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Record W2040797512 · doi:10.2514/1.j053755

Evaluation of Aerodynamic Loads via Reduced-Order Methodology

2015· article· en· W2040797512 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAIAA Journal · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsMcGill University
FundersCompute CanadaUniversité du Québec à MontréalMcGill University
KeywordsTransonicAerodynamicsCentroidal Voronoi tessellationComputational fluid dynamicsInterpolation (computer graphics)Voronoi diagramParametric statisticsSwept wingMathematicsAeroelasticityDegrees of freedom (physics and chemistry)TurbulenceApplied mathematicsComputer scienceMathematical optimizationAerospace engineeringMechanicsEngineeringGeometryPhysics

Abstract

fetched live from OpenAlex

Centroidal Voronoi tessellation, leave-one-out cross validation, proper orthogonal decomposition, and multidimensional interpolation are integrated to define a reduced-order modeling approach for the parametric evaluation of steady aerodynamic loads. The proper orthogonal decomposition-based methodology allows reducing the number of degrees of freedom of the problem while maintaining good accuracy for the solution of complex three-dimensional viscous turbulent flows. As a result, it yields fairly accurate solutions at a fraction of the time required by standard computational fluid dynamics approaches. Three-dimensional examples for fixed- and rotary-wing cases of industrial relevance are used to assess the method in the cases of subsonic and transonic flow conditions.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.178
GPT teacher head0.380
Teacher spread0.202 · 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