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Record W7056040695

Effect of Thermal Neutrons on the Mechanical Properties of Stainless Steel and Inconel Reactor Internals

2019· other· en· W7056040695 on OpenAlexaboutno aff

Bibliographic record

VenueMecánica Computacional (Asociación Argentina de Mecánica Computacional) · 2019
Typeother
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsInconelThermalNeutron temperatureMetallographyMaterials testingNeutron irradiation
DOInot available

Abstract

fetched live from OpenAlex

The irradiation embrittlement of Reactor Vessel Internals is essential to perform the Time Limited Aging Analyses studies in view of the Life extension of Nuclear Power reactors. Heavy water reactors, such as Atucha and Embalse, has thermal flux comparable with the fast flux, therefore it is necessary to consider the efficiency of thermal neutrons in the calculation of displacements per atom.Using the SPECTER calculation code, the effective damage function for stainless steels and nickel-based alloys is obtained extending the concept introduced by Jones R. B., Edens D. J., Effects of Radiation on Materials, ASTM STP 1366, in the context of ferritic steels. Finally, the results are compared with the Canadian experience in CANDU type reactors.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.222
Teacher spread0.208 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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