Coupling of MELCOR with surrogate model for quench estimation of conical debris beds
Bibliographic record
Abstract
• The developed SM captures the quench time of a two-dimensional conical debris bed similarly but much quicker than COCOMO does. • A MELCOR/SM interface for quench estimation of conical debris beds is designed and applied to a SBO scenario in a Nordic BWR. • Coupled MELCOR/SM can predict similar containment pressure and water temperature change trend as coupled MELCOR/COCOMO. • Sensitivity analysis show that porosity has a major influence on the quench time of a conical debris bed. • Coupling of MELCOR with the developed SM extends MELCOR capability for quick quench estimation of conical debris beds. The MELCOR code as a severe accident simulation tool does not have the capability to capture the quench process of a debris bed which may form in the wet cavity during a severe accident of light water reactors (LWRs). Although the coupled MELCOR/COCOMO simulation could overcome the limitation (Chen et al., 2022), the calculation time was explosively escalated due to mechanistic modeling of debris bed thermal-hydraulics in COCOMO. To suppress the computational cost, a surrogate model (SM) was developed in our previous study (Wang et al., 2023), and its coupling with MELCOR could realize a quick estimation of the quench process of one-dimensional debris beds. The present study is an extension of the previous work, aiming at the development of a new surrogate model for the quench process of two-dimensional conical debris beds. The new surrogate model (SM) was based on artificial neural networks (ANNs) and trained by the database from COCOMO calculations of various conical debris beds quenched in the reactor cavity of a Nordic boiling water reactor (BWR). The MELCOR was then coupled with the new SM to simulate a postulated station blackout (SBO) scenario in the BWR. The results show that the coupled MELCOR/SM simulation could provide similar ex-vessel debris bed quench period and containment pressure/temperature trends as the coupled MELCOR/COCOMO. Compared with the MELCOR standalone calculation, the coupled calculations predicted earlier points of time for water pool saturation and containment venting, since the heat transfer from conical debris bed to water pool is faster in the coupled simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".