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Record W2410038711 · doi:10.2514/6.2016-3946

Effects of free-stream turbulence intensity on laminar separation bubbles

2016· article· en· W2410038711 on OpenAlexafffund
Mark S. Istvan, John W. Kurelek, Serhiy Yarusevych

Bibliographic record

Venue46th AIAA Fluid Dynamics Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceLaminar flowSeparation (statistics)MechanicsPhysicsMaterials scienceComputer science

Abstract

fetched live from OpenAlex

The effects of free-stream turbulence intensity on the flow over a NACA 0018 airfoil are studied experimentally in a wind tunnel facility. A parametric study is performed over a range of chord Reynolds numbers from 100 000 to 200 000, angles of attack from 0 to 20, and free-stream turbulence intensities from 0.09% to 2.03% in order to unravel the effects of each parameter on suction side laminar separation bubble topology and the resulting changes in airfoil lift. In order to investigate the effects of free-stream turbulence intensity on the streamwise and spanwise flow development within a separation bubble, flow field measurements are made using planar Particle Image Velocimetry for an angle of attack of 4, chord Reynolds numbers of 80 000 and 125 000, and free-stream turbulence intensities between 0.10% and 1.94%. \n \nThe results show that increasing the level of free-stream turbulence intensity leads to a reduction in the length of the separation bubble formed over the suction side of the airfoil. The reduction in bubble length is a result of a downstream shift in mean separation as well as an upstream shift in mean transition and, consequently, mean reattachment. At low angles of attack, the reduction in separation bubble length leads to a slight reduction in airfoil lift, while at pre-stall angles of attack the reduction in separation bubble length alleviates the loss of suction at the location of the suction peak, thereby increasing lift, and can delay stall. While the effects of turbulence intensity and chord Reynolds number on the mean flow are shown to be similar, their effects on transition are shown to be notably different. The upstream shift in mean transition with increasing turbulence intensity is shown to be the result of disturbances reaching higher amplitudes earlier upstream as the level of turbulence intensity is increased, despite increased bubble stability. This result suggests that the increased initial perturbation amplitude at elevated turbulence intensity levels is solely responsible for the upstream shift in mean transition. In contrast, the upstream shift in mean transition with increasing Reynolds number is a result of decreased bubble stability. \n \nWavenumber-frequency spectra of velocity fluctuations in the separated shear layer show that disturbances become more broadband in both time and space with increasing turbulence intensity. In addition, the results show that as the level of free-stream turbulence intensity is increased, the spanwise coherence of shear layer rollers decreases at the location of roll-up, leading to earlier vortex breakdown. At elevated levels of turbulence intensity, streamwise streaks of low speed fluid are observed, and originate in the boundary layer upstream of the separation bubble. These streaks form as a result of the onset of bypass transition, leading to significant changes in bubble dynamics, particularly at the highest level of turbulence intensity investigated. The results suggest that the transition mechanism in the separation bubble at the highest level of turbulence intensity investigated is altered.

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)0.000
metaresearch head score (Gemma)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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