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Record W3120345475 · doi:10.2514/6.2021-1234

Aircraft Wing Design Through Concurrent Thickness and Material Optimization

2021· article· en· W3120345475 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 Scitech 2021 Forum · 2021
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsQueen's University
Fundersnot available
KeywordsWingComputer scienceSelection (genetic algorithm)Relevance (law)Shell (structure)Focus (optics)Work (physics)Concurrent engineeringOptimization problemGlobal optimizationMechanical engineeringStructural engineeringMathematical optimizationEngineeringAlgorithmMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2021-1234.vid Existing optimization algorithms do not allow for simultaneous thickness optimization and material selection of non-laminate shell structures. The proposed concurrent thickness and material optimization (CTMO) scheme fills this niche, with a focus on practical, industry-style problems. A complex aircraft wing model is developed and optimized through CTMO. An extensive parameter sweep is conducted for this model, generating 384 results with different designable thickness limits and masses. These results are analyzed to show overall trends in compliance based on problem constraints. Selected results are presented qualitatively to study material selection and optimized thickness. Finally, the relevance of this work to real-world engineering studies is discussed, along with some areas for future improvement of CTMO.

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: Methods · Consensus signal: none
Teacher disagreement score0.563
Threshold uncertainty score0.955

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 communication0.0000.000
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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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