CFD modelling of discharging process in a two-tank molten salt thermal storage system
Bibliographic record
Abstract
Canadian Nuclear Laboratories (CNL) has established a lab-scale experimental facility to address the potential solidification at local cold spots, and/ or stratification in a two-tank molten salt thermal storage system, which severely influences the system's performance and safety. In the experiment design, a preliminary similarity analysis and computational fluid dynamics (CFD) simulations of the heat transfer performance during the transient discharging process have been undertaken. Reynolds number and Péclet number were chosen for the similarity criteria, and a bounding range for the inlet mass flow rate was determined based on the geometry and kinematic scaling ratio. Since the flow is mixed convection during the process, the significance of the forced convection was determined for different inlet mass flow rates. In smaller inlet flow rate cases, natural convection was found to be dominant over forced convection, resulting in stratification with a high risk of solidification. However, enhanced mixing from larger inlet mass flow rates contributed to lower temperature deviation, i.e., a better thermal mixing performance. This study was used to support the CNL operating conditions of the test facility and materials, and appropriate inlet flow rates to reproduce the corresponding flow and heat transfer phenomena in the full-scale facility.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".