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Record W4411850991 · doi:10.4050/f-0081-2025-0087

Performance Wind Tunnel Testing of an X2 Technology® Mach-Scaled Powered Model

2025· article· en· W4411850991 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsMach numberWind tunnelAerospace engineeringMarine engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

A 1/5th scale powered coaxial rotor and propeller system has been developed and tested in the National Full Scale Aerodynamic Complex (NFAC) 40x80 ft Wind Tunnel. Test conditions include airspeeds in excess of 250 kts, the highest recorded for a rotor in edgewise flight at the NFAC. The system was studied in four configurations: a powered coaxial rotor, a powered coaxial rotor with a propeller wake rake, a powered coaxial rotor with a powered propeller, and a bare hub rotor with a propeller wake rake. The high-quality data from the test included propeller, fuselage and main-rotor performance; aerodynamic-interactions between the rotors, fuselage, empennage, and propeller; acoustics and handling-qualities attributes. These results have been used to validate physics-based rotorcraft modeling tools and enhance the quality of full-scale X2 Technology® aircraft designs. Innovative solutions to test measurement challenges included rotor shaft strain gages, balance thermal control systems, and a dual arm rotating rake to measure inflow at the propeller plane. Main rotor hub and blade system performance trends are presented as functions of speed, thrust, and body attitude. Time-averaged and unsteady pressure measurements on the fuselage, empennage, and propulsor plane characterized the interactions between the fuselage, rotor hubs and blades, empennage, and propeller over a wide range of operating conditions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.516

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)0.0000.000

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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