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Integrated Design of Micro Configuration and Macro Arrangement with Scale-Coupled Effect for Maximum the Fundamental Frequency

2010· article· en· W2045245125 on OpenAlexaff
Yuan Dong Liu, Yi Hui Yin

Bibliographic record

VenueAdvanced materials research · 2010
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsCanadian Association of Emergency Physicians
FundersScience and Technology Development FundChina Academy of Engineering Physics
KeywordsTopology optimizationMacroMicrostructureTopology (electrical circuits)Network topologyScale (ratio)Materials scienceFinite element methodMechanical engineeringStructural engineeringComputer scienceEngineeringComposite materialPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

It is well known that structural behaviors of composite solids are determined by topology of microstructures of different sizes. In this paper a concurrent topology optimization method for integrated design of materials and structures with periodical microstructure was presented. The microstructures were assumed to be uniform in macro scale and heterogeneous in micro scale and the optimization object was to maximize the material fundamental frequency. Design variables for structure and material microstructures were defined, independently. RAMP (Rational Approximation ofMaterial Properties) was adopted to ensure clear topologies in both macro and micro scales. Design variables for structure and material microstructures were integrated into one system by using the super-element method. Influences of Representative Volume Element sizes, the microstructure configuration and macro arrangement are investigated. Numerical experiments validate the proposed method which can be used as an innovative design concept for the lightweight structures.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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