Microparticle cloud imaging and tracking for data-driven plasma science

Wang, ‪Zhehui; Kovach, Yao; Wolfe, Bradley T.; Thomas, E.; Guo, Hanqi; Foster, John E.; Shen, Han‐Wei

doi:10.1063/1.5134787

Cited by 14 publications

(7 citation statements)

References 68 publications

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“…They essentially reflect the behavior of the front particles, while little information is available on the grains behind. Similar challenges of 3D and 2D tracking of multiple particles are often encountered in the studies of complex (dusty) plasmas 54 , 55 .…”

Section: Introductionmentioning

confidence: 81%

“…Shadows cast by the grains or reflections on the particle surfaces make a homogeneous illumination practically impossible. Sophisticated tracking mechanisms for clouds of spherical particles at low filling fractions were recently presented with application to dusty plasmas 54 . Some information on the evolution of a sufficiently dilute granular ensemble, the local filling fraction and general trends of the direction of motion can be obtained by averaging image sequences 26 .…”

Section: Introductionmentioning

confidence: 99%

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Visual analysis of density and velocity profiles in dense 3D granular gases

Puzyrev

Fischer

Harth

et al. 2021

Sci Rep

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Granular multiparticle ensembles are of interest from fundamental statistical viewpoints as well as for the understanding of collective processes in industry and in nature. Extraction of physical data from optical observations of three-dimensional (3D) granular ensembles poses considerable problems. Particle-based tracking is possible only at low volume fractions, not in clusters. We apply shadow-based and feature-tracking methods to analyze the dynamics of granular gases in a container with vibrating side walls under microgravity. In order to validate the reliability of these optical analysis methods, we perform numerical simulations of ensembles similar to the experiment. The simulation output is graphically rendered to mimic the experimentally obtained images. We validate the output of the optical analysis methods on the basis of this ground truth information. This approach provides insight in two interconnected problems: the confirmation of the accuracy of the simulations and the test of the applicability of the visual analysis. The proposed approach can be used for further investigations of dynamical properties of such media, including the granular Leidenfrost effect, granular cooling, and gas-clustering transitions.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Visual analysis of density and velocity profiles in dense 3D granular gases

Puzyrev

Fischer

Harth

et al. 2021

Sci Rep

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“…For visualization techniques, our method enables feature-preserving vector field compression by adapting sufficient local error bounds for lossy compressors to preserve critical points in decompressed data without any false positives, false negatives, and false types [22]. For science applications, we also demonstrated the use of our tools to track microparticle clouds in laboratory plasma experiments [52].…”

Section: Case Studies With Applicationsmentioning

confidence: 99%

FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking

Guo,

Lenz,

et al. 2020

Preprint

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We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our high-dimensional simplicial meshing scheme that generalizes both regular and unstructured spatial meshes to spacetime while tessellating spacetime mesh elements into simplices. The benefits of using simplicial spacetime meshes include (1) reducing ambiguity cases for feature extraction and tracking, (2) simplifying the handling of degeneracies using symbolic perturbations, and (3) enabling scalable and parallel processing. The use of simplicial spacetime meshing simplifies and improves the implementation of several feature-tracking algorithms for critical points, quantum vortices, and isosurfaces. As a software framework, FTK provides end users with VTK/ParaView filters, Python bindings, a command line interface, and programming interfaces for feature-tracking applications. We demonstrate use cases as well as scalability studies through both synthetic data and scientific applications including Tokamak, fluid dynamics, and superconductivity simulations. We also conduct end-to-end performance studies on the Summit supercomputer. FTK is open-sourced under the MIT license: https://github.com/hguo/ftk.

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“…Terabyte datasets are available from dusty plasma experiments through particle tracking and imaging [336]. Dusty plasma movies have been recorded at about 1500 to 5000 frame length, at the rates between 100 -500 frames s −1 and each image size of a few MB per frame [337].…”

Section: Dusty and Complex Plasmasmentioning

confidence: 99%

“…For an experimental campaign consisting of a few hundred runs, more than 1.5 million movie frames or more than 1 TB of raw data becomes available [338]. Automated particle tracking through machine learning is emerging as a necessary to process the large number of images and to extract the particle trajectories [336]. Particle tracking and particle imaging velocimetry (PIV) techniques have wider applications than plasma physics.…”

Section: Dusty and Complex Plasmasmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Microparticle cloud imaging and tracking for data-driven plasma science

Cited by 14 publications

References 68 publications

Visual analysis of density and velocity profiles in dense 3D granular gases

Visual analysis of density and velocity profiles in dense 3D granular gases

FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking

2022 Review of Data-Driven Plasma Science

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