(Presented at the 10th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, Operation and Safety NUTHOS-10, Okinawa, Japan, 2014)
Simulation of reflood phase during loss-of-coolant accident in a nuclear reactor power plant is an important part in the plant safety analysis. During reflood, a steady stream of water is injected into a dry fuel channel to cool down the heated channel and to prevent fuel failure. Computer simulation of such phenomena relied on a set of phenomenological models that contains uncertain parameters. Such uncertainties will result in variation of the prediction made by simulation. The quantity of interest of the simulation comes in a form of time-dependent fuel temperature prediction. The presence of simultaneous amplitude and phase variations in the time-dependent dataset prevent straightforward statistical summary of such data. In this presentation, I'll describe the application of functional data analysis to the problem of characterizing the variation of the high-dimensional, highly correlated computer code output stemming from parameter uncertainty.