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Record W2096946704 · doi:10.2514/1.j051016

On Strain-Rate Dependence of Kinetic Energy in Homogenization Approach: Theory and Experiment

2012· article· en· W2096946704 on OpenAlexafffund
Armaghan Salehian, Yan Chen

Bibliographic record

VenueAIAA Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaDefense Advanced Research Projects Agency
KeywordsHomogenization (climate)Kinetic energyMaterials scienceStrain rateMechanicsThermodynamicsClassical mechanicsPhysicsComposite material

Abstract

fetched live from OpenAlex

The presented work pertains to a homogenization-modeling technique developed for structures of repeated pattern to predict their dynamic behavior. Strain and kinetic-energy expressions for the fundamental element are found along with necessary assumptions that are made to reduce the order of the model. Subsequently, the partial differential equations for the equivalent one-dimensional model are derived. The methodis applied to a planar truss structure,andthenatural-frequencyresultsarevalidatedagainsttheexperimentalvaluesaswellasthe finiteelement results.The advantageof the presentworkover thepreviously established homogenizationmethods thatemploy the energy-equivalence technique is that the effects of strain components are included in the kinetic energy of the fundamental elements, which results in improved accuracy in the frequency estimations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.200
Teacher spread0.195 · 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 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

Citations19
Published2012
Admission routes2
Has abstractyes

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