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Record W2097631844 · doi:10.2514/2.2754

Tail Buffet of F/A-18 at High Incidence with Sideslip and Roll (Part 1)

2001· article· en· W2097631844 on OpenAlexafffund
Stavros Tavoularis, S. Marineau-Mes, A. Woronko, B. H. K. Lee

Bibliographic record

VenueJournal of Aircraft · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersMinistère de la Défense Nationale
KeywordsMach numberWind tunnelAngle of attackBending momentRADIUSVortexMechanicsPhysicsMoment (physics)MathematicsGeometryAerodynamicsClassical mechanicsThermodynamics

Abstract

fetched live from OpenAlex

The unsteady pressure was measured simultaneously at 24 locations on both sides of a vertical e n of a rigid, 6% scale model of the F/A-18 aircraft in a wind tunnel. This pressure was integrated over the entire e n surface and over time to provide the mean and rms normal force, bending moment, and torsional moment on the e n in order to determine its buffet loading. Results are available for Mach numbers M =0.25, 0.60, and 0.80; angles of attack ®=25, 30, and 32.5 deg; and a sideslip angle i 15 deg < ¯ < 15 deg or a roll angle i 30 deg < ‐ ’ < 30 deg. At zero roll and sideslip the mean force and moment coefe cients generally increased with increasing angle of attack; but as the sideslip or roll angles increased some coefe cients changed sign, and their relationships to ® became more complex. A measurable trend of the mean force coefe cient to decrease with increasing Mach number was also observed. The rms normal force coefe cient increased signie cantly as ® increased from 25 to 32.5 deg, but showed no appreciable trends as the Mach number increased from 0.25 to 0.80. The wind-tunnel tests are complemented by a e ow-visualization study of the leading-edge extensions vortices of a 1:48 scale model of the F/A-18 in a water tunnel, showing the vortex burst locations at different aircraft orientations. Part 2 of this study presents statistical results of the forces and moments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.366

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 head0.007
GPT teacher head0.188
Teacher spread0.182 · 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 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

Citations7
Published2001
Admission routes2
Has abstractyes

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