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Record W2578605434 · doi:10.24423/aom.2260

Nano-inclusion with uniform internal strain induced by a screw dislocation

2016· article· en· W2578605434 on OpenAlexaff
Ming Dai, Peter Schiavone, C. F. Gao

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConformal mapInclusion (mineral)DislocationMaterials scienceDeformation (meteorology)Composite numberField (mathematics)Nano-Composite materialGeometryNonlinear systemMechanicsPhysicsMathematicsThermodynamics

Abstract

fetched live from OpenAlex

This paper addresses the question of whether it is possible to design a nanoinclusion (characterized here by the incorporation of interface effects along the material interface) to achieve a screw dislocation-induced uniform internal strain field when a composite is subjected to anti-plane shear deformation. We demonstrate the existence of such an inclusion by identifying its shape via a conformal mapping with unknown coefficients obtained through a system of nonlinear equations. Our numerical examples verify that the inclusion shape is dependent on its size and the specific uniform internal strain field. We show also that the inclusion shape is available even with increasing distance between the inclusion and dislocation. This latter fact leads to the additional conclusion that non-circular nano-inclusions which achieve uniform internal strain fields do indeed exist in a composite subjected to uniform remote anti-plane shear loading.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

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 science0.0000.000
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 head0.091
GPT teacher head0.440
Teacher spread0.349 · 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
Published2016
Admission routes1
Has abstractyes

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