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Record W7066697324

Implementation and validation of the spalart-allmaras turbulance model

2018· dissertation· en· W7066697324 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsTurbulenceBenchmark (surveying)Computational fluid dynamicsK-omega turbulence modelAerodynamicsK-epsilon turbulence modelTurbulence modelingField (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

In the field of computational fluid dynamics (CFD), the most popular method to resolvethe effects of turbulence in a numerical simulation is to employ the Reynolds-averagedNavier-Stokes Equations (RANS) and capture the turbulence effects using a turbulence model.Among the many turbulence models that exist, the Spalart-Allmaras (S-A) model hasproven to be reliable for attached and moderately separated flows. In this work, the S-Amodel has been implemented in two CFD frameworks : an in-house academic structuredfinite-volume cell-centered code and MAYA HTT’s commercial unstructured vertex-centeredcode. The implementation details are presented along with validation results on NASA TurbulenceModel Resource benchmark cases and on aerodynamic cases.

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 categoriesMeta-epidemiology (narrow)
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 score0.394
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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.

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

Citations0
Published2018
Admission routes2
Has abstractyes

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