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Three-dimensional Optimal Flux Map Generation through Minimum Variance Technique

Three-dimensional Optimal Flux Map Generation through Minimum Variance Technique
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One of the key parameters that determine the power rating of any nuclear power plant and ultimately impact its safety is the power peaking factor for the core. The power peaking factor or the hot channel factor in turn depends on the flux and power distributions. This thesis research develops an advanced method that can more accurately determine flux and power distributions by combining the measured detector signals and properly predicted power distribution with errors in all available information. The Kalman filter is a minimum variance estimation algorithm that efficiently combines uncertain measurements representing diverse detector signals and nominal system predictions to yield an optimal estimate of the system variable, i.e., 3-D distributions for reaction rate, neutron flux, and eventually power density throughout the core. In various applications associated with reactor physics, it was verified that the Kalman filtering provides accurate interpolations and extrapolations in space as well as representing time evolutions in dynamical systems. Taking advantage of the properties of the Kalman filtering our study yields improved estimates of the actual flux and power distributions and hence more accurate estimate of the overall hot channel factor with all the uncertainties of the measurement and numerical calculation duly accounted for. In this dissertation we generate 3-D optimal power map for Cycle 18, Ulchin of the Unit 1 of PWR plant through the minimum variance technique and using actual measured data. In addition, to show that the MVE technique can reduce the unnecessary conservatism introduced in the power output determination, we perform an upper bound estimation for the 3-D optimal power distribution and compare the optimally estimated upper bound peaking factor with the conventionally estimated upper bound peaking factor.
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