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3.3.2 Design Concept Evaluation Using System Throughput Model

2004· article· en· W2116550565 on OpenAlexaff
Gary Sequeira, William Mark Nutt

Bibliographic record

VenueINCOSE International Symposium · 2004
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsGolder Associates (Canada)
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungU.S. Department of Energy
KeywordsLicenseProcess (computing)Systems engineeringIterative designEngineering design processRadioactive wasteSystems designEngineeringComputer scienceOperations managementMechanical engineeringWaste management

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.276
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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