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Record W4312768285 · doi:10.31399/asm.cp.itsc2017p0867

Heat Treatment of Cold Spray Copper on the Closure Weld Zone of Used Nuclear Fuel Containers

2017· article· en· W4312768285 on OpenAlexaboutno aff
Jason D. Giallonardo, Dominique Poirier, J.-G. Legoux, P. Vo, C. Hoang, Delmar Doyle

Bibliographic record

VenueThermal spray · 2017
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceGas dynamic cold sprayCoatingDuctility (Earth science)MetallurgyWeldingCopperCorrosionThermal sprayingComposite materialCreep

Abstract

fetched live from OpenAlex

Abstract The Nuclear Waste Management Organization (NWMO) has proposed the concept of a deep geological repository (DGR) for the storage of Canada’s used nuclear fuel. A major engineered component is the used fuel container (UFC) consisting of a steel core coated with copper for corrosion resistance. The copper coating is required to have sufficient ductility and adhesion strength to the steel substrate for loading requirements under DGR conditions. The NWMO has identified two coating technologies for the application process: electrodeposition and cold spray. Electrodeposition is utilized to coat the bulk of the UFC components (i.e., hemi-spherical head and lower assembly). A portion of the hemi-spherical head and the lower assembly openings remain uncoated in order to facilitate the final assembly closure weld process after fuel loading. This area is then cold sprayed with copper to complete the coating on the steel. Since the cold sprayed coating is highly strained in the as-sprayed state, it requires a heat treatment to impart ductility. The ductility is assessed indirectly by measuring the hardness of the material before and after the heat treatment. A recent advancement on this front includes the implementation of an optimized band heat treatment method to prototype UFC’s.

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 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 score0.038
Threshold uncertainty score0.611

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)0.0000.000

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 head0.020
GPT teacher head0.240
Teacher spread0.220 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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