2025/12/16 by Ayushi Sharma, Libin Varghese, Bhaskar Chaudhury · 1 citation
Engineering · Physics and Astronomy · Mathematics · #Plasma Diagnostics and Applications #Magnetic confinement fusion research #Gas Dynamics and Kinetic Theory
paper · doi:10.1088/1361-6463/ae2d68
Abstract The accurate simulation of low-temperature plasmas (LTP) using the particle-in-cell Monte Carlo collisions (PIC-MCC) method strongly depends on the fidelity of the input collision cross-section data. For many gases, multiple cross-section datasets are available from experimental, theoretical, and numerical sources resulting in significant variability and the lack of universally accepted benchmark data remains a major challenge. This work presents a systematic uncertainty quantification (UQ) study that propagates cross-section dataset variability through two-dimensional (2D)-3 V PIC-MCC simulations of LTPs in a generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi mathvariant="italic">E</mml:mi> </mml:mrow> <mml:mo>×</mml:mo> <mml:mrow> <mml:mi mathvariant="italic">B</mml:mi> </mml:mrow> </mml:mrow> </mml:math> configuration. Focusing on Argon and Hydrogen, we perform multiple simulations under identical conditions, systematically varying magnetic fields and utilizing several electron–neutral cross-section datasets from the LXCat platform. Plasma density, electron energy, and potential profiles obtained from the simulations are analyzed, with a bootstrap based UQ framework providing 95% confidence intervals. Results reveal that uncertainty in plasma properties is dependent on gas type, input parameters, magnetic field and spatial conditions, highlighting that dataset choice can impact plasma predictions from PIC-MCC simulations. Beyond establishing the first reproducible UQ framework for PIC based LTP simulations, this work underscores the need for standardized cross-section databases and highlights the importance of future efforts in validation studies, sensitivity analyses and broader operating regimes to advance robust UQ practices in plasma modeling.