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Nonlinear, Bidirectional Control of Three-Stage Aircraft Generators

2024· article· en· W4407316286 on OpenAlexaff
Jonathan M. M. Hall, Julia Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsNonlinear systemStage (stratigraphy)Control theory (sociology)Computer scienceControl (management)PhysicsArtificial intelligenceGeology

Abstract

fetched live from OpenAlex

As electrical demands grow onboard aircraft, so to does the speed and magnitude of electrical transients. Typical three-stage aircraft generators are ill-suited to respond to large transients, due to inherent variability in their control loops. A review of various methods to address this control shortcoming is conducted and a novel control strategy is selected. A nonlinear, bidirectional Generator Control Unit (GCU) is simulated, designed, and manufactured. The novel GCU is tested on a production 15-kVA aviation generator and compared to the generator’s traditional linear, unidirectional GCU. The novel GCU is found to reduce time to transient resolution and transient overshoot, without increasing the complexity of the control loop or generator design.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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 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
Published2024
Admission routes1
Has abstractyes

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