Use of Neural Network for the Simulation of a Gas Centrifuge

Abstract

The prediction by a mathematical model of the separation of uranium isotopes using a gas centrifuge process is a hard task. The gas motion can be described by analytical or numerical solutions of the system of equations defined by the equation of continuity, the Navier-Stokes equation and the equation of energy. However, these calculations cannot be performed for actual centrifuges.Neural networks are an alternative for modelling complex problems that show too many difficulties to be solved by phenomenological models.The authors propose the use of neural networks for the simulation and prevision of the separative and operational parameters of a gas centrifuge separating uranium isotopes. The results from the uranium separation experiments (Zippe data) are compiled and presented to the neural network in the learning and testing processes. The prediction using the neural network model shows good agreement with the experimental data.