原子力発電所における多忠実度シミュレーションを用いた動的確率論的リスク評価
Bibliographic record
Abstract
Dynamic probabilistic risk assessment (PRA) more explicitly treats timing issues and stochastic elements of risk models. It extensively resorts to iterative simulations of accident progressions for the quantification of risk triplets including accident scenarios, probabilities and consequences. Dynamic PRA leverages the level of detail for risk modeling while intricately increases computational complexities, which result in heavy computational cost. This paper proposes to apply multi-fidelity simulations for a cost- effective dynamic PRA. It applies and improves the multi-fidelity importance sampling (MFIS) algorithm to generate cost-effective samples of nuclear reactor accident sequences. Sampled accident sequences are paralleled simulated by using mechanistic codes, which is treated as a high-fidelity model. Adaptively trained by using the high-fidelity data, low-fidelity model is used to predicting simulation results. Interested predictions with reactor core damages are sorted out to build the density function of the biased distribution for importance sampling. After when collect enough number of high-fidelity data, risk triplets can be estimated. By solving a demonstration problem and a practical PRA problem by using MELCOR 2.2, the approach has been proven to be effective for risk assessment. Comparing with previous studies, the proposed multi-fidelity approach provides comparative estimation of risk triplets, while significantly reduces computational cost.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".