Reliability and Safety Assessment Model for LOCA Event in BWRX-300
Bibliographic record
Abstract
SUMMARY & CONCLUSIONS The BWRX-300, an advanced Small Modular Reactor (SMR), represents the latest evolution in boiling water reactor technology, drawing on over six decades of experience. This reactor emphasizes the use of Passive Safety Systems (PSSs) that leverage natural forces, such as gravity and natural circulation, to enhance safety and reliability while minimizing the need for active components and manual interventions. Building on the design of the Economic Simplified Boiling Water Reactor (ESBWR), the BWRX-300 aims to improve safety features while reducing capital costs. However, the innovative nature and complexity of these passive systems present significant challenges in accurately assessing their reliability. In the event of a Loss-of-Coolant Accident (LOCA), critical safety functions are performed by systems including the Control Rod Drive (CRD) system and Boron Injection System (BIS) for reactivity control, the Isolation Condenser System (ICS) for core cooling and long-term heat removal, and the Reactor Pressure Vessel Isolation Valves (RPVIVs) for reactor isolation. Each of these systems plays a vital role in preventing core damage by ensuring over-pressure protection and maintaining sufficient coolant levels. This paper conducts a comprehensive reliability and safety assessment for a LOCA scenario in the BWRX-300 using dynamic Probabilistic Risk Assessment (PRA) modeling through the EMRALD tool. The study evaluates the effectiveness of these safety systems and identifies potential vulnerabilities in their interdependent functions. The findings advance the understanding of the BWRX-300's operational safety under critical conditions and provide insights that could guide future improvements in SMR design and regulatory standards, underscoring the importance of robust safety systems in nuclear reactor engineering. The findings of this paper underscore the importance of robust maintenance protocols and the potential need for further simulations to explore low-probability failure modes. The results contribute valuable insights into the operational safety of the BWRX-300, offering recommendations that could inform future improvements in SMR design and regulatory frameworks, ultimately enhancing the safety and reliability of nuclear reactor operations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".