Accuracy and convergence of iteratively solved Monte Carlo codes for simulations in the plasma edge of nuclear fusion reactors

Iteratively solved Monte Carlo (MC) codes are frequently used for plasma edge simulations. However, their accuracy and convergence assessment are still unresolved issues. In analogy with the error classification recently developed for coupled finite-volume/Monte Carlo (FV-MC) codes, we define different error contributions and analyse them separately in a simplified non-linear MC code. Three iterative procedures are examined: Random Noise (RN), where different seeds are used in each iteration; Correlated Sampling, where particle trajectories remain correlated between iterations; and Robbins Monro, where averaging is used during the simulation. We show that, as in FV-MC codes, RN is the most efficient iterative procedure provided averaging is used to decrease the statistical error. In addition, we conclude that the accuracy can be assessed using the same techniques as in FV-MC codes.