Evidence map›Paper›PMID 39406841›Full record

ArticleScientific reports2024

Evaluation of particle tracking codes for dispersing particles in porous media.

Marc Berghouse, Filippo Miele, Lazaro J Perez, Ankur Deep Bordoloi, Verónica L Morales, Rishi Parashar

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marc BerghouseDivision of Hydrologic Sciences, Desert Research Institute, Reno, 89512, USA.
Filippo MieleUC Davis, Civil and Environmental Engineering, Davis, 95616, USA.
Lazaro J PerezCivil and Construction Engineering, Oregon State University, Corvallis, 97331, USA.
Ankur Deep BordoloiUniversity of Lausanne, Geosciences and Environment, Lausanne, 1015, Switzerland.
Verónica L MoralesUC Davis, Civil and Environmental Engineering, Davis, 95616, USA.
Rishi ParasharDivision of Hydrologic Sciences, Desert Research Institute, Reno, 89512, USA. Rishi.Parashar@dri.edu.

Funding

U.S. Department of Energy DE-SC0019437
6 · The paper itself

Abstract

Particle tracking (PT) is a popular technique in microscopy, microfluidics and colloidal transport studies, where image analysis is used to reconstruct trajectories from bright spots in a video. The performance of many PT algorithms has been rigorously tested for directed and Brownian motion in open media. However, PT is frequently used to track particles in porous media where complex geometries and viscous flows generate particles with high velocity variability over time. Here, we present an evaluation of four PT algorithms for a simulated dispersion of particles in porous media across a range of particle speeds and densities. Of special note, we introduce a new velocity-based PT linking algorithm (V-TrackMat) that achieves high accuracy relative to the other PT algorithms. Our findings underscore that traditional statistics, which revolve around detection and linking proficiency, fall short in providing a holistic comparison of PT codes because they tend to underpenalize aggressive linking techniques. We further elucidate that all codes analyzed show a decrease in performance due to high speeds, particle densities, and trajectory noise. However, linking algorithms designed to harness velocity data show superior performance, especially in the case of high-speed advective motion. Lastly, we emphasize how PT error can influence transport analysis.

Indexed as

DispersionImage analysisParticle trackingPorous mediaStatistical comparisonV-TrackMat

Identifiers

PMID39406841
PMCPMC11480406

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.