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Record W4389540746 · doi:10.17118/11143/21153

Couple-stress theory for cellular metamaterials

2023· article· en· W4389540746 on OpenAlexaff
Shahin Eskandari, Benyamin Shahryari, Hamid Akbarzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsMetamaterialPath (computing)Chain (unit)Displacement (psychology)Computer scienceEnergy (signal processing)Topology (electrical circuits)Materials sciencePhysicsOptoelectronicsElectrical engineeringEngineeringProgramming language

Abstract

fetched live from OpenAlex

All materials from natural to synthetic, metals to polymers, and crystalline to amorphous, have microstructure in different length scales; however, classical Cauchy continuum theory does not have any characteristic length and cannot recognize the size effect.Crystalline materials are categorized into thirty-two symmetry classes based on their intrinsic microstructure.However, they can only be categorized into nine classes based on their overall anisotropic behavior in the classical theory of elasticity.This is due to the central nature of deformation and force measures, i.e., the symmetry of strain and stress tensors.Implementing a bottom-up approach based on an augmented asymptotic homogenization, we present a consistent and self-sufficient effective generalized continuum theory for materials with microstructures in 3D, 2D and 1D spaces.Our theories connect the three-dimensional continuum theories and the one-dimensional beam and two-dimensional plate theories.The accuracy of these models is investigated by comparing them to the detailed finite element models and experiments performed on 3D printed samples.The proposed models provide a benchmark for the rational design, classification, and manufacturing of mechanical metamaterials with programmable deformation modesKeywords: Metmaterials, Multistable chain, Continues path, Snap-back energy release.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.239

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 head0.010
GPT teacher head0.202
Teacher spread0.193 · 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 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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