Use of neural network for the simulation of a gas centrifuge

The prediction by a mathematical model of the separation of uranium isotope s using a gas centrifuge process is a hard task. The gas motion can be desc ribed by analytical or numerical solutions of the system of equations defin ed by the equation of continuity, the Navier-Stokes equation and the equati on of energy. However, these calculations cannot be performed for actual ce ntrifuges. Neural networks are an alternative for modelling complex problems that show too many difficulties to he solved by phenomenological models. The authors propose the use of neural networks for the simulation and previ sion of the separative and operational parameters of a gas centrifuge separ ating uranium isotopes. The results from the uranium separation experiments (Zippe data) are compiled and presented to the neural network in the learn ing and testing processes. The prediction using the neural network model sh ows good agreement with the experimental data.