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The blossoming of cooperation between the U.S. and Canada
The United States and Canadian nuclear industries used to be an example of how two independent teams of engineers facing an identical problem—making electricity from uranium—could come up with completely different answers. In the 1950s, Canada began designing a reactor with tubes, heavy water, and natural uranium, while in the U.S. it was big pots of light water and enriched uranium.
But 80 years later, there is a remarkable convergence. The North American push for a new generation of nuclear reactors, mostly small modular reactors (SMRs), is becoming binational, with U.S. and Canadian companies seeking markets and regulatory certification on both sides of the border and in many cases sourcing key components in the other country.
S. C. Wilson, R. N. Slaybaugh
Nuclear Science and Engineering | Volume 179 | Number 1 | January 2015 | Pages 22-41
Technical Paper | doi.org/10.13182/NSE13-109
Articles are hosted by Taylor and Francis Online.
Continued demand for accurate and computationally efficient transport methods to solve optically thick, fixed-source transport problems has inspired research on variance-reduction (VR) techniques for Monte Carlo (MC). Methods that use deterministic results to create VR maps for MC constitute a dominant branch of this research, with Forward Weighted–Consistent Adjoint Driven Importance Sampling (FW-CADIS) being a particularly successful example. However, locations in which energy and spatial self-shielding are combined, such as thin plates embedded in concrete, challenge FW-CADIS. In these cases the deterministic flux cannot appropriately capture transport behavior, and the associated VR parameters result in high variance in and following the plate. This work presents a new method that improves performance in transport calculations that contain regions of combined space and energy self-shielding without significant impact on the solution quality in other parts of the problem. This method is based on FW-CADIS and applies a Resonance Factor correction to the adjoint source. The impact of the Resonance Factor method is investigated in this work through an example problem. It is clear that this new method dramatically improves performance in terms of lowering the maximum 95% confidence interval relative error and reducing the compute time. Based on this work, we recommend that the Resonance Factor method be used when the accuracy of the solution in the presence of combined space and energy self-shielding is important.