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Record W2061299639 · doi:10.1115/imece2014-36676

Conceptual Design of a 200 Passenger Blended Wing Body Aircraft

2014· article· en· W2061299639 on OpenAlexafffund
Sami Ammar, Jean‐Yves Trépanier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsPolytechnique Montréal
FundersFondation J. Armand BombardierPratt and Whitney Canada
KeywordsAerodynamicsConceptual designPayload (computing)WingAerospace engineeringAutomotive engineeringAirplaneEngineeringAeronauticsRange (aeronautics)Longitudinal static stabilityComputer scienceSystems engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The Blended Wing Body (BWB) aircraft is based on the flying wing concept. For this aircraft the literature has reported performance improvements compared to conventional aircraft. However, most BWB studies have focused on large aircraft and it is not sure whether the gains are the same for smaller aircraft. The main objective of this work is to perform the conceptual design of a 200 passengers BWB and compare its performance against an equivalent conventional A320 aircraft in terms of payload and range. Moreover, an emphasis will be placed on obtaining a stable aircraft, with the analysis of static and dynamic stability. The design of BWB was carried out under the platform called Computerized Environment for Aircraft Synthesis and Integrated Optimization Methods (CEASIOM). This design platform, suitable for conventional aircraft design, has been modified and additional tools have been integrated in order to achieve the aerodynamic analysis, performance and stability of the BWB aircraft.

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 score0.004
Threshold uncertainty score0.014

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)0.0040.001

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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