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Record W3031594887 · doi:10.5006/c2004-04677

A Flow-Impingement Facility for Corrosion and Electrochemical Studies in High–Temperature Aqueous Solutions

2004· article· en· W3031594887 on OpenAlexaff
Yucheng Lu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsMaterials scienceCorrosionAqueous solutionElectrochemistryFlow (mathematics)MetallurgyNuclear engineeringMechanicsEngineeringChemistryElectrode

Abstract

fetched live from OpenAlex

Abstract A flow-impingement facility has been developed at the AECL Chalk River Laboratories to study flow accelerated corrosion (FAC) and the electrochemical behaviour of reactor and steam generator (SG) materials. Tests can be done under conditions simulating a nuclear power plant heat transport system (HTS) and SG operation. High-temperature water is pumped through nozzles, impinging on the surface of test samples, and simulates turbulent flow conditions for accelerated corrosion tests. The flow-impingement stand was installed in a once-through autoclave system. Tests can be performed under impinging flow at temperatures up to 330°C. The maximum linear velocity of the impinging jet can be as high as 6 m/s at 300°C. Mass transfer coefficient calculations indicate that the hydrodynamic conditions under the impinging jet are equivalent to that in a 2.5-inch tube at free flow velocities between 12 to 18 m/s. This facility can be used to verify the corrosion rate of material as a function of temperature and key water chemistry parameters, such as pH, oxygen concentration, etc., in flowing high-temperature water. Accelerated corrosion tests and a variety of electrochemical measurements can also be performed under well controlled hydrodynamic and chemistry conditions using this facility. An overview will be provided of some of these test results, and their application to operating plant issues.

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 score0.005
Threshold uncertainty score0.249

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 head0.052
GPT teacher head0.282
Teacher spread0.230 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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