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Record W2196646933 · doi:10.1115/pvp2015-45137

Experimental Investigation of Landing Light Orientation on Landing Gear Noise

2015· article· en· W2196646933 on OpenAlexaff
Eric Salt, Marko Arežina, James Lepore, Samir Ziada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWakeAcousticsPhysicsWind tunnelNoise (video)OpticsParticle image velocimetryOrientation (vector space)SplitterFrequency bandGeologyMechanicsTurbulenceEngineeringGeometryElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

A simplified model of a landing gear is tested in a wind tunnel to investigate the effect of the landing light orientation on the resulting noise generation. Examination of the near-field pressure fluctuations, combined with phase-locked stereoscopic particle imaging velocimetry (SPIV) of the unsteady wake identified two distinct sources of pressure fluctuations. The higher frequency source has a wide frequency band and is situated in the outer regions of the wake near the lights. However, the lower frequency source is found to be stronger, has a narrower frequency band, and is developed further downstream in the wake, closer to the wake centerline. The lower frequency source is observed to be rather robust as it is hardly affected by the orientation of the landing lights, whereas the higher frequency source becomes weaker as the distance between the lights is reduced. The effect of a splitter plate positioned downstream of the strut is also investigated as a means of disrupting the lower frequency pressure fluctuations. Although the lower frequency source is considerably reduced by the splitter plate, substantial enhancement of the higher frequency source is observed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.260

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 head0.030
GPT teacher head0.274
Teacher spread0.244 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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