Speaker
Description
We present KN1DPy, a Python translation of KN1D, a spatially 1D kinetic neutral transport code that was originally written in IDL to model gas fuelling on the Alcator C-Mod tokamak. Given input profiles of electron density, $n_e$, and temperature, $T_e$, in the scrape-off-layer and on closed flux surfaces, KN1D solves the Boltzmann Equation to obtain the distribution functions for atomic and molecular neutral hydrogen. While its 1D geometry represents a step down in fidelity compared to 2D codes like EIRENE and DEGAS-2, this simplification also vastly reduces the computation time. Each KN1D run takes only a few seconds, and as a result KN1D can easily be included within integrated transport models to give an estimate for the edge ionisation source from gas fuelling. Furthermore, because KN1D returns the full distribution function for the neutrals, it also yields detailed information about the effective neutral temperature, the fraction of impinging neutrals reflected by the plasma, and the fraction of neutrals that have undergone charge-exchange.
We show some examples from DIII-D and Alcator C-Mod, and include comparisons with results from 2D codes on these devices. We also demonstrate how KN1DPy can be used to provide a quick estimate for the neutral source in integrated transport models.