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Record W2723527028 · doi:10.2514/6.2017-3258

Aerodynamic Shape Optimization of a Box-Wing Regional Aircraft Based on the Reynolds-Averaged Navier-Stokes Equations

2017· article· en· W2723527028 on OpenAlexafffund
Timothy Chau, David W. Zingg

Bibliographic record

Venue35th AIAA Applied Aerodynamics Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEmbraerCompute Canada
KeywordsAerodynamicsWingNavier–Stokes equationsReynolds numberReynolds-averaged Navier–Stokes equationsAerospace engineeringMechanicsComputational fluid dynamicsComputer scienceControl theory (sociology)PhysicsEngineeringTurbulenceArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

The box wing is an unconventional aircraft configuration that has the potential to provide major savings in fuel consumption relative to the conventional cantilever wing. In order to further develop and evaluate this potential, high-fidelity aerodynamic shape optimization is applied to the aerodynamic design of a box wing and a cantilever wing, based on the Embraer E190 regional jet, with the latter serving as a performance baseline. The optimization framework consists of B-spline parameterization, free-form and axial deformation geometry control, an integrated mesh-movement scheme based on the theory of linear elasticity, a Newton-Krylov-Schur flow solver for the Reynolds-averaged Navier-Stokes equations, a gradient-based optimizer, and the discrete-adjoint method for gradient evaluation. Results indicate that a box-wing with a height-to-span ratio of 0.26 burns 7.61% less fuel at cruise than a conventional baseline of the same span and lift. Aerodynamic trends and trade-offs are investigated, and a weight sensitivity study is performed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.227
Teacher spread0.205 · 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.

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

Citations15
Published2017
Admission routes2
Has abstractyes

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