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An experimental study of a plane turbulent wall jet using LDA

2009· article· en· W2019803129 on OpenAlexaff
Noorallah Rostamy, Donald J. Bergstrom, David Deutscher, David S. Sumner, J. D. Bugg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTurbulenceJet (fluid)MechanicsScalingPhysicsReynolds numberPlane (geometry)Power lawMomentum (technical analysis)Classical mechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

An experimental investigation using laser Doppler anemometry was conducted of the mean flow characteristics of a turbulent wall jet on a smooth surface. The Reynolds number based on the slot height and exit velocity of the jet was Re ≈ 7,400. Measurements were carried out at downstream distances from the jet exit ranging from 20 to 80 slot heights. Conventional scaling, momentum-viscosity scaling and wall-jet similarity theory were used to analyze the streamwise evolution of the flow. Mean velocity profiles in both outer and inner coordinates are presented in this paper. Using conventional scaling, the mean velocity profiles were self-similar in the developed region of the jet and the jet spread rate was linear. Fitting to velocity profiles in inner coordinates indicated that use of a power law provides accurate and consistent estimates of the friction velocity in a plane turbulent wall jet. Both the spread rate and the maximum velocity decay were analyzed using different scaling laws. The power law constants in the present work differ from those in other studies which illustrates the sensitivity of the constants to the initial conditions. Skin friction coefficients in the present work showed good agreement with the results of other correlations and experimental studies.

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 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 score0.320
Threshold uncertainty score0.368

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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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.

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

Citations6
Published2009
Admission routes1
Has abstractyes

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