Development of a transfer model for the design and the operation of sodium purification systems for fast breeder reactors
Bibliographic record
Abstract
Operating a Sodium Fast Reactor (SFR) in reliable and safe conditions requires mastering the quality of the sodium fluid coolant, regarding oxygen and hydrogen impurities contents. A cold trap is a purification unit in SFR, designed to maintain oxygen and hydrogen contents within acceptable limits. The purification of these impurities is based on crystallization of sodium hydride on cold walls and sodium oxide or hydride on wire mesh packing. Indeed, as oxygen and hydrogen solubilities are nearly nil at temperatures close to the sodium melting point, i.e., 97.8 °C, on line sodium purification can be performed by cooling down liquid sodium flows and promoting crystallization of sodium oxide and hydride. However, the management of cold trap performances is necessary to prevent from unforeseen maintenance operations, which could induce shut‐down of the reactor. It is thus essential to understand how a cold trap fills up with impurities crystallization in order to optimize the design of this system and to overcome any problems during nominal operation. This paper deals with the mathematical modelling of crystallization process in a cold trap and predicts the location and the amount of the impurities deposit, on cold walls for sodium hydride and on wire mesh packing for sodium oxide. A model of the front propagation by “diffuse deposit interface method” was developed and sensitivity to various parameters was evaluated. These results will enable to understand the consequences of the impurities deposited on the hydrodynamics and heat transfer in a cold trap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".