Theory and generation methods for N-ary stochastic mixtures with Markovian mixing statistics
Abstract not provided.
Publications
Amnesia radius versions of Conditional Point Sampling for radiation transport in 1D stochastic media
Abstract not provided.
Using Deep Neural Networks to Predict Material Types in Conditional Point Sampling Applied to Markovian Mixture Models
Abstract not provided.
Amnesia radius versions of Conditional Point Sampling for radiation transport in 1D stochastic media
Abstract not provided.
Benchmark comparisons of Monte Carlo algorithms for one-dimensional N-ary stochastic media
Abstract not provided.
Recent Memory Versions of Conditional Point Sampling for Radiation Transport in 1D Stochastic Media
Abstract not provided.
Recent Memory Versions of Conditional Point Sampling for Transport in 1D Stochastic Media
Abstract not provided.
Benchmark Comparisons of Monte Carlo Algorithmsfor One-Dimensional N-ary Stochastic Media
Abstract not provided.
Amnesia Radius Versions of Conditional Point Sampling for Radiation Transport in 1D Stochastic Media
Abstract not provided.
Recent Memory Versions of Conditional Point Sampling for Transport in 1D Stochastic Media
Abstract not provided.
Amnesia Radius Versions of Conditional Point Sampling for Radiation Transport in 1D Stochastic Media
Abstract not provided.
Recharacterization of Saturn's radiation dose environment through simulation and experiment
Abstract not provided.
An Extension of Conditional Point Sampling to Quantify Uncertainty Due to Material Mixing Randomness
Abstract not provided.
An extension of Conditional Point Sampling to multi-dimensional transport
Abstract not provided.
Exploration of Biased Woodcock Tracking in C++ for Use in Conditional Point Sampling and the CHEETAH-MC Monte Carlo Code
Abstract not provided.
Use of Woodcock Tracking to Enable Deferred Material Definition for Transport in Stochastic Media
Abstract not provided.
Use of Woodcock Tracking to Enable Deferred Material Definition for Transport in Stochastic Media
Abstract not provided.
An extension of conditional point sampling to quantify uncertainty due to material mixing randomness
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019
Radiation transport in stochastic media is a problem found in a multitude of applications, and the need for tools that are capable of thoroughly modeling this type of problem remains. A collection of approximate methods have been developed to produce accurate mean results, but the demand for methods that are capable of quantifying the spread of results caused by the randomness of material mixing remains. In this work, the new stochastic media transport algorithm Conditional Point Sampling is expanded using Embedded Variance Deconvolution such that it can compute the variance caused by material mixing. The accuracy of this approach is assessed for 1D, binary, Markovian-mixed media by comparing results to published benchmark values, and the behavior of the method is numerically studied as a function of user parameters. We demonstrate that this extension of Conditional Point Sampling is able to compute the variance caused by material mixing with accuracy dependent on the accuracy of the conditional probability function used.
Conditional point sampling: A novel monte carlo method for radiation transport in stochastic media
Transactions of the American Nuclear Society
Abstract not provided.
An extension of conditional point sampling to multi-dimensional transport
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019
Radiation transport in stochastic media is a challenging problem type relevant for applications such as meteorological modeling, heterogeneous radiation shields, BWR coolant, and pebble-bed reactor fuel. A commonly cited challenge for methods performing transport in stochastic media is to simultaneously be accurate and efficient. Conditional Point Sampling (CoPS), a new method for transport in stochastic media, was recently shown to have accuracy comparable to the most accurate approximate methods for a common 1D benchmark set. In this paper, we use a pseudo-interface-based approach to extend CoPS to application in multi-D for Markovian-mixed media, compare its accuracy with published results for other approximate methods, and examine its accuracy and efficiency as a function of user options. CoPS is found to be the most accurate of the compared methods on the examined benchmark suite for transmittance and comparable in accuracy with the most accurate methods for reflectance and internal flux. Numerical studies examine accuracy and efficiency as a function of user parameters providing insight for effective parameter selection and further method development. Since the authors did not implement any of the other approximate methods, there is not yet a valid comparison for efficiency with the other methods.
20 Results