Using genetic algorithms for calibrating simplified models of nuclear reactor dynamics

In this paper the use of genetic algorithms for the estimation of the effective parameters of a model of nuclear reactor dynamics is investigated. The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest (e.g., reactor power, average fuel and coolant temperatures) to the actual evolution profiles, here simulated by the Quandry based reactor kinetics (Quark) code available from the Nuclear Energy Agency. Alternative schemes of single- and multi-objective optimization are investigated. The efficiency of convergence of the algorithm with respect to the different effective parameters to be calibrated is studied with reference to the physical relationships involved.