Evidence map›Paper›PMID 40038445›Full record

ArticleScientific reports2025

The uncertainty inherent to DEM simulations of interlocking particles.

Lukas Maier, Michael Mitterlindner, Hadie Benabchiasli, Gregor Fasching, Stefan Radl

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Lukas MaierInstitute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/III, 8010, Graz, Austria.
Michael MitterlindnerInstitute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/III, 8010, Graz, Austria.
Hadie BenabchiasliInstitute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/III, 8010, Graz, Austria.
Gregor FaschingInstitute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/III, 8010, Graz, Austria.
Stefan RadlInstitute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/III, 8010, Graz, Austria. radl@tugraz.at.

Funding

Amt der Steiermärkischen Landesregierung - Zukunftsfonds 1511lCluster NAWI-GrazVienna Scientific Cluster 72416
6 · The paper itself

Abstract

In industrial applications, the handling of heterogeneous mixtures of phases and materials poses challenges for direct measurements and experiments, necessitating complementary modeling approaches. The Discrete Element Method (DEM) is commonly used for simulating the flow of granular systems, typically with spherical particles. However, certain applications, such as recycled polymers and batteries, require alternative non-convex particle representations in DEM simulations. Tetrapods are a promising shape candidate for modeling the flow behavior of such materials, as well as the associated uncertainty. We investigate the impact of the tetrapods' properties on the outcome and uncertainty inherent to DEM-based simulations. We demonstrate that tetrapods are effective for modeling interlocking materials, with their shape and size parameter significantly affecting interlocking behavior. Most interestingly, we can correlate the shape and size of tetrapods to the uncertainty inherent to our simulations. Specifically, we find that this uncertainty is positively correlated with both tetrapod size and the interlocking parameter ξ/D that quantifies their non-convexity. Lastly, we provide guidelines for selecting optimal tetrapod parameter sets for accurately modeling materials based on mean and variability measured in experiments.

Indexed as

Discrete element methodMachine learningNon-spherical particlesSimulation

Identifiers

PMID40038445
PMCPMC11880563

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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 OpenQuestion graph.