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Record W2091051234 · doi:10.2514/1.j053575

Two-Level Free-Form and Axial Deformation for Exploratory Aerodynamic Shape Optimization

2015· article· en· W2091051234 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.

Bibliographic record

VenueAIAA Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsShape optimizationWingtip deviceAerodynamicsSolverMorphingTransonicDeformation (meteorology)Computer scienceMathematicsStructural engineeringMathematical optimizationMechanicsFinite element methodEngineeringMaterials sciencePhysics

Abstract

fetched live from OpenAlex

An intuitive shape parameterization and control technique suitable for high-fidelity aerodynamic shape optimization is presented. It relies on the principles of free-form and axial deformation, enabling thorough exploration of the design space while keeping the number of design variables manageable. Surface sensitivities to the design variables are readily available; their inclusion in a highly efficient and robust adjoint-based optimization methodology involving linearly elastic volume mesh deformation and a Newton–Krylov solver for the Euler equations is described. The flexibility of the proposed approach is demonstrated through the exploratory shape optimization of a three-pronged feathered winglet, leading to a span efficiency of 1.19 under a height-to-span ratio constraint of 0.1, and an optimization of a regional jet wing at transonic speed where a winglet is allowed to develop starting from a planar wingtip extension, leading to an 18.8% reduction in drag.

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 categoriesnone
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 score0.564
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.229
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