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Record W2801212812 · doi:10.1139/tcsme-2017-0022

Wake measurements of oscillating supercritical airfoil in compressible flow

2018· article· en· W2801212812 on OpenAlexvenueno aff
Behnaz Beheshti Boroumand, Mahmoud Mani

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
Fundersnot available
KeywordsWakeMach numberAirfoilMechanicsAngle of attackReduced frequencyOscillation (cell signaling)Wind tunnelPhysicsChord (peer-to-peer)Trailing edgeAerodynamicsAmplitudeAcousticsReynolds numberOptics

Abstract

fetched live from OpenAlex

Boundary layer and wake behaviors are strongly affected by airfoil motion. Moreover, parameters like body oscillation frequency, oscillation type, Mach number, and angle of attack play main roles in wake characteristics. In this research, both static and dynamic tests were carried out in a tri-sonic wind tunnel to study wake profiles experimentally by hot wire anemometry. All data were recorded at a free stream Mach number of 0.4. Quarter-length and half-length of chord were also considered as downstream distances from the trailing edge in pitching motions of mean angle of attack of −0.4°. Frequencies of 3 Hz and 6 Hz with amplitude of 3° were chosen as oscillation parameters. Voltages at hot wire outputs were measured and analyzed qualitatively and statistically with root-mean-square, correlation, mean value distribution, time history, and frequency. Flow parameters were obtained by computational studies under similar experimental test conditions. The wake characteristics obtained from numerical and experimental methods were compared.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.208
Teacher spread0.190 · 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 designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207