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Record W2891808721 · doi:10.1088/1361-651x/aae2c8

Multiscale approach for determining hydrogen diffusivity in zirconium

2018· article· en· W2891808721 on OpenAlexafffund
Manura Liyanage, Ronald E. Miller

Bibliographic record

VenueModelling and Simulation in Materials Science and Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsSimon Fraser UniversityCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceZirconiumThermal diffusivityHydrogenThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

Abstract The current research presents an approach which is used to determine the diffusivity of hydrogen in the hexagonal close packed (hcp) zirconium crystal, using a combination of first principles calculations and kinetic Monte Carlo (KMC) simulations. Rate constants found through the energy landscapes of hydrogen motion between different interstitial sites in the zirconium lattice were used in KMC to determine the values of bulk diffusivity. We simulated a stress-free environment to eliminate the effect of stress. It is hypothesized that stress could act as a driving forces for diffusion. We found that hydrogen diffusivity in hcp Zr is closely isotropic, with a slightly higher diffusivity in the axis direction. The individual diffusion jumps were closely investigated to identify the reasons for the isotropic nature of the diffusivity in the anisotropic hcp Zr lattice. We also use this study to validate the modeling approach followed to extend it to other diffusion studies of similar nature, which comprises of clearly understood diffusion steps.

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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.031
GPT teacher head0.244
Teacher spread0.213 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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