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Record W4407411917 · doi:10.2514/6.2025-2575

Aerothermal Performances of a Shaped Film-Cooling Hole With Spalart-Allmaras Turbulence Model

2025· article· en· W4407411917 on OpenAlexaff
Enza Parente, Alberto Remigi, Frédéric Alauzet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsTurbulenceAerospace engineeringMechanicsPhysicsMaterials scienceEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

The intent of this work is to investigate the numerical behavior of the Spalart-Allmaras one-equation turbulence model applied to a shaped film-cooling hole. The motivation is to identify the strengths and limits of this model in describing the film cooling performances on high-pressure turbines. Additionally, an anisotropic correction, such as a QCR correction, is also evaluated which shows some improvement in the flow description. An anisotropic mesh adaptation process is employed, using the goal-oriented error estimator based on the adiabatic film cooling effectiveness. This mesh adaptation process reduces the discretization error, and, consequently, the numerical dissipation, and so enhancing the error related to the turbulence model and highlighting the differences between different versions of the model. Numerical simulations are performed on the 777 shaped hole configuration of Pennsylvania State University designed by Schroeder [1]. The numerical results are compared to the experimental data from an aerodynamic and thermal point of view.

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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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
Published2025
Admission routes1
Has abstractyes

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