Arc channels tend to shrink mainly due to the fact that the plasma conductivity naturally increases with its temperature. In this letter, we report a method of generating a large area homogeneous arc plasma at atmospheric pressure. The plasma generator consists of an 80mm diameter graphite anode chamber and an 18mm diameter concentric graphite cathode. A solenoid coil is used to produce a magnetic field along the electrode axis. In the chamber, the arc plasma is observed in various configurations and temporally and spatially evolves from a contractive column to diffuse plasma cloud which fills the entire chamber cross section.

This paper focuses on the numerical research of the influence of electrode erosion on the arc behavior during opening process of low-voltage circuit breakers. The mathematical model of three-dimensional air arc plasma considering electrode erosion is built based on magnetic hydrodynamics. The mass fraction equation of copper vapor is introduced to the model on the basis of traditional mass, momentum, and energy balance equations. The influence of copper vapor on the thermodynamic and transport properties of the gas mixture is considered in this paper. The distributions of temperature field, gas flow field, and mass fraction of copper vapor in the arc chamber are simulated. The arc root displacements and arc voltage, which takes account of the influence of electrode erosion, are calculated. The simulation results indicate that the immobility time of both moving contact and stationary contact is much longer considering electrodes erosion. The calculated voltage of the arc column during arc motion considering erosion is smaller because of the change in the electrical conductivity of air-copper vapor mixtures. Except for the numerical investigation on the arc motion considering electrode erosion, the experiment work is also carried out to support the simulation work.

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Memristor, as the future of artificial intelligence, has been widely used in pattern recognition or signal processing from sensor arrays. Memristor-based recurrent neural network (MRNN) is an ideal model to mimic the functionalities of the human brain due to the physical properties of memristor. In this paper, the periodicity for memristor-based Cohen–Grossberg neural networks (MCGNNs) is studied. The neural network (NN) considered in this paper is based on the memristor and involves time-varying delays, distributed delays and impulsive effects. The boundedness and monotonicity of the activation function are not assumed. By some inequality technique and contraction mapping principle, we prove the existence, uniqueness and exponential stability of periodic solution for MCGNNs. Finally, some numeral examples and comparisons are provided to illustrate the validation of our results.