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Nonlinear model-based condition monitoring of advanced gas-cooled nuclear reactor cores

Yang, Erfu and Grimble, Michael and Inzerillo, Santo and Katebi, M (2011) Nonlinear model-based condition monitoring of advanced gas-cooled nuclear reactor cores. In: Proceedings of the Institute of Measurement and Control. UNSPECIFIED.

Text (Yang-etal-PIMC-2014-Nonlinear-model-based-condition-monitoring-of-advanced-gas-cooled-nuclear)

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    The graphite core is one critical component in gascooled nuclear reactors and it ages and degrades over time. As a result, the graphite core can dictate the life-time of a reactor in a nuclear power station. To support the safety cases and ensure the continued safe operation of an advanced gas-cooled reactor (AGR) nuclear plant, it is important to closely monitor the condition of its reactor graphite core to maintain the integrity throughout the life of the reactor. Toward this end, the fuel grab load trace (FGLT) measurements are currently used as main information sources to infer the core condition. Due to the fact that the FGLTs are masked by many effects in the refuelling process, the first principles models for nuclear refuelling process are promising to separate the information of interests to core condition from the masked FGLT measurements. To reliably and accurately obtain the unknown parameters existing in the developed first principles model for model-based condition monitoring of AGR nuclear graphite cores, this paper presents a nonlinear system identification approach. In this approach, a nonlinear first principles model is first developed to describe the refuelling process. A friction model is then investigated to mathematically deal with the frictional effects. The aerodynamic force is also modelled separately. Finally, the Trust- Region Reflective Newton method is used to find the optimal parameters in the nonlinear refuelling model. The real-world data from an AGR nuclear power plant is employed to demonstrate the effectiveness of the proposed nonlinear system identification approach for nonlinear model-based condition monitoring of graphite cores