Multi-Objective Optimization of Uranium Target Assembly–3: A Comparison of Genetic and Traditional Methods

Journal Article · Fri Sep 26 00:00:00 EDT 2025 · Nuclear Science and Engineering

DOI:https://doi.org/10.1080/00295639.2025.2540659· OSTI ID:3002425

Nelson, Noel B. ^[1]; Conant, Andrew J. ^[1]; Navarro, Jorge ^[1]; Wahlen, Robert N. ^[2]; Denbrock, Chad P. ^[2]; Grimm, Terry L. ^[2]

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1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
2. Niowave Inc., Lansing, MI (United States)

Commonly produced as a byproduct of uranium fission, ⁹⁹Mo is a key medical isotope that is in high demand in the United States. An international goal is to switch from medical isotope production technologies that require highly enriched uranium to medical isotope production technologies that require only low-enriched uranium. Niowave Inc. is contributing to this goal by developing an accelerator-driven subcritical assembly called the Uranium Target Assembly (UTA). This work compares the performance of Dakota’s Multi-Objective Genetic Algorithm (MOGA) against traditional sensitivity analysis in the neutronic optimization of the UTA-3 system. The design objectives are k-eigenvalue (k_eff) and natural uranium fission power, which are directly correlated with the amount of ⁹⁹Mo produced. Dakota:MOGA did not perform as well as human engineering ingenuity in optimization studies with high numbers of input parameters, such as fuel rod type selection and fuel rod placement. However, Dakota:MOGA did outperform traditional sensitivity analysis in optimization studies with fewer than 20 parameters and revealed the degree to which each parameter influences the optimal design space for k_eff and natural uranium fission power (to a lesser extent). As the design model became more complex in the final stage of design, the computational resources required to calculate the design objective values in the Monte Carlo N-Particle transport code from selected input parameter combinations limited Dakota:MOGA’s performance, and, unfortunately, human intervention was required to discern the optimal design space. In conclusion, future work will attempt to reduce computational resource constraints by incorporating areduced-order neutronics model into the optimization cycle.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 3002425
• Journal Information: Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering; ISSN 0029-5639; ISSN 1943-748X
• Publisher: Taylor & Francis
• Country of Publication: United States
• Language: English

Related Subjects

Accelerator-driven systems
genetic algorithm
optimization
photonuclear
subcritical assembly