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Record W2051210587 · doi:10.1002/nme.3112

An adaptive concurrent multiscale method for the dynamic simulation of dislocations

2011· article· en· W2051210587 on OpenAlexaff
Robert Gracie, Ted Belytschko

Bibliographic record

VenueInternational Journal for Numerical Methods in Engineering · 2011
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversity of Waterloo
FundersArmy Research OfficeOffice of Naval Research
KeywordsClassification of discontinuitiesVoid (composites)Statistical physicsDislocationFinite element methodDiscontinuity (linguistics)Enhanced Data Rates for GSM EvolutionMultiscale modelingMaterials scienceComputer sciencePhysicsMathematicsMathematical analysisThermodynamicsComputational chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract A continuum‐atomistic adaptive multiscale method is developed for the simulation of the dynamics of dislocations. Two key features of the method are (i) methods for both refining and coarsening the model, where coarsening refers to a continuum to atomistic transition and refinement to the opposite and (ii) error criteria for refining and coarsening. In coarsening of edge dislocation solutions, it is crucial to capture the discontinuities across the glide plane, which is accomplished here by the extended finite element method. Error criteria are developed in terms of energies so that the atomistic model tends to follow the core, where continuum models are generally quite inaccurate. The method is applied to two‐dimensional problems involving dislocations emitted from a void and from a crack tip. The results show good agreement with other multiscale methods and result in a large savings in computational effort. Copyright © 2011 John Wiley & Sons, Ltd.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.068
GPT teacher head0.432
Teacher spread0.363 · 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".

Quick stats

Citations61
Published2011
Admission routes1
Has abstractyes

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