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DOE launches UPRISE to boost nuclear capacity
The Department of Energy’s Office of Nuclear Energy has launched a new initiative to meet the government’s goal of increasing U.S. nuclear energy capacity by boosting the power output of existing nuclear reactors through uprates and restarts and by completing stalled reactor projects.
UPRISE, the Utility Power Reactor Incremental Scaling Effort, managed by Idaho National Laboratory, is to “deliver immediate results that will accelerate nuclear power growth and foster innovation to address the nation’s urgent energy needs,” DOE-NE said in its announcement.
Jeffery D. Densmore, Thomas M. Evans, Michael W. Buksas
Nuclear Science and Engineering | Volume 159 | Number 1 | May 2008 | Pages 1-22
Technical Paper | doi.org/10.13182/NSE159-01
Articles are hosted by Taylor and Francis Online.
Discrete Diffusion Monte Carlo (DDMC) is a technique for increasing the efficiency of Monte Carlo simulations in diffusive media. If standard Monte Carlo is employed in such a regime, particle histories will consist of many small steps, a situation that results in a computationally inefficient calculation. In DDMC, particles take discrete steps between spatial cells according to a discretized diffusion equation. Each discrete step replaces many smaller Monte Carlo steps, thus increasing the efficiency of the simulation. In addition, because DDMC is based on the diffusion approximation, it should yield accurate solutions if used judiciously. In this paper, we present a new DDMC method for linear, steady-state radiation transport on adaptive-refinement meshes in two-dimensional Cartesian geometry. Adaptive-refinement meshes are characterized by local refinement such that a spatial cell may have multiple neighboring cells across each face. We specifically examine the cases of (a) a regular mesh structure without refinement, (b) a refined mesh structure where neighboring cells differ in refinement, and (c) a boundary mesh structure representing the interface between a diffusive region (where DDMC is used) and a nondiffusive region (where standard Monte Carlo is employed). With numerical examples, we demonstrate that our new DDMC technique is accurate and can provide efficiency gains of two orders of magnitude over standard Monte Carlo.