Masters Theses

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

Committee Member(s)

Alam, Syed B.
Thompson, Nicholas


Nuclear Engineering and Radiation Science

Degree Name

M.S. in Nuclear Engineering


This work was supported in part by the DOE Nuclear Criticality Safety Program, funded and managed by the National Nuclear Security Administration for the Department of Energy.


Missouri University of Science and Technology

Publication Date

Spring 2022


x, 85 pages

Note about bibliography

Includes bibliographic references (pages 82-84).


© 2022 Cole Michael Kostelac, All rights reserved.

Document Type

Thesis - Open Access

File Type




Thesis Number

T 12117