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Record W2016262333 · doi:10.1080/10407790490268896

AN EFFICIENT SOLUTION SCHEME FOR APPLYING THE INTEGRAL-TYPE DYNAMIC LOCALIZATION SUBGRID-SCALE MODEL IN TURBULENCE WITH HOMOGENEOUS DIRECTIONS

2004· article· en· W2016262333 on OpenAlexaff
Bing-Chen Wang, Donald J. Bergstrom

Bibliographic record

VenueNumerical Heat Transfer Part B Fundamentals · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFredholm integral equationIntegral equationMathematicsApplied mathematicsLarge eddy simulationScale (ratio)Couette flowTurbulenceMathematical optimizationIntegral transformMathematical analysisFlow (mathematics)PhysicsMechanicsGeometry

Abstract

fetched live from OpenAlex

The conventional integral-type dynamic localization subgrid-scale stress model is a Fredholm integral equation of the second kind. This model is mathematically consistent, but it has not been widely used in the large-eddy simulation community due to the relatively high computational cost of solving the Fredholm integral equation using an iterative scheme. In this article, a direct solution scheme based on a discrete Gaussian filter is developed to solve the integral system for turbulent flow with homogeneous dimensions. The proposed direct solution scheme is computationally efficient and makes the integral-type dynamic localization subgrid-scale stress model affordable. Turbulent Couette flows with Reynolds numbers of 2,600 and 4,762 are used in numerical simulations to validate the proposed approach.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueNumerical Heat Transfer Part B FundamentalsSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207