BDV prediction in cryogenic insulation using geometric regression modeling

Accurate prediction of breakdown voltage (BDV) in cryogenic environments is critical for ensuring the operational safety and reliability of high-temperature superconducting (HTS) systems. In such systems, dielectric failure due to extreme thermal and electrical stresses can lead to catastrophic malfunction. This study presents a second-order polynomial regression model that quantitatively predicts BDV in liquid nitrogen (LN₂) as a function of electrode gap and diameter. The model was developed based on experimentally measured data and incorporates nonlinear and interaction effects between geometric variables. Statistical validation confirmed its high predictive accuracy (R² = 0.9917), demonstrating robustness. This modeling approach enables pre-operational insulation design optimization and may be embedded into digital twin frameworks for real-time diagnostics. The findings offer both theoretical insights and practical tools for the development and deployment of next-generation cryogenic insulation systems in HTS applications.