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Record W2334095104 · doi:10.2514/6.2013-2850

Geometry Generation of Complex Unconventional Aircraft with Application to High-Fidelity Aerodynamic Shape Optimization

2013· article· en· W2334095104 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

Venue21st AIAA Computational Fluid Dynamics Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsAerodynamicsAerospaceComputer sciencePython (programming language)DragFidelityLeverage (statistics)Complex geometryHigh fidelityAerospace engineeringMechanical engineeringGeometryComputational scienceSimulationEngineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes a geometry generator capable of providing a high-quality model of unconventional aerospace vehicles with fast turnaround. These desirable but contradictory properties are made possible by striking the right balance between user interaction and modeling automation. The former is achieved through a popular Python-based command line interface and the latter through a component-based approach. Final geometries are defined analytically with watertight networks of non-uniform rational B-spline surfaces that are topologically four-sided. Such a tool is expected to leverage the high-fidelity multidisciplinary design analysis and optimization packages that are being increasingly incorporated in earlier stages in the design of novel aircraft. As an example, a drag minimization problem applied to a regional jet geometry generated by the proposed tool concludes the paper.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.434
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.231
Teacher spread0.218 · 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