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Record W2097081826 · doi:10.1260/147547204323022248

Airframe Noise Study of a Bombardier CRJ-700 Aircraft Model in the NASA Ames 7-by 10-Foot Wind Tunnel

2004· article· en· W2097081826 on OpenAlex
Paul T. Soderman, F. Kafyeke, Josée Boudreau, Nathan Burnside, Stephen Jaeger, Reuben Chandrasekharan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Aeroacoustics · 2004
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsWind tunnelAirframeLanding gearTakeoffAerodynamicsScale modelAcousticsNoise (video)Aircraft noiseAeroacousticsAerospace engineeringFuselageMach numberAirplaneMarine engineeringFull scaleCeiling (cloud)Computer scienceStructural engineeringEngineeringPhysicsSound pressureNoise reduction

Abstract

fetched live from OpenAlex

An acoustic and aerodynamic experimental study was conducted of a 7%-scale unpowered Bombardier CRJ-700 aircraft model in the NASA Ames 7- by 10-Foot Wind Tunnel for the purpose of identifying and attenuating airframe noise sources. Simulated landing, takeoff and approach configurations were evaluated at Mach 0.22 and 0.26. With a phased-microphone array mounted in the ceiling over the inverted model, various noise sources in the high-lift system, landing gear, fins, and other miscellaneous components were located and compared for sound level and frequency at one flyover location. Numerous model modifications and noise-alleviation devices were evaluated. Simultaneous with acoustic measurements, aerodynamic forces were recorded to document aircraft conditions and any performance changes caused by the geometric modifications. Such performance changes were small and are not reported here. Ten airframe noise sources were identified that might be important to approach and landing noise of the full-scale aircraft The top five noise sources were: a) slat gap, b) main gear, c) flap tips at wing crank, d) flap inboard gap, and e) slat inboard tip. Relative strengths of these sources were documented along with their dependence on aircraft configuration and operating condition. Although the data were scaled to full-scale frequencies, no extrapolation to full-scale flyover was attempted.

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.

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 score0.086
Threshold uncertainty score0.697

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 science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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