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Record W4412925565 · doi:10.1080/10618562.2025.2537029

RANS and Scale-Resolving Simulations of the Flow Over Static Pressure Ports

2025· article· en· W4412925565 on OpenAlexaff
M. L. Shur, М. Х. Стрелец, Philippe R. Spalart, А. В. Травин

Bibliographic record

VenueInternational journal of computational fluid dynamics · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsBoeing (Canada)
Fundersnot available
KeywordsReynolds-averaged Navier–Stokes equationsScale (ratio)Flow (mathematics)MechanicsEnvironmental scienceComputational fluid dynamicsStatistical physicsGeologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Compressible flow fields over flush static pressure ports (SPPs) with sharp and rounded rims, which are used on airplanes and wind-tunnel models are studied for a range of diameters and speeds. Both steady RANS computations with the use of different eddy-viscosity models and turbulence-resolving simulations in the framework of the Improved Delayed Detached-Eddy Simulation (IDDES) approach are performed for an SPP in the flat plate boundary layer. The primary objective is to evaluate the capabilities of the two approaches for predicting the difference between the pressure deep in the SPP tube and the ambient pressure in the boundary layer, including a limited comparison with experiment. It is shown that although both approaches return results that agree qualitatively with the available experimental data, the RANS models’ predictions disagree appreciably among themselves and with the IDDES predictions, the latter being close to the experiment for both sharp and rounded port rims.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.003
GPT teacher head0.218
Teacher spread0.215 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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