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Record W2052055001 · doi:10.1139/p07-145

Multiscale modeling of solids at the nanoscale: dynamic approach

2008· article· en· W2052055001 on OpenAlexvenueno aff
Behrouz Shiari, Ronald E. Miller, D. D. Klug

Bibliographic record

VenueCanadian Journal of Physics · 2008
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsnot available
Fundersnot available
KeywordsNanoindentationIndentationPhysicsStatistical physicsMultiscale modelingPlasticityFinite element methodNanoscopic scaleDeformation (meteorology)Computer scienceMaterials scienceThermodynamicsBioinformatics

Abstract

fetched live from OpenAlex

One major class of multiscale models directly couples a region described with full atomistic detail to a surrounding region modeled using continuum concepts and finite element methods. Here, the development of a new dynamic approach to such coupled atomistic-continuum models is discussed with insight into the key ideas and features, with emphasis on fundamental difficulties involved in dynamic multiscale models. Simulations of nanoindentation in single crystals are performed to demonstrate the power of the developed method in capturing both long-range dislocation plasticity and short-range atomistic phenomena during single or cyclic loading without the computational cost of full atomistic simulations. The effects of several process variables are investigated, including system temperature and rate of indentation. The deformation mechanisms and the surface evaluation that occur during a series of single and cyclic indentation simulations are discussed. PACS Nos.: 81.07.–b or 73.22.–f

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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