How tracer particles sample the complexity of turbulence

Lalescu, Cristian; Wilczek, Michael

doi:10.1088/1367-2630/aa8ecd

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…This effort has ensured that the Lagrangian velocity and acceleration statistics, as obtained from finite differences of trajectories, are in good agreement with those sampled from the fields. Further details of the solver can be found in Lalescu and Wilczek (2018b).…”

Section: Direct Numerical Simulationmentioning

confidence: 99%

Bias in particle tracking acceleration measurement

et al. 2018

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We investigate sources of error in acceleration statistics from Lagrangian Particle Tracking (LPT) data and demonstrate techniques to eliminate or minimise bias errors introduced during processing. Numerical simulations of particle tracking experiments in isotropic turbulence show that the main sources of bias error arise from noise due to position uncertainty and selection biases introduced during numerical differentiation. We outline the use of independent measurements and filtering schemes to eliminate these biases. Moreover, we test the validity of our approach in estimating the statistical moments and probability densities of the Lagrangian acceleration. Finally, we apply these techniques to experimental particle tracking data and demonstrate their validity in practice with comparisons to available data from literature. The general approach, which is not limited to acceleration statistics, can be applied with as few as two cameras and permits a substantial reduction in the spatial resolution and sampling rate required to adequately measure statistics of Lagrangian acceleration.

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Section: Direct Numerical Simulationmentioning

confidence: 99%

Bias in particle tracking acceleration measurement

et al. 2018

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“…To proceed beyond the basic analytical consideration above, we use direct numerical simulations of homogeneous and isotropic turbulence. The simulation data are generated by a pseudo-spectral solver (Lalescu & Wilczek 2018) in a three-dimensional periodic domain of length L = 2π (code units). Full details about the simulation parameters can be found in table 1.…”

Section: Numerical Detailsmentioning

confidence: 99%

On the small-scale structure of turbulence and its impact on the pressure field

Vlaykov

Wilczek

2018

J. Fluid Mech.

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Understanding the small-scale structure of incompressible turbulence and its implications for the non-local pressure field is one of the fundamental challenges in fluid mechanics. Intense velocity gradient structures tend to cluster on a range of scales which affects the pressure through a Poisson equation. Here we present a quantitative investigation of the spatial distribution of these structures conditional on their intensity for Taylorbased Reynolds numbers in the range [160, 380]. We find that the correlation length, the second invariant of the velocity gradient, is proportional to the Kolmogorov scale. It also is a good indicator for the spatial localization of intense enstrophy and strain-dominated regions, as well as the separation between them. We describe and quantify the differences in the two-point statistics of these regions and the impact they have on the non-locality of the pressure field as a function of the intensity of the regions. Specifically, across the examined range of Reynolds numbers, the pressure in strong rotation-dominated regions is governed by a dissipation-scale neighbourhood. In strong strain-dominated regions, on the other hand, it is determined primarily by a larger neighbourhood reaching inertial scales.

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“…As the Reynolds number increases, the number of particles required to adequately sample the turbulent fields needs to grow with the increasing numerical resolution, since this is a measure of the degrees of freedom of the flow. In addition higher-order statistics might be needed to address specific research questions, and thus the number of particles required for converged statistics increases as well [4,30,31,32,33,34,35,36]. Overall, this requires an approach which handles the parallel implementation in an efficient manner for arbitrarily accurate methods.…”

Section: Introductionmentioning

confidence: 99%

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

Lalescu,

Bramas,

Rampp

et al. 2021

Preprint

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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.

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How tracer particles sample the complexity of turbulence

Cited by 9 publications

References 40 publications

Bias in particle tracking acceleration measurement

Bias in particle tracking acceleration measurement

On the small-scale structure of turbulence and its impact on the pressure field

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

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