Modelling the Role of the Inner Electrode Radius on the Oxygen Negative Corona Discharge in Coaxial Electrode Geometry

The charge species plays a vital role in changing the field in direct current discharge (DC). This article introduces a numerical modeling in one dimension of the inner electrode diameter of oxygen-fed negative corona discharge in coaxial electrodes geometry. The properties of negative corona plasma in a concentric cylindrical electrodes (wire-cylinder) were simulated by COMSOL Multiphysics software. Various diameters of negative corona electrode, namely 0.01, 0.025, 0.05, 0.075, and 0.125 mm, were applied, ​​where the diameter of the outer cylindrical electrode was taken as 15 mm. The model was run at atmospheric pressure and the applied negative voltage was -10 KV. Moreover, oxygen gas was used to fill the inter-electrodes distance. Furthermore, the spatial distribution of electrons, positive ions, and negative ions as a function of the diameter of negative electrode of the negative corona discharge were investigated. The study also tested the effects of the electrode diameter of the negative corona discharge on ozone generation. The observed decrease in ozone density with the increase in negative electrode diameter was reasonable and consistent with other results provided by researchers in this field.

The Role of Nonlinear Body Force in Electrostatic Propeller Driven by Atmospheric DC Corona Discharges

In this study, the body force generated by atmospheric positive and negative corona discharges were investigated using a wire-cylinder configuration experimentally and numerically. We provided new insight into the atmospheric electric thruster by introducing a nonlinear term in body force constituent the thrust of the system. It was observed that the direction of both body forces and electric winds is always from the wire to the cylinder irrespective of the applied voltage polarity. It was illustrated that the corresponding thrusts and the electric wind of the positive corona are larger than that of the negative corona discharge. We took into account the nonlinear mechanisms to explain the difference in thrust forces in positive and negative corona discharges. To elucidate the origin of the body force in corona discharges, we performed 2-D simulations via COMSOL Multiphysics and MATLAB software. The results of the numerical simulation showed that in addition to the linear body force (Coulomb force) a strong nonlinear body force is generated around the wire electrode that plays a crucial role in corona thrusters. To verify the direction and magnitude of the thrust, a simple theory was proposed based on variable mass systems and confirmed by published experimental works.

Influence of Magnetic Field on Characteristics of Corona Discharge in Wire-Cylinder Electrodes Configuration

The behavior of corona discharge was investigated in wire–cylinder electrodes under the effect of a crossed magnetic field. Townsend’s formula was used commonly with a modified empirical formula to evaluate the different parameters of corona discharge in positive and negative discharge. By using a least-squares fitting, the dimensional constants A, K, and the exponent n displayed a significant dependence on the applied magnetic field. An improvement of pre-breakdown has been achieved by using a crossed magnetic field. For both polarities, while the magnetic field is present, breakdown voltage VB and corona inception voltage V0 increased, whereas the corona current decreases. In addition, the corona inception voltage was greater in positive corona in the absence of a magnetic field, while the opposite occurred regarding the crossed magnetic field. Furthermore, the breakdown streamer demonstrated significant triggering in the negative corona by applying the magnetic field.

THE INFLUENCE OF CATHODE MATERIAL ON THE CHARACTERISTICS OF PULSED NEGATIVE CORONA IN OXYGEN

Change in time for the shape of the discharge current pulses of the pulsed negative corona in oxygen with cop-per and stainless steel cathodes has been studied for two discharge modes. The change lies in the decrease of the pulse amplitude and duration at half maximum. It is shown that for stainless steel cathodes, the amount of electric charge transferred in one pulse of the discharge current is 15% greater than for copper cathodes. It is also shown that under the maximum load mode, the amount of charge transferred in one pulse of the discharge current is de-creased with time by 10% for both types of cathodes. It is shown that ozone synthesis in the electrode system with copper cathodes is 25% more efficient.

Simulation of negative corona discharge in atmospheric air: from mode of Trichel pulses to stationary discharge

The paper presents a 2D multi-fluid non-stationary model of a negative corona discharge in atmospheric-pressure air in the needle-to-plane diode. Discharges were simulated in gaps up to 1 cm with an applied voltage in the range of 8-100 kV. The simulation results demonstrate two stages of the discharge evolution: a pulsed-periodic stage called the Trichel pulses mode and a stationary glow discharge mode. The spatio-temporal distributions of the discharge plasma and electric field are shown in detail. Physical mechanism of Trichel pulses formation and transition to the stationary discharge are also revealed. The duration of the Trichel pulse mode gradually decreases with increasing of the applied voltage.

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.