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Fusion Science and Technology
Latest News
In transition: Commercializing fusion power
Commercial fusion power is closer than ever. There are now around 30 U.S. fusion companies, several of which claim to be on track to connect to the grid as early as the 2030s.
Tokamak and laser inertial confinement approaches benefit from decades of research at facilities such as the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory and ITER, with alternative concepts including stellarator, magnetic mirror, and Z-pinch confinement also making notable progress as private and government funding for fusion increases.
Emily R. Wolters, Edward W. Larsen, William R. Martin
Nuclear Science and Engineering | Volume 174 | Number 3 | July 2013 | Pages 286-299
Technical Paper | doi.org/10.13182/NSE12-72
Articles are hosted by Taylor and Francis Online.
In this paper, two modifications to improve the efficiency of Lee et al.'s recently proposed “CMFD [coarse-mesh finite difference]-accelerated Monte Carlo” method for neutron criticality problems are presented and tested. This CMFD method employs standard Monte Carlo techniques to estimate nonlinear functionals (ratios of integrals), which are used in low-order CMFD equations to obtain the eigenvalue and discrete representations of the eigenfunction. In a “feedback” procedure, the Monte Carlo fission source is then modified to match the resulting CMFD fission source. The proposed new methods differ from the CMFD-accelerated Monte Carlo method only in the definition of the nonlinear functionals. The new methods are compared with the CMFD-accelerated Monte Carlo method for two high-dominance-ratio test problems. All of the hybrid methods rapidly converge the Monte Carlo fission source, enabling a large reduction in the number of inactive cycles. However, the new methods stabilize the fission source more efficiently than the CMFD-accelerated Monte Carlo method, enabling a reduction in the number of active cycles as well. Also, in all the hybrid methods, the apparent variance of the eigenfunction is nearly equal to the real variance, so the real statistical error is well estimated from a single calculation. This is a major advantage over the standard Monte Carlo method, in which the real variance is typically underestimated due to intercycle correlations.