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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Hash Hashemian: Visionary leadership
As Dr. Hashem M. “Hash” Hashemian prepares to step into his term as President of the American Nuclear Society, he is clear that he wants to make the most of this unique moment.
A groundswell in public approval of nuclear is finding a home in growing governmental support that is backed by a tailwind of technological innovation. “Now is a good time to be in nuclear,” Hashemian said, as he explained the criticality of this moment and what he hoped to accomplish as president.
Moosung Jae, Antony D. Milici, William E. Kastenberg, George E. Apostolakis
Nuclear Technology | Volume 104 | Number 1 | October 1993 | Pages 13-36
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT93-A34867
Articles are hosted by Taylor and Francis Online.
A framework for assessing severe accident management strategies is presented using a new analytical tool, namely, influence diagrams. This framework includes multiple and sequential decisions, sensitivity analysis, and uncertainty propagation, and is applied to a proposed set of strategies for a pressurized water reactor station blackout sequence. The influence diagram associated with these strategies is constructed and evaluated. Each decision variable, represented by a node in the influence diagram, has an uncertainty distribution associated with it. Using the mean value of these distributions, a best estimate assessment is performed, and each strategy is ranked with respect to the conditional frequency of early containment failure (ECF). For the preferred alternative, the sensitivity of the results to values of the input variables is investigated. The sensitivity of the ranking itself is then considered. The distributions of the uncertain variables are also propagated through the influence diagram to rank the alternatives with respect to the uncertainty associated with the calculated conditional frequency of ECF. Finally, the sensitivity of the variance of the output distribution, given the preferred decision alternative, to the uncertainty of the input variables is investigated.