches, multifractal models have been successful in capturing intermittency in velocity gradient and increment distributions. On the other hand, superstatistical models from nonequilibrium statistical mechanics have shown the capacity to model PDFs of various statistical turbulent quantities as ensembles of simpler stochastic processes. Here, we present an approach that generates field statistics in the form of a characteristic functional by promoting a model for multifractal increment statistics to an ensemble of Gaussian fields. By carefully designing the correlation function and the corresponding weight of each subensemble, we are able to define a functional that exhibits multifractal two-point inertial-range and dissipation-range statistics, and that blends into realistic large-scale behavior. Additionally, the method is capable of producing multifractal statistics with any of the widely used singularity spectra. We characterize the fidelity of our approach through comparisons to literature results from direct numerical simulations. Overall, our framework thereby bridges between three different perspectives to turbulence: superstatistics, multifractals, and functional approaches to turbulence.

physics.flu-dyn↗

arXiv2025

Predicting any small-scale statistics of high-Reynolds-number turbulence using ensemble simulations

The complex small-scale statistics of turbulence are a result of the combined cascading dynamics through all scales of the flow. Predicting these statistics using fully resolved simulations at the high Reynolds numbers that typically occur in engineering, geophysical, and astrophysical flows will exceed the capabilities of even the largest supercomputers for the foreseeable future. A common observation is that high-Reynolds-number flows are organized in clusters of intense turbulent activity separated by large regions of quiescent flow. We here show that small-scale statistics in high-Reynolds-number turbulence can be predicted based on an ensemble hypothesis, stating that they can be emulated by the statistical mixture of a heterogeneous ensemble of lower-Reynolds-number simulations. These simulations are forced at smaller scales with energy injection rates varying across the ensemble. We show that our method predicts complex gradient statistics from the recent literature, including the joint QR-PDF and extreme dissipation and enstrophy statistics, to unprecedented accuracy. Remarkably, we find that the weight distribution needed for the ensemble method to make predictions can be inferred from the anomalous scaling exponents of turbulence. Thus combining theory with fully resolved simulations, our method can be readily applied to predict a wide range of statistics at high Reynolds number but low cost, while opening up various avenues for further theoretical and numerical exploration.

physics.flu-dyn↗

arXiv2025

Temporal large-scale intermittency and its impact on the statistics of turbulence

Turbulent flows in three dimensions are characterized by the transport of energy from large to small scales through the energy cascade. Since the small scales are the result of the nonlinear dynamics across the scales, they are often thought of as universal and independent of the large scales. However, as famously remarked by Landau, sufficiently slow variations of the large scales should nonetheless be expected to impact small-scale statistics. Such variations, often termed large-scale intermittency, are pervasive in experiments and even in simulations, while differing from flow to flow. Here, we evaluate the impact of temporal large-scale fluctuations on velocity, vorticity, and acceleration statistics by introducing controlled sinusoidal variations of the energy injection rate into direct numerical simulations of turbulence. We find that slow variations can have a strong impact on flow statistics, raising the flatness of the considered quantities. We discern three contributions to the increased flatness, which we model by superpositions of statistically stationary flows. Overall, our work demonstrates how large-scale intermittency needs to be taken into account in order to ensure comparability of statistical results in turbulence.

physics.flu-dyn↗

arXiv2024

Twisting vortex lines regularize Navier-Stokes turbulence

Fluid flows are intrinsically characterized via the topology and dynamics of underlying vortex lines. Turbulence in common fluids like water and air, mathematically described by the incompressible Navier-Stokes equations (INSE), engenders spontaneous self-stretching and twisting of vortex lines, generating a complex hierarchy of structures. While the INSE are routinely used to describe turbulence, their regularity remains unproven; the implicit assumption being that the self-stretching is ultimately regularized by viscosity, preventing any singularities. Here, we uncover an inviscid regularizing mechanism stemming from self-stretching itself, by analyzing the flow topology as perceived by an observer aligned with the vorticity vector undergoing amplification. While, initially, vorticity amplification occurs via increasing twisting of vortex lines, a regularizing anti-twist spontaneously emerges to prevent unbounded growth. By isolating a vortex, we additionally demonstrate the genericity of this self-regularizing anti-twist. Our work, directly linking dynamics of vortices to turbulence statistics, reveals how the Navier-Stokes dynamics avoids the development of singularities even without the aid of viscosity.

physics.flu-dyn↗

arXiv2024

Tailor-designed models for the turbulent velocity gradient through normalizing flow

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for non-local pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations statistically closely resemble the time series from DNS.

