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Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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2027 ANS Winter Conference and Expo
October 31–November 4, 2027
Washington, DC|The Westin Washington, DC Downtown
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Disney World should have gone nuclear
There is extra significance to the American Nuclear Society holding its annual meeting in Orlando, Florida, this past week. That’s because in 1967, the state of Florida passed a law allowing Disney World to build a nuclear power plant.
Te-Chuan Wang, Min Lee
Nuclear Technology | Volume 206 | Number 3 | March 2020 | Pages 414-427
Technical Paper | doi.org/10.1080/00295450.2019.1653152
Articles are hosted by Taylor and Francis Online.
MAAP5 is an integral severe accident analysis program that simulates the responses of a light water reactor power plant during a severe accident. This program has been used extensively for probabilistic safety assessments, verification and validation of mitigation actions specified in severe accident management guidelines, and source term quantification. In this study, the uncertainty of in-vessel hydrogen generation predicted by the MAAP5 code was quantified. The surrogate plant that was analyzed is the Lungmen Nuclear Power Station of the Taiwan Power Company. The plant employs an advanced boiling water reactor. We performed sensitivity studies to identify the important model parameters that affect the target output parameters. A range and distribution were assigned to these parameters on the basis of experimental results and expert judgment. The number of input parameters in the analysis was 27. Multiple MAAP5 calculations were performed with an input combination generated from Latin hypercube sampling. The calculation results were analyzed parametrically and nonparametrically to determine the 95th percentile with the 95% confidence level value of the amount of in-vessel hydrogen generation. The Pearson correlation coefficient was used to determine the effect of the model parameters on the target output parameters. The analysis results provide guidance for code applications. The only parameters that pass the threshold of 0.362 for hydrogen generation in the core are FCO and TCLMAX. For hydrogen generation in the lower plenum, FOXBJ is the only input parameter that passes the threshold.