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Record W2990357769 · doi:10.1115/pvp2019-93318

Specimen Curvature and Size Effects on Crack Growth Resistance From Compact Tension Specimens of CANDU Pressure Tubes

2019· article· en· W2990357769 on OpenAlexaff
Bruce W. Williams, W. R. Tyson, C. Hari Manoj Simha, Bogdan Wasiluk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear Safety CommissionUniversity of Guelph
Fundersnot available
KeywordsFracture toughnessMaterials scienceCompact tension specimenFracture mechanicsTension (geology)CurvatureComposite materialPressure vesselCrack growth resistance curveToughnessFracture (geology)Structural engineeringCrack closureCompression (physics)EngineeringGeometryMathematics

Abstract

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Abstract CSA Standard N285.8 requires leak-before-break and fracture protection for Zr-2.5Nb pressure tubes in operating CANDU reactors. In-service deuterium uptake causes the formation of hydrides, which can result in additional variability and reduction of fracture toughness. Pressure tube fracture toughness is assessed mainly through rising pressure tube section burst tests. Given the length of the ex-service pressure tubes required for burst testing and the requirement to increase the hydrogen content of irradiated ex-service pressure tubes, only a limited number of burst tests can be performed. Using small-scale compact tension, C(T), specimens are advantageous for obtaining a statistically significant number of fracture toughness measurements while using less ex-service pressure tube material. This work focuses on the study of C(T) geometry designs in order to obtain crack growth resistance and fracture toughness closer to those deduced from burst tests. Because C(T) specimens must be machined from pressure tubes of about 100 mm in diameter and 4 mm in wall thickness, they are out-of-plane curved. As well, they undergo significant tunnelling during crack extension. These two factors can result in a violation of the ASTM standard for fracture toughness testing. The current work examined the influence of specimen curvature and tunnelled crack front on the crack growth resistance curve, or J-R curve. Finite element (FE) models using stationary and growing cracks were used in a detailed numerical investigation. To capture crack tunnelling in the FE models, a damage mechanics approach was adopted, with the critical strain to accumulate damage being a function of crack front stress triaxiality. The J-integral numerically estimated from the domain integral approach was compared to the J-integral calculated from the analytical equations in the ASTM E-1820 standard. In most cases, the difference between the numerical and the standard estimations was less than 10%, which was considered acceptable. It was found that at higher load levels of load-line-displacement, specimen curvature influenced the J-integral results. Crack tunnelling was shown to have a small influence on the estimated J-integrals, in comparison with the straight crack fronts. A modest number of experiments were carried out on unirradiated Zr-2.5Nb pressure tube material using three designs of curved C(T) specimens. It was found that the specimens of both designs that featured a width of 34 mm had more than twice the crack extension of the specimens of the 17-mm width design. The 17-mm width specimens are used mainly to assess the small-scale fracture toughness of pressure tube material. Additionally, the applied J-integral at the maximum load was about 1.4 times higher for the larger-width C(T) specimens. These C(T) specimens also produced J-R curves with greater crack extensions, which were closer to those obtained from the pressure tube section burst tests. Artificially hydrided pressure tube material was not considered in the current work, to avoid any potential source of experimental variability; however, it should be considered in future work.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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)0.0030.000

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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.

Study designBench or experimental
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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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