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Record W4317651246 · doi:10.2514/6.2023-2494

Novel Approach to Characterizing Tare & Interference Effects on the Lockheed Martin CRANE Wind Tunnel Model

2023· article· en· W4317651246 on OpenAlex

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

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsWind tunnelAerodynamicsInterference (communication)Marine engineeringSubsonic and transonic wind tunnelEngineeringComputational fluid dynamicsMoment (physics)Aerospace engineeringScale modelStructural engineeringPhysicsElectrical engineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-2494.vid A novel approach for identifying and quantifying wind tunnel strut interference effects was developed for a wind tunnel test with the Lockheed Martin DARPA CRANE model. The large-scale wind tunnel measurements were done at the Wichita State University National Institute for Aviation Research Beech Wind Tunnel facility. A smaller model that included a replica of the support strut was used in the Lockheed Martin Low-speed 2 Wind Tunnel that measured the force and moment distortions created by the presence of the support strut. Differences in Reynolds numbers between the large- and small-scale facilities were determined to be insignificant to the strut interference corrections. As a result, high-quality aerodynamic force and moment data were obtained from the NIAR Beech Wind Tunnel test, which resulted in excellent agreement with computational fluid dynamic simulations.

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 categoriesInsufficient payload (model declined to judge)
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 score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.224
Teacher spread0.200 · 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