Abstract
This dissertation introduces a novel approach to nuclear reactor design, emphasizing molten salt reactors (MSRs) to address the global need for safer, more efficient, and sustainable energy sources. It confronts the inherent challenges of nuclear fission reactors--proliferation risks, cost efficiency, waste management, and passive accident prevention--by developing an innovative MSR model and a machine learning (ML)-based optimization framework. The molten salt microreactor (MSMR), devised by researchers at Brigham Young University, utilizes thermally conductive plates for efficient heat dissipation during operational anomalies, ensuring safety within operational limits. This design was rigorously tested using STARCCM+ for fluid dynamics modeling and RELAP5-3D for verification, demonstrating the MSMR's capability for safe operation under steady-state and transient conditions. This dissertation also presents a novel ML-based optimization framework. Given the complexity of nuclear reactor design, enhancing the efficiency of this process has profound implications for the reactor design community and global energy strategies. Utilizing computational fluid dynamics (CFD) software STARCCM+ and Open Monte Carlo (OpenMC) for neutronics, the study generated a dataset from minimal simulation runs, addressing data scarcity through an innovative approach. The optimization explored variables such as moderator and salt thickness, and moderator housing material, alongside ML model parameters, to identify an optimal reactor geometry that significantly surpasses existing designs in efficiency and safety. Furthermore, the research extends this framework to reactor shielding optimization, traditionally a resource-intensive task. By applying ML models with the Gekko Optimization Suite, the study achieves significant improvements in shield design with minimal data, meeting safety standards while reducing mass, cost, and simplifying the design process. In summary, this dissertation advances Generation IV (Gen. IV) reactor technology by merging a unique MSR design with a comprehensive ML optimization framework. This breakthrough not only contributes to the solutions to key nuclear power challenges but also sets a new standard for the design of safer, more efficient, and economically viable nuclear reactors, paving the way for the licensing of the first domestic Gen. IV reactor.
Degree
PhD
College and Department
Ira A. Fulton College of Engineering; Chemical Engineering
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Larsen, Andrew William, "Development of a Molten Salt Reactor Design and Design Framework for Overall Reactor Performance Prediction and Optimization" (2024). Theses and Dissertations. 10828.
https://scholarsarchive.byu.edu/etd/10828
Date Submitted
2024-04-22
Document Type
Dissertation
Handle
http://hdl.lib.byu.edu/1877/etd13632
Keywords
molten salt reactor, passive safety, computational fluid dynamics, machine learning, optimization, thermal hydraulics, neutronics, shielding, remote power, small modular reactor
Language
english