MétaCan
Menu
Back to cohort
Record W2762384841 · doi:10.1109/ccta.2017.8062767

Flight management systems for all-electric aircraft

2017· article· en· W2762384841 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue2017 IEEE Conference on Control Technology and Applications (CCTA) · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsConcordia University
FundersMitacs
KeywordsRange (aeronautics)Parameterized complexityOptimal controlMaximum principleMode (computer interface)Control theory (sociology)Pontryagin's minimum principleMinificationComputer scienceMathematical optimizationAutomotive engineeringEngineeringMathematicsControl (management)Aerospace engineeringAlgorithm

Abstract

fetched live from OpenAlex

Recent years have marked a significant step forward in the development of all-electric airplanes, some of which have been built and tested recently. This paper proposes an optimal control framework for flight management systems of all-electric aircraft. The optimal control problems of economy mode and maximum endurance will be solved using Pontryagin's minimum principle. The economy mode optimization problem corresponds to the minimization of a functional parameterized by a coefficient index that performs a trade-off between the cost of the battery charge and time-related costs. The speed for maximum endurance, the maximum endurance and the maximum range were obtained as analytical solutions of the parameters. However, the speed for maximum range and the speed for economy mode are the positive real roots of a polynomial equation of order eight, which can easily be obtained numerically. The Airbus E-Fan 1.0 model is used to obtain numerical results and validate the optimal solutions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.299
Teacher spread0.272 · 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