Validation of a general-use high flux isotope reactor-specific metaheuristic optimization framework for isotope production target design.

Currently, advanced optimization methods are limited for isotope production (IP) campaigns at the US Department of Energy's High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL), leading to years of conservative and historical approaches with minimal innovation. Moreover, the growing demand for new and existing isotopes is beginning to challenge the capacity of HFIR. This work explores the development and integration of metaheuristic (MH) optimization techniques for more efficient target design and irradiation strategies.

Stochastic gradient descent for optimization for nuclear systems.

The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems.