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Record W2732166517 · doi:10.4050/f-0070-2014-9667

OH-58 Block II Control Law Upgrades

2014· article· en· W2732166517 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsBlock (permutation group theory)Control (management)Computer scienceLawPolitical scienceMathematicsArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

The Bell Helicopter OH-58 Block II Concept Demonstrator was developed under an internal research and development program. In 2011, the Block II aircraft demonstrated HOGE Performance at 6000 ft pressure altitude, 95 degrees Fahrenheit (6k95F), for test conditions that exceeded the maximum gross weight. As part of the overall development program, the OH-58D baseline control laws were updated to improve handling qualities. This paper describes the control law changes and the resulting improved handling qualities. VMS studies were initiated to assess the main rotor cyclic actuator arrangement. CIFER and COPTER linear aircraft models were developed to support the analysis in conjunction with the CONDUIT flight control design tool. Ultimately, Level 1 Handling Qualities were achieved in the demonstrator aircraft. Predicted optimal SCAS gains were tuned during flight tests. ADS-33 MTE maneuvers were evaluated by both Bell and Army flight test pilots. Pilots were very impressed by the improved handling qualities achieved through this process, making the OH-58 Block II Demonstrator a great candidate for a potential future light scout/attack helicopter.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.004
GPT teacher head0.176
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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