| Single-use, low-enriched uranium oxide fuel, consumed through several cycles in a light-water reactor (LWR) before being disposed, has become the dominant source of commercial-scale nuclear electric generation in the United States and throughout the world. However, it is not without its drawbacks and is not the only potential nuclear fuel cycle available. Numerous alternative fuel cycles have been proposed at various times which, through the use of different reactor and recycling technologies, offer to counteract many of the perceived shortcomings with regards to waste management, resource utilization, and proliferation resistance. However, due to the varying maturity levels of these technologies, the complicated material flow feedback interactions their use would require, and the large capital investments in the current technology, one should not deploy these advanced designs without first investigating the potential costs and benefits of so doing. As the interactions among these systems can be complicated, and the ways in which they may be deployed are many, the application of automated numerical optimization to the simulation of the fuel cycle could potentially be of great benefit to researchers and interested policy planners.;To investigate the potential of these methods, a computational program has been developed that applies a parallel, multi-objective simulated annealing algorithm to a computational optimization problem defined by a library of relevant objective functions applied to the Ver ifiable Fuel Cycle Simulati on Model (VISION, developed at the Idaho National Laboratory). The VISION model, when given a specified fuel cycle deployment scenario, computes the numbers and types of, and construction, operation, and utilization schedules for, the nuclear facilities required to meet a predetermined electric power demand function. Additionally, it calculates the location and composition of the nuclear fuels within the fuel cycle, from initial mining through to eventual disposal. By varying the specifications of the deployment scenario, the simulated annealing algorithm will seek to either minimize the value of a single objective function, or enumerate the trade-off surface between multiple competing objective functions. The available objective functions represent key stakeholder values, minimizing such important factors as high-level waste disposal burden, required uranium ore supply, relative proliferation potential, and economic cost and uncertainty. The optimization program itself is designed to be modular, allowing for continued expansion and exploration as research needs and curiosity indicate.;The utility and functionality of this optimization program are demonstrated through its application to one potential fuel cycle scenario of interest. In this scenario, an existing legacy LWR fleet is assumed at the year 2000. The electric power demand grows exponentially at a rate of 1.8% per year through the year 2100. Initially, new demand is met by the construction of 1-GW(e) LWRs. However, beginning in the year 2040, 600-MW(e) sodium-cooled, fast-spectrum reactors operating in a transuranic burning regime with full recycling of spent fuel become available to meet demand. By varying the fraction of new capacity allocated to each reactor type, the optimization program is able to explicitly show the relationships that exist between uranium utilization, long-term heat for geologic disposal, and cost-of-electricity objective functions. The trends associated with these trade-off surfaces tend to confirm many common expectations about the use of nuclear power, namely that while overall it is quite insensitive to variations in the cost of uranium ore, it is quite sensitive to changes in the capital costs of facilities. The optimization algorithm has shown itself to be robust and extensible, with possible extensions to many further fuel cycle optimization problems of interest. |
