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Record W3034121920 · doi:10.2514/1.j059368

Low-Frequency Dynamics of Flow over a Backward-Facing Step

2020· article· en· W3034121920 on OpenAlexafffund
Stephen J. Wilkins, Mahdi Hosseinali, Joseph W. Hall

Bibliographic record

VenueAIAA Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMach numberVorticityMechanicsReynolds numberPhysicsVortexTurbulenceFlow (mathematics)Offset (computer science)GeometryOpticsMathematics

Abstract

fetched live from OpenAlex

The low-frequency dynamics of turbulent flow downstream of a backward-facing step are examined by low-pass filtering the velocity fields from a long run-time large-eddy simulation. The low-frequency behaviors were found to set the location of streamwise reattachment. Examination of the instantaneous three-component velocity fields showed regions of flow where the streamwise velocity was momentarily driven all the way back to the step face. After the flow reaches the step, it is then directed along the step face in the spanwise direction, resulting in large regions of wall-normal vorticity . These large regions of vorticity are opposed by smaller regions of counter-rotating flow offset in the spanwise direction and were responsible for the spanwise variation of the reattattachment. The low-pass filtered streamwise velocity fields just above the lower wall showed significant spanwise oscillations with a mean wavelength of the streamwise low-pass filtered reattachment position of , consistent with previous studies at different Reynolds and Mach numbers. As the step geometry is homogeneous in the spanwise direction, this shows that this behavior is not the result of wall or edge effects, which is a conclusion that cannot easily be drawn from experimental studies with limited step-height-to-width ratios.

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 score0.096
Threshold uncertainty score0.579

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 head0.006
GPT teacher head0.188
Teacher spread0.181 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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