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Record W7071770381

Verlet buffer for broad phase interaction detection in Discrete Element Method

2018· article· en· W7071770381 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Repository and Bibliography (University of Luxembourg) · 2018
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsVerlet integrationDiscrete element methodComputationCollisionExtension (predicate logic)Stability (learning theory)Extended discrete element methodRange (aeronautics)Particle (ecology)
DOInot available

Abstract

fetched live from OpenAlex

The Extended Discrete Element Method (XDEM) is a novel and innovative numerical simulation technique that extends the dynamics of granular materials or particles as described through the classical discrete element method (DEM) by additional properties such as the thermodynamic state, stress/strain for each particle. Such DEM simulations used by industries to set up their experimental processes are complexes and heavy in computation time. Therefore, simulations have to be precise, efficient and fast in order to be able to process hundreds of millions of particles. To tackle this issue, such DEM simulations are usually parallelized with MPI. One of the most expensive computation parts of a DEM simulation is the collision detection of particles. It is classically divided into two steps: the broad phase and the narrow phase. The broad phase uses simplified bounding volumes to perform an approximated but fast collision detection. It returns a list of particle pairs that could interact. The narrow phase is applied to the result of the broad phase and returns the exact list of colliding particles. The goal of this research is to apply a Verlet buffer method to (X)DEM simulations regardless of which broad phase algorithm is used. We rely on the fact that such DEM simulations are temporal coherent: the neighborhood only changes slightly from the last time-step to the current time-step. We use the Verlet buffer method to extend the list of pairs returned by the broad phase by stretching the particles bounding volume with an extension range. This allows re-using the result of the broad phase for several time-steps before an update is required once again and thereby its reduce the number of times the broad phase is executed. We have implemented a condition based on particles displacements to ensure the validity of the broad phase: a new one is executed to update the list of colliding particles only when necessary. This guarantees identical results because approximations introduced in the broad phase by our approach are corrected in the narrow phase which is executed at every time-steps anyway. We perform an extensive study to evaluate the influence of the Verlet extension range on the performance of the execution in terms of computation time and memory consumption. We consider different test-cases, partitioners (ORB, Zoltan, METIS, SCOTCH, ...), broad phase algorithms (Link cell, Sweep and prune, ...) and grid configurations (fine, coarse), sequential and parallel (up to 280 cores). While a larger Verlet buffer increases the cost of the broad phase and narrow phase, it also allows skipping a significant number of broad phase execution (> 99 \%). As a consequence, our first results show that this approach can speeds up the total .execution time up to a factor of 5 for sequential executions, and up to a factor of 3 parallel executions on 280 cores while maintaining a reasonable memory consumption.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.273
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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