Malfunction diagnosis and applications of stable adaptive schemes for a nuclear reactor system

Malfunction diagnosis concerns a method to detect the abnormal phenomena during nuclear reactor operations, while stable adaptive schemes does the application of Model Reference Adaptive System (MRAS) to the nonlinear dynamics of a reactor for parameter identification and control. The new method for the malfunction diagnosis consists of the following ideas; an index defined as the sum of ratios of the square of a factor score to the contribution weight of the factor, which is evaluated by applying the multi-factor analysis technique to the data of the state of nuclear reactor systems like neutron flux, temperature, flow rate and so on. The excess of the index over some given threshold shows the reactor system would be in an abnormal state. Then a theory of optimal filtering by Kalman with the aid of the stochastic approximation is applied to estimate the neutron flux distribution at its abnormal state and subsequently the squared sum of difference between desirable and estimated flux distributions shows the spot at which the abnormal phenomena would have occurred in terms of the peak of its distribution. Parameter identification and adaptive control schemes are presented for a point reactor and a loosely-coupled-core reactor with internal feedbacks which lead to the nonlinearity of the overall system. Both schemes are shown stable with new representations of the systems, which correspond to the nonminimal system representation, in the vein of the MRAS via the Lyapunov's method. For the sake of the parameter identification, model parameters can be adjusted adaptively as soon as measurements start, while plant parameters can also adaptively be compensated through control input to reduce the output error between the model and the plant for the case of the adaptive control. Some experiments of parameter identification for the thermal-hydraulic system are carried out successfully using a simplified channel in which flow rate is varied in a binary form. (J.P.N.)