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Record W1986932646 · doi:10.1520/mpc20130051

Influence of Soaking Temperature and Time on Microstructure and Mechanical Properties of Water Quenched Zr–2.5Nb Alloy

2014· article· en· W1986932646 on OpenAlexaboutno aff
H.K. Khandelwal, R.N. Singh, A.K. Bind, S. Sunil, B.N. Rath, J.B. Singh, S. Kumar, J.K. Chakravartty

Bibliographic record

VenueMaterials Performance and Characterization · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceMicrostructureAlloyFracture toughnessUltimate tensile strengthTexture (cosmology)MetallurgyFabricationComposite materialDeformation (meteorology)

Abstract

fetched live from OpenAlex

Abstract In heavy water cooled nuclear reactors, Zr–2.5Nb alloy is used for pressure tubes under cold worked and stress relieved (CWSR) condition in Canadian Deuterium Uranium (CANDU) reactors and under quenched and aged condition in RBMK and Fugen reactors. Because of its ease of use and the lower cost of fabrication, most of the work reported in literature is focused on the development and characterization of cold worked and stress relieved pressure tube material. However, recent work showed that the tubes manufactured using quenched and aged route showed a lower and predictable rate of in-reactor deformation during reactor operation as compared to those fabricated using the CWSR route. One of the important stages in the fabrication of heat treated pressure tube material is solution heat treatment (SHT), which governs the microstructure and hence, the mechanical properties of pressure tubes. Usually, SHT is carried out in two phase (α + β) region at a temperature closer to the β transus. The present work characterizes the influence of different SHT parameters such as soaking temperature (850, 870, and 890°C) and duration (15 and 30 min) on microstructure, texture, and Nb partitioning between phases and mechanical properties such as tensile properties, hardness, and fracture toughness. Fracture toughness parameters were determined as per ASTM standard E1820-11. Optical microscopy was used for microstructure, X-ray diffraction for texture, and EPMA for Nb partitioning investigation. The fracture surfaces were examined using SEM.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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)0.0010.000

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 head0.005
GPT teacher head0.173
Teacher spread0.168 · 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 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".

Quick stats

Citations10
Published2014
Admission routes1
Has abstractyes

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