MétaCan
Menu
Back to cohort
Record W4389540989 · doi:10.17118/11143/20948

Aeroelastic behaviour of urban air mobility aircraft with distributedelectric propulsion subjected to urban wind gusts

2023· article· en· W4389540989 on OpenAlexaff
Antoine Boissinot, Mojtaba Kheiri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsPropulsionAeroelasticityAerospace engineeringEnvironmental scienceAerodynamicsMarine engineeringAutomotive engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

The goal of this project is to perform a parametric study on the aeroelastic behaviour of an urban air mobility (UAM) vehicle with a distributed electric propulsion (DEP) system, subjected to urban environment and to showcase the most important parameters.The study focuses on the stability analysis and time response of a wing with distributed propellers.With the increasing density of city populations, it is becoming more challenging to navigate through daily activities, leading the aerospace industry to explore environmentally friendly and economically accessible solutions such as flying taxis, also known as UAM vehicles.This solution has the benefits of increasing accessibility, safety, urban mobility, and reliability and reducing travel time and environmental impacts.However, the aeroelastic behaviour of UAM vehicles is still not fully understood, particularly since they fly in urban areas with increased turbulence and wind gusts.Additionally, contrary to conventional aircraft, UAM vehicles have the particularity of using DEP systems, affecting the distribution of force, mass, and moment of inertia along the wingspan.These aspects may be detrimental to the aircraft if the structural and propulsion parameters of the system are not suitably considered.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicAerodynamics and Fluid Dynamics ResearchFrench-language works237,207