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Record W2995494833 · doi:10.1002/cjce.23697

GPU‐accelerated simulation of polydisperse multiphase flows using dual‐quadrature‐based moment methods

2019· article· en· W2995494833 on OpenAlexvenueno aff
Fabio Pereira dos Santos, Paulo L.C. Lage, Jovani L. Fávero, Inanc Senocak

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsnot available
FundersFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroPetrobrasConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsQuadrature (astronomy)Computer scienceGraphicsAccelerationPopulationApplied mathematicsNyström methodComputational scienceBreakageMultiphase flowCUDAMathematical optimizationMechanicsMathematicsParallel computingPhysicsMathematical analysisClassical mechanicsIntegral equationComputer graphics (images)

Abstract

fetched live from OpenAlex

Abstract Polydisperse multiphase flows can be simulated by coupling a population balance model to the multi‐fluid model. For the first time, this simulation is carried out using the dual‐quadrature method of generalized moments (DuQMoGeM) and its direct version to solve the population balance model. The main disadvantage of these methods is the high computational cost of the embedded cubature. Herein, this challenge was addressed by parallelization on graphics processing units, which resulted in a significant acceleration of the simulations, with speedups that can be larger than 1000. Numerical simulations considering simultaneous particles breakage and aggregation were conducted with direct DuQMoGeM‐FC and DQMoM‐FC, whose results were different due to the existence of quadrature error in the latter.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.293
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
GenreEmpirical

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

Citations2
Published2019
Admission routes1
Has abstractyes

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Same venueThe Canadian Journal of Chemical EngineeringSame topicCoagulation and Flocculation StudiesFrench-language works237,207