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Record W4298355901 · doi:10.1615/tsfp7.1140

HOT WIRE SPATIAL RESOLUTION EFFECTS IN MEASUREMENTS OF GRID GENERATED TURBULENCE

2011· article· en· W4298355901 on OpenAlexaff
A. Ashok, Marcus Hultmark, Sean Bailey, Alexander J. Smits

Bibliographic record

VenueProceeding of Seventh International Symposium on Turbulence and Shear Flow Phenomena · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsWestern University
Fundersnot available
KeywordsTurbulenceSpatial filterPhysicsTurbulence kinetic energyK-epsilon turbulence modelGridHomogeneous isotropic turbulenceFilter (signal processing)IsotropyK-omega turbulence modelImage resolutionMechanicsComputational physicsOpticsGeometryDirect numerical simulationMathematicsEngineeringElectrical engineeringReynolds number

Abstract

fetched live from OpenAlex

We use grid generated homogeneous isotropic turbulence as a benchmark flow to test the effects of spatial resolution on turbulence measurements with hot wires. The grid turbulence is generated in a low speed 0.6 m by 0.9 m closed circuit wind tunnel using a square mesh grid placed at the test section inlet. Measurements of the turbulence statistics and spectra downstream of the grid were made using hot-wires of varying length. An empirical correlation for the attenuation of the energy due to spatial filtering is constructed as a function of the parameter l/η. The effect of spatial filtering on the streamwise spectrum function is observed to extend to almost all wavenumbers, even those significantly lower than the length of the hot wire itself. It is shown that these grid turbulence results relate directly to the nearwall region of wall-bounded flows, where the effects of spatial filtering are most acutely felt.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.019
GPT teacher head0.212
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2011
Admission routes1
Has abstractyes

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