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Record W7132879974

Free-stream Turbulence Effects on Laminar Separation Bubbles at Low Reynolds Numbers

2021· dissertation· W7132879974 on OpenAlexafffund
Blago A. Hristovski

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsInstitute for Christian Studies
FundersUniversity of Toronto
KeywordsTurbulenceReynolds numberLaminar flowParticle image velocimetryFlow separationTurbulence kinetic energySeparation (statistics)Transition pointBubble
DOInot available

Abstract

fetched live from OpenAlex

This thesis investigates the behaviour of a laminar separation bubble (LSB) on a low Reynolds number airfoil, at various free-stream turbulence intensities. The experiments were conducted in a wind tunnel for Reynolds numbers 1.0 × 10^5 – 2.0 × 10^5, at angles of attack 4◦ – 15◦, over free-stream turbulence intensities 0.05% – 1.1%. Particle image velocimetry and oil film interferometry were used to measure skin friction, separation and reattachment points, displacement thickness, and flow transition. The experimental results indicate that increasing turbulence intensity reduces the overall LSB effect, by reducing its length and height. The length reduction is driven by the reattachment point moving upstream, due to an earlier transition to turbulence, and the separation point moving downstream, due to additional upstream mixing. The height reduction results in decreasing skin friction magnitudes within the LSB and is the result of a decrease in the wall-normal velocity fluctuations, indicating more 3-D turbulent structures.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.277
Teacher spread0.269 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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