physics.flu-dyn↗

arXiv2023

On the large-scale sweeping of small-scale eddies in turbulence -- A filtering approach

We present an analysis of the Navier-Stokes equations based on a spatial filtering technique to elucidate the multi-scale nature of fully developed turbulence. In particular, the advection of a band-pass-filtered small-scale contribution by larger scales is considered, and rigorous upper bounds are established for the various dynamically active scales. The analytical predictions are confirmed with direct numerical simulation data. The results are discussed with respect to the establishment of effective large-scale equations valid for turbulent flows.

physics.flu-dyn↗

arXiv2023

Asymptotic predictions on the velocity gradient statistics in low-Reynolds number random flows: Onset of skewness, intermittency and alignments

Stirring a fluid through a Gaussian forcing at a vanishingly small Reynolds number produces a Gaussian random field, while flows at higher Reynolds numbers exhibit non-Gaussianity, cascades, anomalous scaling and preferential alignments. Recent works (Yakhot and Donzis, Phys. Rev. Lett., vol. 119, 2017, pp. 044501; Gotoh and Yang, Philos. Trans. Royal Soc. A, vol. 380, 2022, pp. 20210097) investigated the onset of these turbulent hallmarks in low-Reynolds number flows by focusing on the scaling of the velocity increments. They showed that the scalings in random flows at low-Reynolds and in high-Reynolds number turbulence are surprisingly similar. In this work, we address the onset of turbulent signatures in low-Reynolds number flows from the viewpoint of the velocity gradient dynamics, giving insights into its rich statistical geometry. We combine a perturbation theory of the full Navier-Stokes equations with velocity gradient modeling. This procedure results in a stochastic model for the velocity gradient in which the model coefficients follow directly from the Navier-Stokes equations and statistical homogeneity constraints. The Fokker-Planck equation associated with our stochastic model admits an analytic solution which shows the onset of turbulent hallmarks at low Reynolds numbers: skewness, intermittency and preferential alignments arise in the velocity gradient statistics as the Reynolds number increases. The model predictions are in excellent agreement with direct numerical simulations of low-Reynolds number flows.

physics.flu-dyn↗

arXiv2023

How contact patterns destabilize and modulate epidemic outbreaks

The spread of a contagious disease clearly depends on when infected individuals come into contact with susceptible ones. Such effects, however, have remained largely unexplored in the study of epidemic outbreaks. In particular, it remains unclear how the timing of contacts interacts with the latent and infectious stages of the disease. Here, we use real-world physical proximity data to study this interaction and find that the temporal statistics of actual human contact patterns i) destabilize epidemic outbreaks and ii) modulate the basic reproduction number. We explain both observations by distinct aspects of the observed contact patterns. On the one hand, we find the destabilization of outbreaks to be caused by temporal clustering of contacts leading to over-dispersed offspring distributions and increased probabilities of otherwise rare events (zero- and super-spreading). Notably, our analysis enables us to disentangle previously elusive sources of over-dispersion in empirical offspring distributions. On the other hand, we find the modulation of R0 to be caused by a periodically varying contact rate. Both mechanisms are a direct consequence of the memory in contact behavior, and we showcase a generative process that reproduces these non-Markovian statistics. Our results point to the importance of including non-Markovian contact timings into studies of epidemic outbreaks.

physics.soc-ph↗

arXiv2022

A minimal phase-coupling model for intermittency in turbulent systems

Turbulent systems exhibit a remarkable multi-scale complexity, in which spatial structures induce scale-dependent statistics with strong departures from Gaussianity. In Fourier space, this is reflected by pronounced phase synchronization. A quantitative relation between real-space structure, statistics, and phase synchronization is currently missing. Here, we address this problem in the framework of a minimal phase-coupling model, which enables a detailed investigation by means of dynamical systems theory and multi-scale high-resolution simulations. We identify the spectral power-law steepness, which controls the phase coupling, as the control parameter for tuning the non-Gaussian properties of the system. Whereas both very steep and very shallow spectra exhibit close-to-Gaussian statistics, the strongest departures are observed for intermediate slopes comparable to the ones in hydrodynamic and Burgers turbulence. We show that the non-Gaussian regime of the model coincides with a collapse of the dynamical system to a lower-dimensional attractor and the emergence of phase synchronization, thereby establishing a dynamical-systems perspective on turbulent intermittency.

