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Record W4379877603 · doi:10.2514/6.2023-4541

Simulation of a hybrid electric propulsion used to reduce aircraft asymmetry following a failure.

2023· article· en· W4379877603 on OpenAlexaff
Ana Truc-Hermel, Christophe Maury

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsPropulsionSizingElectrically powered spacecraft propulsionAerospace engineeringAutomotive engineeringTrajectoryComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-4541.vid Hybrid electric propulsion is widely studied looking for future aircraft solutions to reduce CO2 emissions. Parallel hybrid architectures offer different operating modes either in normal or abnormal conditions. Propulsion sizing, especially hybridization or electrication, can have an impact on the aircraft capabilities in term of trajectory and control. Thus, hybrid-electric propulsive system studies require anticipating those impacts. Therefore, this paper presents how a propulsion system simulation in an environment representative of aircraft flight at a preliminary level is tested. Three simulations are presented which focus on the take-off flight phase. The first simulation presents a take-off scenario with an engine failure 1 second before the decision speed. The second and the third manoeuvers calculate the minimal control speed on ground and on air. Different necessary blocks for modelling engines, aircraft flight dynamics, control and environment are described. Results show how the use of hybridization could limit asymmetry in case of failure and reduce the minimal control speed on ground and in air.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.491

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 head0.014
GPT teacher head0.259
Teacher spread0.244 · 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
Published2023
Admission routes1
Has abstractyes

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