A Monte Carlo method for the model-based estimation of nuclear reactor dynamics

The safe operation and control of a nuclear system requires the accurate estimation of its dynamic state in real time. This can be pursued starting from a model of the system dynamics and on related measurements, which are typically affected by noise. In practice, the nonlinearity of the model and non-Gaussianity of the noise are such that classical approximate approaches, e.g. the extended-Kalman, Gaussian-sum and grid-based filters, often lead to inaccurate results and/or are too computationally expensive for real-time applications. On the contrary, Monte Carlo estimation methods, also called particle filters, can be very effective. The present paper investigates the use of a Monte Carlo method, called sampling importance resampling (SIR), for the estimation of the nonlinear dynamics of a nuclear reactor, as described by a simplified model of literature