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Record W4387334057 · doi:10.2514/1.c036927

Off-Design Performance Analysis of a Parallel Hybrid Electric Regional Turboprop Aircraft

2023· article· en· W4387334057 on OpenAlexafffund
Dominik Quillet, Vincent Boulanger, David Rancourt

Bibliographic record

VenueJournal of Aircraft · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsTurbopropPropulsionFuel efficiencyAutomotive engineeringSizingPayload (computing)Aircraft fuel systemPowertrainRobustness (evolution)EngineeringAerospace engineeringComputer scienceTorqueCombustion

Abstract

fetched live from OpenAlex

Hybrid electric propulsion is one of the alternative solutions for reducing fuel burn and lower [Formula: see text] emissions while keeping a reasonable battery mass for regional turboprop aircraft operating on short routes. Most studies reporting fuel burn reductions evaluate the aircraft on the design mission, although regional transport aircraft rarely operate under these conditions. Therefore, considering its off-design performance is essential for providing a more complete understanding of aircraft capabilities under various operating conditions. Under these flight conditions, the multi-energy management aspect of hybrid propulsion and the fixed size of the batteries could have a significant impact on the system robustness in off-design operation. In this study, the off-design performance of an existing regional turboprop aircraft retrofitted with a parallel hybrid electric powertrain is assessed. Fuel burn benefits are evaluated on the payload–range diagram for an initial hybrid design and compared to the baseline aircraft. Then, using a novel sizing approach, considering a typical mission operation, this study shows an average improvement of [Formula: see text] percentage point on fuel burn benefits relative to the initial hybrid aircraft, creating a more robust 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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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 designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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