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Record W2154673764 · doi:10.1017/s0022112008001079

New dynamic subgrid-scale heat flux models for large-eddy simulation of thermal convection based on the general gradient diffusion hypothesis

2008· article· en· W2154673764 on OpenAlexaff
Bing-Chen Wang, Eugene Yee, Donald J. Bergstrom, Oaki Iida

Bibliographic record

VenueJournal of Fluid Mechanics · 2008
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of SaskatchewanDefence Research and Development Canada
Fundersnot available
KeywordsHeat fluxThermal diffusivityMechanicsTemperature gradientLarge eddy simulationConvectionCauchy stress tensorThermalTensor (intrinsic definition)Statistical physicsTurbulenceConvective heat transferPhysicsHeat transferClassical mechanicsThermodynamicsMeteorologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Three new dynamic tensor thermal diffusivity subgrid-scale (SGS) heat flux (HF) models are proposed for large-eddy simulation of thermal convection. The constitutive relations for the proposed modelling approaches represent the most general explicit algebraic formulations possible for the family of SGS HF models constructed using the resolved temperature gradient and SGS stress tensor. As a result, these three new models include a number of previously proposed dynamic SGS HF models as special cases. In contrast to the classical dynamic eddy thermal diffusivity SGS HF model, which strictly requires the SGS heat flux be aligned with the negative of the resolved temperature gradient, the three new models proposed here admit more degrees of freedom, and consequently provide a more realistic geometrical and physical representation of the SGS HF vector. To validate the proposed models, numerical simulations have been performed based on two benchmark test cases of neutrally and unstably stratified horizontal channel flows.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.506
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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 teacher head, 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

Citations44
Published2008
Admission routes1
Has abstractyes

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