MétaCan
Menu
Back to cohort
Record W2593842318

Modifications of the k-kL-ω Transition Model based on Pohlhausen and Falkner-Skan Profiles

2015· article· en· W2593842318 on OpenAlexfundno aff
J. J. Fuerst, Mazhural Islam, Jaromír Příhoda, David Wood

Bibliographic record

VenueVTechWorks (Virginia Tech) · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPressure gradientAdverse pressure gradientTurbulenceLaminar flowTurbulence modelingMechanicsAirfoilThermodynamicsPhysicsViscosityComputational fluid dynamicsMeteorologyStatistical physicsFlow separation
DOInot available

Abstract

fetched live from OpenAlex

We will present novel modifications of the three-equation k-kL-ω eddy viscosity model proposed by Walters and Cokljat [1] for the adverse pressure gradient flows that occur on wind turbine blades and airfoils. The original model was based on the k-ω framework with an additional transport equation for laminar kinetic energy which allows the prediction of natural or bypass laminar-turbulent transitions. The model uses only local information and is, therefore, easily implemented in modern CFD codes including Fluent and OpenFOAM. The original model shows very good agreement with experimental data for zero pressure gradient flows (see e.g. [1]) but it delays the transition for adverse pressure gradient flows at low free-stream turbulence levels [2]. Both stability analysis and experiments show that the pressure gradient has a big influence on transition [3].

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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.196
Teacher spread0.184 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueVTechWorks (Virginia Tech)Same topicFluid Dynamics and Turbulent FlowsFrench-language works237,207