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Record W2324968360 · doi:10.2514/6.2008-6081

Three-Dimensional Structural Topology Optimization of an Aircraft Wing Using Level Set Methods

2008· article· en· W2324968360 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

Venue12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsTopology optimizationLevel set methodChord (peer-to-peer)WingCantileverTopology (electrical circuits)Level set (data structures)Shape optimizationComputer scienceOptimization problemVoid (composites)Set (abstract data type)Mathematical optimizationStructural engineeringMathematicsFinite element methodEngineeringMaterials science

Abstract

fetched live from OpenAlex

A three-dimensional structural topology optimization framework is applied to the problem of aircraft wing design. The approach presented is unique in that the working domain of the design problem is given by the full three-dimensional region inside the wing skin, with no assumptions being made with regard to the number, location or orientation of the structural members. The wing is modelled as an elongated cantilever beam with a taper ratio of 0.7 in both chord and thickness. Distributed, pressure-type loading is applied to the top and bottom surfaces. The optimization is performed using a level set method, with advection velocities given by classical shape sensitivity information along the material boundary. We also devise a method for enforcing local stress constraints that accounts for the void regions associated with topology optimization problems and is consistent with the level set approach used.

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: Methods
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.051
GPT teacher head0.319
Teacher spread0.268 · 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