Keywords and Phrases
Criticality; Nuclear; Optimization; PSO; Nuclear physics and radiation
“Critical experiments are used by nuclear data evaluators and criticality safety engineers to validate nuclear data and computational methods. Many of these experiments are designed to maximize the sensitivity to a certain nuclide-reaction pair in an energy range of interest. Traditionally, a parameter sweep is conducted over a set of experimental variables to find a configuration that is critical and maximally sensitive. As additional variables are added, the total number of configurations increases exponentially and quickly becomes prohibitively computationally expensive to calculate, especially using Monte Carlo methods.
This work presents the development of a particle swarm optimization algorithm to design these experiments in a more computationally efficient manner. The algorithm is then demonstrated by performing a two-dimensional and three-dimensional optimization of a uranium-molybdenum and plutonium-molybdenum critical experiment, respectively.
The two-dimensional and three-dimensional optimizations on average performed 35x and 3277x faster than the parameter sweep method, respectively. This corresponds to a 5.6 day and 2,314 day reduction in computation time”--Abstract, page iii.
Alajo, Ayodeji Babatunde
Alam, Syed B.
Nuclear Engineering and Radiation Science
M.S. in Nuclear Engineering
Missouri University of Science and Technology
x, 85 pages
© 2022 Cole Michael Kostelac, All rights reserved.
Thesis - Open Access
Kostelac, Cole Michael, "Particle swarm optimization for critical experiment design" (2022). Masters Theses. 8088.