TRANSIENTS IN A SMALL LEAD COOLED REACTOR
Bibliographic record
Abstract
In the recent years there has been growing interest in small modular reactors (SMRs). Before this type of reactors are deployed it is necessary to assess their safety with the newest available tools. This thesis focuses on one SMR, the SEALER reactor, which is a 3 to 10 MWe lead cooled reactor intended for remote communities or mines. The designer of the SEALER reactor has previously identified a possible issue during a loss of flow transient. At the beginning of the transient, the mass flow at the pumps undergoes a fluctuation that could lead to reverse flow if amplified. The first part of the thesis was to perform an uncertainty and sensitivity analysis using an existing lumped-parameter model. Two types of transients were studied: unprotected loss of flow and unprotected overpower. Results show that, for both transients, temperatures remain well below safety limits for the entire parameters space. However, it is also found that reverse flow at the pumps is possible by changing some parameters in a realistic way. It was therefore decided to develop a more realistic model to study the same transients, which constitutes the second and main part of the thesis. The new model uses CFD for simulating the flow of coolant in the entire primary circuit. The complex components (fuel channels, pumps and steam generators) are replaced with a simple geometry and appropriate heat and momentum sources/sinks. The CFD simulation is coupled with a custom-made code for solving heat transfer in the fuel pins and to point kinetics for neutronics. To demonstrate the viability of the model, a validation exercise was performed to ensure that the CFD part is able to reproduce experimental data with important features, like temperature stratification and a jet into a plenum. Results from the new model confirm the mass flow fluctuation if a small pump flywheel is used. For a flywheel of reasonable size, transients are slow without any mass flow fluctuation, and temperature variations are small.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".