At the University of Thessaly the ARIADNE code for modeling complex gas distribution systems operating under any vacuum conditions has been developed by integrating a kinetic database to a typical gas network solver. The ARIADNE code has been successfully implemented to model the ITER primary pumping system providing the torus effective pumping speed, as well as the pressure evolution during the dwell phase. However, the computed results are subject to the input data, which include the pipe network geometry, approximating the real geometry of the ITER primary pumping system and the operating data, such as the torus pressure, gas temperature and cryopump pumping speed. The effect of the aforementioned input quantity uncertainties to the torus effective pumping speed is investigated via an uncertainty propagation analysis by coupling the Monte Carlo method (MCM) with the ARIADNE code. Documenting the propagation of each input parameter uncertainty to the torus effective pumping speed uncertainty is beneficial for judging the accuracy of the modeling and simulation re-sults, as well as for identifying the most important sources of uncertainty. Furthermore, the presented meth-odology can be used to investigate the uncertainty propagation of any input quantity to any output quantity for vacuum systems of arbitrary complexity.
