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DOE awards ANS-backed workforce consortium $19.2M
The Department of Energy’s Office of Nuclear Energy recently awarded about $49.7 million to 10 university-led projects aiming to develop nuclear workforce training programs around the country.
DOE-NE issued its largest award, $19.2 million, to the newly formed Great Lakes Partnership to Enhance the Nuclear Workforce (GLP). This regional consortium, which is led by the University of Toledo and includes the American Nuclear Society, will use the funds to fill a variety of existing gaps in the nuclear workforce pipeline.
Jung-Woo Kim, Dong-Keun Cho, Nak-Youl Ko, Jongtae Jeong, Min-Hoon Baik
Nuclear Technology | Volume 203 | Number 1 | July 2018 | Pages 1-16
Technical Paper | doi.org/10.1080/00295450.2018.1426331
Articles are hosted by Taylor and Francis Online.
New methodology for a risk-based safety assessment of a geological disposal system of nuclear waste was implemented using the numerical Korea Atomic Energy Research Institute (KAERI) Performance Assessment Model (K-PAM). K-PAM was applied to a conceptual geological disposal system for pyroprocessed radioactive wastes based on the KAERI Underground Research Tunnel (KURT) site. The methodology was systematically organized for model development considering two types of external events: earthquakes and well intrusion. Following description of its conceptual models and submodules, K-PAM was partially verified by comparing the consequences of two major modules of K-PAM—engineered barrier system and natural barrier system—with those by a well-known, comparable process model using COMSOL. In addition, K-PAM was demonstrated using three scenarios: (1) the reference scenario, in which the normal consequences of the disposal system without external events could be predicted; (2) the deterministic complex scenario, in which the impacts of individual external events on the disposal system could be estimated separately; and (3) the probabilistic complex scenario, in which the efficiency of the new methodology for a risk-based safety assessment could be confirmed numerically by showing the probable maximum dose rate according to any single scenario, the convergence of risk, the dominant impacts contributing to the maximum dose rate, and the probability of occurrence of the scenario groups.