A parameterized learning method based on data-driven and physical constraints for solving the fluid model of low-temperature plasma

Abstract

Low-temperature plasma (LTP) plays an indispensable role in environmental remediation and energy conversion. Rapid prediction of state parameters in LTP is crucial for enhancing both pollutant degradation efficiency and fuel conversion performance. To address the high computational cost of traditional numerical methods for LTP modeling, this research proposes a coupled physics-driven and data-driven approach incorporating parameterized learning. This approach introduces applied voltage as an input parameter while parameterized learning is adopted for rapid prediction of electrostatic potential and charged particle densities in LTP. Meanwhile, optical sensing technology is employed to high-fidelity measurement of potential for model validation. The results demonstrate that the method effectively captures the distribution features of electrostatic potential and charged particles in LTP. The trained model can generate prediction results within 1 s, achieve an average relative L2 error (RL2E) of 8.72 × 10−3 and mean absolute error of 4.39 V. This study confirms the feasibility of physics-informed approaches in advancing LTP modeling, offering a pathway toward efficient approach for the online condition assessment and multi-parameter optimization design of plasma devices.