Uncertainty and sensitivity analysis of a materials test reactor
Modukanele, Mogomotsi Ignatius
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This study was based on the uncertainty and sensitivity analysis of a generic 10 MW Materials Test Reactor (MTR). In this study an uncertainty and sensitivity analysis methodology called code scaling applicability and uncertainty (CSAU) was implemented. Although this methodology follows 14 steps, only the following were carried out: scenario specification, nuclear power plant (NPP) selection, phenomena identification and ranking table (PIRT), selection of frozen code, provision of code documentation, determination of code applicability, determination of code and experiment accuracy, NPP sensitivity analysis calculations, combination of biases and uncertainties, and total uncertainty to calculate specific scenario in a specific NPP. The thermal hydraulic code Flownex®1 was used to model only the reactor core to investigate the effects of the input parameters on the selected output parameters of the hot channel in the core. These output parameters were mass flow rate, temperature of the coolant, outlet pressure, centreline temperature of the fuel and surface temperature of the cladding. The PIRT process was used in conjunction with the sensitivity analysis results in order to select the relevant input parameters that significantly influenced the selected output parameters. The input parameters that have the largest effect on the selected output parameters were found to be the coolant flow channel width between the plates in the hot channel, the width of the fuel plates itself in the hot channel, the heat generation in the fuel plate of the hot channel, the global mass flow rate, the global coolant inlet temperature, the coolant flow channel width between the plates in the cold channel, and the width of the fuel plates in the cold channel. The uncertainty of input parameters was then propagated in Flownex using the Monte Carlo based uncertainty analysis function. From these results, the corresponding probability density function (PDF) of each selected output parameter was constructed. These functions were found to follow a normal distribution.
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