Masters Theses

Date of Award

5-2022

Degree Type

Thesis

Degree Name

Master of Science

Major

Nuclear Engineering

Major Professor

Sandra Bogetic

Committee Members

Michael Howard, Samantha Hedrick

Abstract

Medical isotopes are used for a variety of different diagnostic and therapeutic purposes Ruth (2008). Due to recent newly discovered applications, their production has become rapidly more scarce than ever before Charlton (2019). Therefore, more efficient and less time consuming methods are of interest for not only the industry’s demand, but for the individuals who require radio-isotope procedures. Currently, the primary source of most medical isotopes used today are provided by reactor and cyclotron irradiation techniques, followed by supplemental radio-chemical separations Ruth (2008). Up until this point, target designs have been optimized by experience, back of the envelope calculations, and parametric studies Bevins (2017). Isotope production is, for many reasons, difficult and expensive, thus, more accurate isotopespecific data is required to build computational models to guide improvements on these processes. However, acquiring this data at large-scale production facilities is time-consuming and impractical Bogetic et al. (2018). The implementation of various optimization algorithms in the field of nuclear engineering and medical physics can be related to the increase in quality and safety of reactor cores, shielding, radiology, radiotherapy, and etc. Ghaheri et al. (2015); Charlton (2019). Thus, continuing these efforts to advanced medical isotope production techniques is inevitable. The work that follows proposes how the use of a meta-heuristic based optimization framework, with target design parameter variables, constraints, and objectives could lead to a more efficient production method. To accomplish this, we introduce Gnowee Bevins (2017), a meta-heuristic software package that has been developed at the University of California, Berkeley Bogetic (2020). This multi variable/multi constraint optimization algorithm will provide a coherent and coordinated approach to tackling the difficult task of designing an optimal target for isotope production, enabling the capabilities to optimize the target using high fidelity neutronic simulations, with imposed constraints related to radio-chemical separations Hogle (2012). Therefore, this goal could potentially be accomplished by supplying Gnowee with the necessary variable options to choose materials and their respective thicknesses that (1) accurately degrade parts of the spectrum that are undesirable, (2) cause the least amount of reactions that require intensive separation techniques, and (3) maximize the production of the desired medical isotope.

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