Boiling Water Reactor Stability Analysis by TRACE/PARCS: Modeling Effects and Case Study of Time versus Frequency Domain Approach
Bibliographic record
Abstract
The TRACE/PARCS code was applied in this work to examine the validity of the coupled three-dimensional thermal-hydraulics and neutronics system analysis codes for boiling water reactor stability analysis. The evaluation was performed against the Ringhals-1 stability tests and compared with the frequency domain analysis using the code STAB. A comprehensive assessment of modeling choices for the TRACE stability analysis has been made, including effects of time-space discretization and numerical schemes, thermal-hydraulics channel grouping, neutronics modeling, and control system modeling. It was found that with careful control of numerical diffusion, the predictions from TRACE agree reasonably well with the Ringhals-1 test results and the predictions from STAB. The benchmark results of both codes against the Ringhals stability test are found to be at the same level of accuracy. The biases for the predicted global decay ratio are ˜0.07 in TRACE results and -0.04 in STAB results. However, the standard deviations of their decay ratios are both large, ˜0.1, indicating large uncertainties in both analyses. The uncertainties in both modeling approaches are identified. Although the TRACE code uses more sophisticated neutronics and thermal-hydraulics models, the modeling uncertainty is not less than that of the STAB 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".