6.4.3 Risk Informed Design for System Life Cycle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Many facilty life cycle activities including design, construction, fabrication, inspection and maintenance are evolving from a deterministic to a risk‐informed basis. The risk informed approach uses probabilistic methods to evaluate the contribution of individual system components to total system performance. Total system performance considers both safety and cost considerations including system failure, reliability, and availability. By necessity, a risk‐informed approach considers both the component's life cycle and the life cycle of the system. In the nuclear industry, risk‐based approaches, namely probabilistic risk assessment (PRA) or probabilistic safety assessment (PSA), have become a standard tool used to evaluate the safety of nuclear power plants. Recent studies pertaining to advanced reactor development have indicated that these new power plants must provide improved safety over existing nuclear facilities and be cost‐competitive with other energy sources. Risk‐based approaches, beyond traditional PRA, offer the opportunity to optimize design while considering the total life cycle of the plant in order to realize these goals. The use of risk‐based design approaches in the nuclear industry is only beginning, with recent promulgation of risk‐informed regulations and proposals for risk‐based codes. This paper briefly summarizes the current state of affairs regarding the use of risk‐based approaches in design. Key points to fully realize the benefit of applying a risk‐based approach to nuclear power plant design are then presented and illustrated in a case study. These points are equally applicable to non‐nuclear facilities where optimization for cost competitiveness and/or safety is desired.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it