Predictive numerical modelling and analysis of the formation of arc attachment modes in high-current vacuum arcs – VArcA

To enhance the application of vacuum arc-based technology, a comprehensive understanding of the physical processes governing arc plasma behaviour and its interactions with surrounding materials—such as electrodes and walls—is essential. Experimental findings have illustrated various attachment modes on both the anode and cathode, influenced by prevailing physical processes; however, crucial aspects of mode formation remain poorly understood. Additionally, there is a pressing need for methods to control mode appearance and transitions to optimize operations across diverse applications. Key knowledge gaps include: (1) the role of excited species near electrodes and in arc fringes; (2) dynamics between convection and diffusion of plasma species; (3) the influence of cathode and anode activity on mode formation; (4) the impact of electrode movement, variations in input power, and external magnetic fields; and (5) the accuracy of existing vacuum arc models in predicting these attachment modes. This proposed project aims to address these gaps by investigating the high-current vacuum arc constriction process and anode attachment mode transitions with specific objectives: (i) clarifying the relationship between attachment modes and species transport processes using a self-consistent DC vacuum arc model; (ii) examining the role of excited species and chemical reactions through a collisional-radiative model; (iii) exploring energy, momentum, and mass transfer processes in electrode regions during arc column constriction via parametric studies; (iv) analysing the influence of electrode motion and AC power changes using an extended transient numerical model; and (v) validating the predictive performance of the vacuum arc model through detailed experimental studies. The insights gained significantly advance the understanding of the fundamental mechanisms underpinning vacuum arc operations.

Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 548964199, https://gepris.dfg.de/gepris/projekt/548964199.