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Record W2965418534 · doi:10.1080/0305215x.2019.1639691

Topology optimization of the internal structure of an aircraft wing subjected to self-weight load

2019· article· en· W2965418534 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

VenueEngineering Optimization · 2019
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWingTopology optimizationAerodynamicsBenchmark (surveying)Topology (electrical circuits)Weight functionStructural engineeringFinite element methodMinimum weightControl theory (sociology)Computer scienceEngineeringMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

A topology optimization design framework for structures including self-weight loads is presented. The proposed methodology applied to lifting structures aims to eliminate numerical instabilities due to the weight load while improving the overall solution quality. In this model, a power-law function is used to update the element density in the determination of the self-weight. The algorithm is tested and verified for a two-dimensional benchmark problem subjected to self-weight loads and a point force. Results show that the proposed method improves the discreteness of the design of structures subjected to lifting surface loads. Following the verification step, the proposed method is used to optimize the internal structure of an aircraft wing. The aerodynamic load is computed assuming a rigid wing body, and the loading condition is completed with the structure self-weight. Results show that, in this particular example, the self-weight load has a negligible influence on the optimal design.

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: Empirical · Consensus signal: none
Teacher disagreement score0.470
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 head0.002
GPT teacher head0.176
Teacher spread0.174 · 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