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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Latest News
U.K.’s NWS gets input from young people on geological disposal
Nuclear Waste Services, the radioactive waste management subsidiary of the United Kingdom’s Nuclear Decommissioning Authority, has reported on its inaugural year of the National Youth Forum on Geological Disposal forum. NWS set up the initiative, in partnership with the environmental consultancy firm ARUP and the not-for-profit organization The Young Foundation, to give young people the chance to share their views on the government’s plans to develop a geological disposal facility (GDF) for the safe, secure, and long-term disposal of radioactive waste.
Douglas E. Peplow
Nuclear Technology | Volume 174 | Number 2 | May 2011 | Pages 289-313
Technical Paper | Special Issue on the SCALE Nuclear Analysis Code System / Radiation Protection | doi.org/10.13182/NT174-289
Articles are hosted by Taylor and Francis Online.
Monte Carlo shielding analysis capabilities in SCALE 6 are centered on the Consistent Adjoint Driven Importance Sampling (CADIS) methodology. CADIS is used to create an importance map for space/energy weight windows as well as a biased source distribution. New to SCALE 6 are the Monaco functional module, a multigroup fixed-source Monte Carlo transport code, and the Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) sequence. MAVRIC uses the Denovo code (also new to SCALE 6) to compute coarse-mesh discrete ordinates solutions that are used by CADIS to form an importance map and biased source distribution for the Monaco Monte Carlo code. MAVRIC allows the user to optimize the Monaco calculation for a specific tally using the CADIS method with little extra input compared with a standard Monte Carlo calculation. When computing several tallies at once or a mesh tally over a large volume of space, an extension of the CADIS method called FW-CADIS can be used to help the Monte Carlo simulation spread particles over phase space to obtain more uniform relative uncertainties.
