Performance Assessment of the Two-Phase Pump Degradation Model in the RELAP5-3D Transient Safety Analysis Code
Bibliographic record
Abstract
RELAP5-3D currently calculates two-phase pump degradation using the Aerojet Nuclear Corporation (ANC) model. This is an empirical model that relates two-phase pump performance to single-phase pump performance using a set of two-phase degradation multipliers, which are only a function of void fraction. The purpose of the present work was to assess the two-phase pump degradation model in RELAP5-3D and various sets of user-supplied two-phase degradation multipliers by modeling a full-scale, two-phase pump test facility and comparing the simulated results to experimental data. Tests conducted by Ontario Hydro Technologies (OHT) using a full-size CANDU reactor primary heat transport pump were used for this assessment. Presently, this work represents the only RELAP5-3D analysis of these tests that has been performed.The experimental data from the OHT tests and results of this assessment both indicate that there is a pressure effect, in addition to void fraction, that cannot be neglected by safety analysis codes when predicting two-phase pump performance. The RELAP5-3D results showed that the widely used Semiscale two-phase head degradation multipliers did a poor job of predicting the experimental data and utilizing pressure-specific two-phase head degradation multipliers developed by OHT significantly improved code-to-data agreement. These results identify both the inaccuracies of using the Semiscale two-phase degradation multipliers and a weakness in the present formulation of the ANC model. As a result of this work, the Idaho National Laboratory recognized the need to include a pressure dependence in the RELAP5-3D calculation of two-phase pump performance, and this capability will be available in the next release of the code.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".