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Nuclear Science and Engineering
Fusion Science and Technology
ITER reaches major construction milestone
The 1,250-ton cryostat base is positioned over the ITER tokamak pit for installation. The base is the heaviest lift of the tokamak assembly. Photo: ITER
ITER, the world’s largest international scientific collaboration, is beginning the assembly of the fusion reactor tokamak that will include 12 essential hardware systems provided by US ITER, which is managed by Oak Ridge National Laboratory. The first major machine element to be installed is the 1,250-ton base of the cryostat, which was placed into the tokamak assembly pit on May 26. ITER is located in southeastern France.
Christopher M. Perfetti, Bradley T. Rearden
Nuclear Science and Engineering | Volume 193 | Number 10 | October 2019 | Pages 1090-1128
Technical Paper | dx.doi.org/10.1080/00295639.2019.1604048
Articles are hosted by Taylor and Francis Online.
Criticality safety analyses rely on the availability of relevant benchmark experiments to determine justifiable margins of subcriticality. When a target application lacks neutronically similar benchmark experiments, validation studies must provide justification to the regulator that the impact of modeling and simulation limitations is well understood for the application and often must provide additional subcritical margin to ensure safe operating conditions. This study estimated the computational bias in the critical eigenvalue for several criticality safety applications supported by only a few relevant benchmark experiments. The accuracy of the following three methods for predicting computational biases was evaluated: the Upper Subcritical Limit STATisticS (USLSTATS) trending analysis method; the Whisper nonparametric method; and TSURFER, which is based on the generalized linear least-squares technique. These methods were also applied to estimate computational biases and recommended upper subcriticality limits for several critical experiments with known biases and for several cases from a blind benchmark study. The methods are evaluated based on both the accuracy of their predicted computation bias and upper subcriticality limit estimates, as well as on the consistency of the methods’ estimates, as the model parameters, covariance data libraries, and set of available benchmark data were varied. Data assimilation methods typically have not been used for criticality safety licensing activities, and this study explores a methodology to address concerns regarding the reliability of such methods in criticality safety bias prediction applications.