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The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Sam Pasmann, Ilham Variansyah, C. T. Kelley, Ryan McClarren
Nuclear Science and Engineering | Volume 197 | Number 6 | June 2023 | Pages 1159-1173
Technical Paper | doi.org/10.1080/00295639.2022.2143704
Articles are hosted by Taylor and Francis Online.
In this work we investigate replacing standard quadrature techniques used in deterministic linear solvers with a fixed-seed Quasi–Monte Carlo (QMC) calculation to obtain more accurate and efficient solutions to the neutron transport equation (NTE). QMC is the use of low-discrepancy sequences to sample the phase-space in place of pseudorandom number generators used by traditional Monte Carlo (MC). QMC techniques decrease the variance in the stochastic transport sweep and therefore increase the accuracy of the iterative method. Historically, QMC has largely been ignored by the particle transport community because it breaks the Markovian assumption needed to model scattering in analog MC particle simulations. However, by using iterative methods the NTE can be modeled as a pure-absorption problem. This removes the need to explicitly model particle scattering and provides an application well suited for QMC. To obtain solutions we experimented with three separate iterative solvers: the standard Source Iteration (SI) Solver and two linear Krylov Solvers, i.e., the Generalized Minimal RESidual method (GMRES) and the BiConjugate Gradient STABilized method (BiCGSTAB). The resulting hybrid iterative-QMC solver was assessed on three slab geometry problems of one dimension. In each sample problem the Krylov Solvers achieve convergence with far fewer iterations (up to eight times) than the SI Solver. Regardless of the linear solver used, the hybrid method achieved an approximate convergence rate of as compared to the expected of traditional MC simulation across all test problems.