Kinetic Study of Magnetite Precipitation Using a High-Temperature Flow-Through Loop: Experimental Apparatus and Testing Procedure
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".