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Record W4408406657 · doi:10.1115/1.4068168

Kinetic Study of Magnetite Precipitation Using a High-Temperature Flow-Through Loop: Experimental Apparatus and Testing Procedure

2025· article· en· W4408406657 on OpenAlexafffundabout
Vichaiyut Oancherd, Sarita Weerakul, C. J. Drost, Olga Y. Palazhchenko, Chutima Kongvarhodom

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsMagnetiteMaterials sciencePrecipitationNuclear engineeringFlow (mathematics)Loop (graph theory)ThermodynamicsMechanicsMetallurgyPhysicsEngineeringMeteorology

Abstract

fetched live from OpenAlex

Abstract An accurate understanding of material degradation mechanisms and the behavior of the key corrosion products of the materials of construction in nuclear systems is essential in minimizing radiation fields to ensure nuclear worker safety. There are no high-temperature data available on the precipitation constant (kp) of magnetite. Knowledge of this kinetic constant and its temperature dependence would allow for more accurate predictive modeling of the fouling of primary-side sections such as the steam generators. This work summarizes a modification of an experimental loop test section to study the kinetics of magnetite precipitation at temperatures relevant to Canada deuterium uranium (CANDU) reactor primary heat transport systems (PHTSs). A high-temperature, high-pressure flow-through loop was used to simulate the PHTS environment. A cooler test section representative of the temperature range in a typical CANDU-6 steam generator was used to induce supersaturation and precipitation down the length of the cooler. Work is ongoing to accurately quantify both the dissolved aqueous iron in the bulk coolant and the quantity of the precipitated magnetite on the pipe wall, with initial findings reported here.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.239
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 designSimulation or modeling
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
Published2025
Admission routes3
Has abstractyes

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