physics.flu-dyn↗

arXiv2022

Optimal Turbulent Transport in Microswimmer Suspensions

Microswimmer suspensions self-organize into complex spatio-temporal flow patterns, including vortex lattices and mesoscale turbulence. Here we explore the consequences for the motion of passive tracers, based on a continuum model for the microswimmer velocity field. We observe two qualitatively different regimes distinguished via the dimensionless Kubo number $K$. At advection strengths right above the transition to turbulence, the flow field evolves very slowly ($K \gg 1$) and the spatial vortex structures lead to dominant trapping effects. In contrast, deep in the turbulent state, much faster dynamics ($K \ll 1$) consistent with the so-called sweeping hypothesis leads to transport properties completely determined by the temporal correlations. In between ($K \approx 1$), we observe a regime of optimal transport, signaled by a maximum of the diffusion coefficient.

physics.flu-dyn↗

arXiv2022

Quantitative prediction of sling events in turbulence at high Reynolds numbers

Collisional growth of droplets, such as occurring in warm clouds, is known to be significantly enhanced by turbulence. Whether particles collide depends on their flow history, in particular on their encounters with highly intermittent small-scale turbulent structures, which despite their rarity can dominate the overall collision rate. Intuitively, strong vortices may act as slings for inertial particles, leading to intersections where several streams of particles collide at large velocities. Here, we develop a quantitative criterion for sling events based on the velocity gradient history along particle paths. We demonstrate by combination of theory and simulations that the problem reduces to a one-dimensional localization problem as encountered in condensed matter physics. The reduction demonstrates that the creation of slings is completely controlled by the smallest negative eigenvalue of the velocity gradient tensor. We use fully resolved turbulence simulations to confirm our predictions and study their Stokes and Reynolds number dependence. We also discuss extrapolations to the parameter range relevant in clouds, showing that sling events at high Reynolds numbers are significantly enhanced for small Stokes numbers.

physics.flu-dyn↗

arXiv2022

An Efficient Particle Tracking Algorithm for Large-Scale Parallel Pseudo-Spectral Simulations of Turbulence

Particle tracking in large-scale numerical simulations of turbulent flows presents one of the major bottlenecks in parallel performance and scaling efficiency. Here, we describe a particle tracking algorithm for large-scale parallel pseudo-spectral simulations of turbulence which scales well up to billions of tracer particles on modern high-performance computing architectures. We summarize the standard parallel methods used to solve the fluid equations in our hybrid MPI/OpenMP implementation. As the main focus, we describe the implementation of the particle tracking algorithm and document its computational performance. To address the extensive inter-process communication required by particle tracking, we introduce a task-based approach to overlap point-to-point communications with computations, thereby enabling improved resource utilization. We characterize the computational cost as a function of the number of particles tracked and compare it with the flow field computation, showing that the cost of particle tracking is very small for typical applications.

physics.flu-dyn↗

arXiv2022

The statistical geometry of material loops in turbulence

Material elements - which are lines, surfaces, or volumes behaving as passive, non-diffusive markers - provide an inherently geometric window into the intricate dynamics of chaotic flows. Their stretching and folding dynamics has immediate implications for mixing in the oceans or the atmosphere, as well as the emergence of self-sustained dynamos in astrophysical settings. Here, we uncover robust statistical properties of an ensemble of material loops in a turbulent environment. Our approach combines high-resolution direct numerical simulations of Navier-Stokes turbulence, stochastic models, and dynamical systems techniques to reveal predictable, universal features of these complex objects. We show that the loop curvature statistics become stationary through a dynamical formation process of high-curvature folds, leading to distributions with power-law tails whose exponents are determined by the large-deviations statistics of finite-time Lyapunov exponents of the flow. This prediction applies to advected material lines in a broad range of chaotic flows. To complement this dynamical picture, we confirm our theory in the analytically tractable Kraichnan model with an exact Fokker-Planck approach.

physics.flu-dyn↗

arXiv2021

Are there higher-order Betchov homogeneity constraints for incompressible isotropic turbulence?

Incompressible and statistically homogeneous flows obey exact kinematic relations. The Betchov homogeneity constraints (Betchov, J. Fluid Mech., vol. 1, 1956, pp. 497-504) for the average principal invariants of the velocity gradient are among the most well known and extensively employed homogeneity relations. These homogeneity relations have far-reaching implications for the coupled dynamics of strain and vorticity, as well as for the turbulent energy cascade. Whether the Betchov homogeneity constraints are the only possible ones or whether additional homogeneity relations exist has not been proven yet. Here we show that the Betchov homogeneity constraints are the only homogeneity constraints for incompressible and statistically isotropic velocity gradient fields. We also extend our results to derive homogeneity relations involving the velocity gradient and other dynamically relevant quantities, such as the pressure Hessian.

physics.flu-dyn↗