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Record W7164191387 · doi:10.4050/f-0079-2023-1168

Flight Testing of Automated Rotor Track and Balance using Active Trim Tab and Pitch Control Rod Technologies

2023· article· W7164191387 on OpenAlexaff
Preston Bates, Ray Vanacore, James DiOttavio, Patrick Reilly

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsTrimRotor (electric)VibrationTrack (disk drive)Flight envelopeDynamic balanceFlight testBalance (ability)Helicopter rotor

Abstract

fetched live from OpenAlex

An innovative prototype in-flight automated rotor track and balance (RTB) system using advanced algorithms to command active rotor blade trim tabs and pitch control rods was demonstrated in a full-scale UH-60M flight test conducted by the U.S. Army. The primary technical objective was to reduce main rotor one per revolution (1P or 1/Rev) vibrations and maintain them at or below vibration acceptance levels throughout the entire flight envelope and achieve this in the presence of blade anomalies that create rotor imbalances. A secondary objective was to minimize higher harmonic vibration at two per revolution (2P or 2/Rev) and three per revolution (3P or 3/Rev) frequencies, without negatively impacting 1P. The flight test demonstration showed the system successfully met the desired performance objectives with the necessary characteristics to operate safely under normal flight 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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.

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
Published2023
Admission routes1
Has abstractyes

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