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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 OpenAlexafffund
Kai A. James, Joaquim R. R. A. Martins

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2008
Admission routes2
Has abstractyes

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