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Record W2626054772 · doi:10.1080/19942060.2017.1331866

Macroscopic particle method for channel flow over porous bed

2017· article· en· W2626054772 on OpenAlexafffund
Lei Fu, Yee‐Chung Jin

Bibliographic record

VenueEngineering Applications of Computational Fluid Mechanics · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsLaminar flowPorous mediumParticle (ecology)PorosityFlow (mathematics)InflowTurbulenceMaterials scienceBoundary value problemDarcy's lawCylinderPhysicsEngineeringMechanical engineeringGeologyMathematicsComposite materialMathematical analysis

Abstract

fetched live from OpenAlex

This paper presents a new macroscopic mesh-free particle method in which Darcy’s and Forchheimer’s terms are introduced into the governing equation to ensure the capacity of the particle method in simulating laminar and turbulent porous medium flows. A developed interfacial condition and inflow boundary condition are implemented in the macroscopic particle method to improve the stability of the Particle-based model. The comparisons of channel flow over and within porous bed among the present method, previous mesh-based method, and experimental data show that the macroscopic particle method is capable of simulating flows in both the clear flow region and porous flow region. Finally, two cases of flow over a rigid box and a cylinder lying on porous bed are simulated, and the numerical results are in good agreement with the measured data. The analysis and comparisons indicate that the newly developed particle-based method is reliable and has been successfully extended to macroscopic porous medium simulation.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.275
Teacher spread0.265 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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