3.3.2 Design Concept Evaluation Using System Throughput Model
Bibliographic record
Abstract
Abstract The U.S. Department of Energy (DOE) Office of Civilian Radioactive Waste Management (OCRWM) is currently developing the technical bases to support the submittal of a license application for construction of a geologic repository at Yucca Mountain, Nevada to the U.S. Nuclear Regulatory Commission. The Office of Repository Development (ORD) is responsible for developing the design of the repository surface facilities for the handling of spent nuclear fuel and high level nuclear waste. Preliminary design activities are underway to sufficiently develop the repository surface facilities design for inclusion in the license application. The design continues to evolve to meet mission needs and to satisfy both regulatory and program requirements. A system engineering approach is being used in the design process since the repository facilities are dynamically linked by a series of sub‐systems and complex operations. In addition, the repository facility is a major system element of the overall waste management process being developed by the OCRWM. Such an approach includes iterative probabilistic dynamic simulation as an integral part of the design evolution process. A dynamic simulation tool helps to determine if: the mission and design requirements are complete, robust, and well integrated; the design solutions under development meets the design requirements and mission goals; opportunities exist where the system can be improved and/or optimized; proposed changes to the mission and design requirements have a positive or negative impact on overall system performance and if the design changes may be necessary to satisfy these changes. This paper will discuss the type of simulation employed to model the waste handling operations. It will then discuss the process being used to develop the Yucca Mountain surface facilities model. The latest simulation model and the results of the simulation and how the data was used in the design evolution process will also be discussed. Since the use of dynamic simulation is iterative and integral to the design effort, future activities will also be summarized. The paper will close discussing lessons learned from applying dynamic simulation to designing complex systems, and will discuss what pitfalls to avoid and recommendations for developing flexibility in system model development.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".