Updating KN1D into Python for Improved Modeling of Neutral Profiles

In fusion research, predicting neutral profiles is critical to understand the fueling requirements for a fusion reactor [1]. KN1D [2], written in IDL, includes collisional-radiative models for atoms and molecules, and accounts for the 10 most important electron interactions; elastic collisions are included using a BKG model. However, atomic physics cross-sections have remained unchanged since the time of initial coding. We will first convert KN1D into Python to simplify its use for the wider community and improve compatibility with experimental data as well as predictive modeling.

The updated code will be verified through comparison to the previous IDL version using data from the Alcator C-Mod tokamak at MIT. Additionally, we will use the newly developed KN1DPy to compare both Lyman-alpha data from C-Mod discharges as well as to compare to SOLPS-ITER modeling. This testing against experimental results and more complex modeling will allow us to verify the continued applicability of KN1D’s predictions, and thus assess the need to upgrade the atomic physics processes in KN1D to leverage open-source databases of atomic physics reactions such as ADAS.

[1] S. Mordijck, Nucl. Fusion 60, 082006 (2020).

[2] LaBombard B., "KN1D: A 1-D Space, 2-D Velocity, Kinetic transport algorithm for atomic and molecular hydrogen in an ionizing plasma", MIT Plasma Science and Fusion Center Report PSFC/RR-01-3; Research Report PSFC/RR-01-3