Small Modular Nuclear Reactors o er a new alternative to carbon based energy sources in the energy market of the near future. Understanding... Show moreSmall Modular Nuclear Reactors o er a new alternative to carbon based energy sources in the energy market of the near future. Understanding the economic forces driving the industrial manufacturing process is crucial to determining the viability of SMRs. This study is a continuation of research that developed a parametric model and initial production cost estimates for a generic 100MWe SMR integrated reactor vessel. The primary goal of this study was to characterize the learning rates, lot sizes, and optimum production of SMR IRVs using the parametric model and the initial cost estimates. Three separate models were developed based on increasing levels of learning transfer: no learning transfer, partial learning transfer, and full learning transfer. Models with no learning transfer and full learning transfer bounded the values for the learning curve expected for IRV manufacture. A model with a partial transfer of learning yielded production cost estimates of $312.2 million. Production of an SMR IRV based on this model is expected to see a learning rate 95.5%. Using the information from the other two models, the expected learning rate for IRV production is expected to fall between 93.3% and 99.1% Simulations of lot sizes of 1 to 12 were conducted to determine the manufacturing lot size that optimizes the factory setting. An optimum con guration of 5 units per lot was determined to be the minimum. However, the lot size is recommended to be increased to 6 units to withstand the possibility of cancellation. In this con guration, the average unit cost is $262 million, with a learning rate of 98.1%. Another important result indicates that optimum manufacturing outcomes are not necessarily correlated with higher levels of learning. Production in larger lot sizes is bene cial, especially for components that are few in number, like the pressure vessel. M.S. in Physics, July 2013 Show less
