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Record W2506340201 · doi:10.1017/jfm.2016.435

Interactions of large-scale free-stream turbulence with turbulent boundary layers

2016· article· en· W2506340201 on OpenAlexfundno aff
Eda Doğan, Ronald Hanson, Bharathram Ganapathisubramani

Bibliographic record

VenueJournal of Fluid Mechanics · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilUniversity of SouthamptonNatural Sciences and Engineering Research Council of CanadaZonta International Foundation
KeywordsTurbulencePhysicsBoundary layerReynolds numberK-epsilon turbulence modelMechanicsLength scale

Abstract

fetched live from OpenAlex

The scale interactions occurring within a turbulent boundary layer are investigated in the presence of free-stream turbulence. The free-stream turbulence is generated by an active grid. The free stream is monitored by a single-component hot-wire probe, while a second probe is roved across the height of the boundary layer at the same streamwise location. Large-scale structures occurring in the free stream are shown to penetrate the boundary layer and increase the streamwise velocity fluctuations throughout. It is speculated that, depending on the extent of the penetration, i.e. based on the level of free-stream turbulence, the near-wall turbulence production peaks at different wall-normal locations than the expected location of $y^{+}\approx 15$ for a canonical turbulent boundary layer. It is shown that the large scales dominating the log region have a modulating effect on the small scales in the near-wall region; this effect becomes more significant with increasing turbulence in the free stream, i.e. similarly increasing $Re_{\unicode[STIX]{x1D706}_{0}}$ . This modulating interaction and its Reynolds-number trend have similarities with canonical turbulent boundary layers at high Reynolds numbers where the interaction between the large scales and the envelope of the small scales exhibits a pure amplitude modulation (Hutchins & Marusic, Phil. Trans. R. Soc. Lond. A, vol. 365 (1852), 2007, pp. 647–664; Mathis et al. , J. Fluid Mech. , vol. 628, 2009, pp. 311–337). This similarity has encouraging implications towards generalising scale interactions in turbulent boundary layers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.524

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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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

Citations66
Published2016
Admission routes1
Has abstractyes

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