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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Nano to begin drilling next week in Illinois
It’s been a good month for Nano Nuclear in the state of Illinois. On October 7, the Office of Governor J.B. Pritzker announced that the company would be awarded $6.8 million from the Reimagining Energy and Vehicles in Illinois Act to help fund the development of its new regional research and development facility in the Chicago suburb of Oak Brook.
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.