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Record W4402454946 · doi:10.11159/icmie24.157

Topology-Lattice Optimization of an Extendable Wing

2024· article· en· W4402454946 on OpenAlexvenueno aff
Altan Kayran, Buğra Aksoy

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsTopology (electrical circuits)Topology optimizationComputer scienceWingLattice (music)MathematicsEngineeringPhysicsCombinatoricsAerospace engineeringFinite element methodStructural engineering

Abstract

fetched live from OpenAlex

The main focus of this study is to develop a structural concept for an extendable wing for munition utilizing lattice cell assisted topology optimization followed by size optimization of the radii of the strut-based lattice cells.The topology optimization of the design space of the wing structure is performed using the Solid Isotropic Material with Penalization (SIMP) method for minimum compliance subject to volume fraction constraint.Depending on the relative density distribution, the topology optimized region is filled with strut-based lattice structures.Following the lattice assisted topology optimization, size optimizations for the radii of the lattice cells are performed using single and multi-objective functions.The resulting optimized wing structures are compared with each other in terms of their mechanical performances and weight.The results shows that the mechanical performance of the wing structure can be increased by employing lattice cell structures.Size optimizations performed using single and multi-objective functions also showed that the mechanical performances of the lattice assisted topology optimized wing structures are very close to each other and as a result of two level optimization approximately 17.5% weight reduction is achieved.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.841

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 head0.005
GPT teacher head0.208
Teacher spread0.202 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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