Performance assessment model development and analysis of radionuclide transport in the unsaturated zone, Yucca Mountain, Nevada

2003 ◽  
Vol 62-63 ◽  
pp. 249-268 ◽  
Author(s):  
Bruce A Robinson ◽  
Chunhong Li ◽  
Clifford K Ho
2007 ◽  
Vol 30 (1) ◽  
pp. 118-134 ◽  
Author(s):  
Ming Ye ◽  
Feng Pan ◽  
Yu-Shu Wu ◽  
Bill X. Hu ◽  
Craig Shirley ◽  
...  

2012 ◽  
Vol 11 (4) ◽  
pp. vzj2011.0133 ◽  
Author(s):  
Bruce A. Robinson ◽  
James E. Houseworth ◽  
Shaoping Chu

2021 ◽  
Author(s):  
Yalda Mousazadeh ◽  
Homayoun Sadeghi-Bazargani ◽  
Ali Janati ◽  
Mahboub Pouraghaei ◽  
Farzad Rahmani ◽  
...  

Abstract Background: Trauma is a major cause of death worldwide, especially in developing countries. The increasing cost of health care and the differences in the quality of provided services indicates the need to assess trauma care. This study aimed to develop and use a performance assessment model for in-hospital trauma care with a focus on traffic injures.Methods: This multi-method study was conducted in three main phases of indicators determination, model development, and model application. Trauma care performance indicators were extracted through literature review and were confirmed using a two-round Delphi survey and experts’ perspective. Two focus group discussions and 16 semi-structured interviews were held to design the initial model. In the next step, components and final form of the model were confirmed following pre-determined factors including importance and necessity, simplicity, clarity, and relevance. Finally, the model was tested by applying it in a trauma center. Results: A total of 50 trauma care indicators were approved after reviewing the literature and obtaining the experts' views. The final model consisted of six components of assessment level, teams, methods, scheduling, frequency, and data source. The model application revealed problems of a selected trauma center in terms of information recording, patient deposition, some clinical services, waiting time for depositing, recording of medical errors and complications, patient follow-up, and patient satisfaction.Conclusion: Performance assessment with an appropriate model can identify deficiencies and failures of provided services in trauma centers. Understanding the current situation is one of the main requirements for designing any quality improvement programs.


1993 ◽  
Vol 333 ◽  
Author(s):  
Daniel B. Bullen

ABSTRACTA mathematical model to predict the cumulative failure distribution for the engineered barrier system employed in a deep geologic disposal facility as a function of container design and near-field environmental conditions has been developed. The model employs Weibull and exponential distributions to describe cumulative container failures as a function of time. Parameter values employed in the model are based upon simple, time-dependent, mechanistic models and relevant corrosion data, which describe failure of individual components of the container as a function of environmental conditions.Recent developments in container design for the Yucca Mountain site center on the possible deployment of Multi-Purpose Containers (MPC). These containers will be designed and constructed to serve as transport casks, interim storage containers, and disposal containers. The current container performance assessment model is applied to evaluate the long-term performance of various MPC designs under the areal power density and heat transfer regimes expected in the Yucca Mountain environment. This model has previously been employed to describe the performance of the container as one part of a risk-based performance assessment of the Yucca Mountain site.The relative importance of container design, areal power density, and dominant heat transfer mode on predicted MPC performance is demonstrated through comparison of the cumulative container failure distributions for each MPC design when exposed to expected Yucca Mountain environmental conditions.


Author(s):  
Donald A. Kalinich ◽  
Michael L. Wilson

Abstract Seepage into the repository drifts is an important factor in total-system performance. Uncertainty and spatial variability are considered in the seepage calculations. The base-case results show 13.6% of the waste packages (WPs) have seepage. For 5th percentile uncertainty, 4.5% of the WPs have seepage and the seepage flow decreased by a factor of 2. For 95th percentile uncertainty, 21.5% of the WPs have seepage and the seepage flow increased by a factor of 2. When seepage was forced on 100% of the WPs the seepage flow increased by a factor of 3.


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