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Record W4404370147 · doi:10.1115/pvp2024-122917

The Effects of Model Thickness on the Variation of Localised Stress Fields in Four-Point Bending Tests: A CPFE Study

2024· article· en· W4404370147 on OpenAlexaff
Masoud Taherijam, Hamidreza Abdolvand

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsBendingMaterials scienceStress (linguistics)Point (geometry)Variation (astronomy)Three point flexural testComposite materialStructural engineeringEngineeringPhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract Hydrogen embrittlement is one of the principal mechanisms leading to the degradation of engineering materials used in the core of nuclear reactors. This mechanism plays a major role in deciding when to replace the Zr-2.5Nb pressure tubes used in the core of CANDU nuclear reactors. The presence of localised stress fields in CANDU pressure tubes affects the diffusion of hydrogen atoms toward cracks, scratches, and other imperfections. These high-stress regions, in which the hydrogen accumulates, serve as the nucleation sites for hydrides. In this study, an in-house crystal plasticity finite element model was used to study the effects of model thicknesses on the distribution of stresses. This was done to provide a road map for comparing the results obtained from electron backscatter diffraction measurements (EBSD) to those calculated by CPFE modelling. It is shown, that within the ROI, the variation of von Mises stresses changes significantly with model thickness, while the hoop and hydrostatic stresses are only slightly affected. In all cases, the hydrostatic stresses were more pronounced at the midpoint of the sample. Additionally, it was found that to minimise the impact of the boundary conditions on the stresses at the free surface a thickness of 500 μm was found to be optimal.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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
Published2024
Admission routes1
Has abstractyes

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