Numerical study of atmospheric-pressure argon plasma jet propagating into ambient nitrogen

In this paper, a two-dimensional fluid model is used to study the properties of atmospheric-pressure argon plasma jet propagating into ambient nitrogen driven by a pulsed voltage, emphasizing the influence of gas velocity on the dynamic characteristics of the jet. The simulation results show that the argon jet exhibits a cylindrical shape channel and with the increase of propagation length, the jet channel gradually shrinks. The jet propagation velocity varies with time. Inside the dielectric tube, the plasma jet accelerates propagation and reaches its maximum value near the nozzle. Exiting from the tube, the propagation velocity of the plasma jet quickly decreases and when approaching the metal plane, the decrease of jet velocity slows down. The increase of gas speed leads to the variation of the jet spatial distribution. The electron density presents a solid structure at lower gas flow speeds, whereas an annular structure can be observed under the higher gas flow velocity in the ionization head. The jet length increases with the gas flow velocity. However, when the flow velocity exceeds a critical value, the increased rate of the plasma jet length becomes slow. Additionally, the influence of the gas flow speed on the production and transport of the reactive species is also studied and discussed.

The Effect of Oxygen Admixture with Argon Discharges on the Impact Parameters of Atmospheric Pressure Plasma Jet Characteristics

Dry argon (Ar) discharge and wet oxygen/argon (O2/Ar) admixture discharge for alternating current atmospheric pressure plasma jets (APPJs) were studied for Ar discharges with flow rates ranging from 0.2 to 4 slm and for O2/Ar discharges with different O2 ratios and flow rates ranging from 2.5 to 15 mslm. The voltage–current waveform signals of APPJ discharge, gas flow rate, photo-imaging of the plasma jet length and width, discharge plasma power, axial temperature distribution, optical emission spectra, and irradiance were investigated. Different behavior for varying oxygen content in the admixture discharge was observed. The temperature recognizably decreased, axially, far away from the nozzle of the jet as the flow rate of dry argon decreased. Similar behavior was observed for wet argon but with a lower temperature than for dry argon. The optical emission spectra and the dose rate of irradiance of a plasma jet discharge were investigated as a function of plasma jet length, for dry and wet Ar discharges, to determine the data compatible with the International Commission on Non-Ionizing Radiation Protection (ICNIRP) data for irradiance exposure limits of the skin, which are suitable for the disinfection of microbes on the skin without harmful effects, equivalent to 30 μJ/mm2.

Numerical Study of the Micro-Jet Formation in Double Flow Focusing Nozzle Geometry Using Different Water-Alcohol Solutions

The purpose of this work is to determine, based on the computational model, whether a mixture of a binary liquid is capable of producing longer, thinner and faster gas-focused micro-jets, compared to the mono-constituent liquids of its components. Mixtures of water with two different alcohols, water + ethanol and water + 2-propanol, are considered. The numerical study of pre-mixed liquids is performed in the double flow focusing nozzle geometry used in sample delivery in serial femtosecond crystallography experiments. The study reveals that an optimal mixture for maximizing the jet length exists both in a water + ethanol and in a water + 2-propanol system. Additionally, the use of 2-propanol instead of ethanol results in a 34% jet length increase, while the jet diameters and velocities are similar for both mixtures. Pure ethanol and pure 2-propanol are the optimum liquids to achieve the smallest diameter and the fastest jets. However, the overall aim is to find a mixture with the longest, the smallest and the fastest jet. Based on our simulations, it appears that water + 2-propanol mixture might be slightly better than water + ethanol. This study reveals the dominant effect of liquid viscosity on the jet breakup process in a flow focusing nozzles operated under atmospheric conditions.

On the Effect of Block Roughness in Ogee Spillways with Flip Buckets

In this study, the effect of the presence of bed-block roughness in an ogee spillway on energy dissipation and jet length is investigated. A series of experimental and numerical tests were conducted using an ogee spillway with block roughness on the bed without a flip bucket and with a flip bucket at different take-off angles (32 °C and 52 °C). To model the free-flow surface, the volume-of-fluid (VOF) method and turbulence model from RNG k–ε were used. Results indicated that the numerical model is fairly capable of simulating a free-flow surface over an ogee spillway; using block roughness on the spillway chute without a bucket, relative energy dissipation increased by 15.4% compared to that in the spillway with a smooth bed, while for the spillway with 32 °C and 52 °C buckets, it increased by 9.5%. The jet length for a spillway with a flip bucket and roughened bed decreased by 8% to 58% compared to that in a smooth bed. Lastly, the relationships for the estimation of relative energy dissipation and jet length are presented.

Estimation of the depth of penetration in a plunging hollow jet using artificial intelligence techniques

Purpose: Experimental investigations assessment and comparison of different classical models and machine learning models employed with Gaussian process regression (GPR) and artificial neural network (ANN) in the estimation of the depth of penetration (Hp) of plunging hollow jets. Design/methodology/approach: In this analysis, a set of data of 72 observations is derived from laboratory tests of plunging hollow jets which impinges into the water pool of tank. The jets parameters like jet length, discharge per unit water depth and volumetric oxygen transfer coefficient (Kla20) are varied corresponding to the depth of penetration (Hp) are estimated. The digital image processing techniques is used to estimate the depth of penetration. The Multiple nonlinear regression is used to establish an empirical relation representing the depth of penetration in terms of jet parameters of the plunging hollow jets which is further compared with the classical equations used in the previous research. The efficiency of MNLR and classical models is compared with the machine learning models (ANN and GPR). Models generated from the training data set (48 observations) are validated on the testing data set (24 observations) for the efficiency comparison. Sensitivity assessment is carried out to evaluate the impact of jet variables on the depth of penetration of the plunging hollow jet. Findings: The experimental performance of machine learning models is far better than classical models however, MNLR for predicting the depth of penetration of the hollow jets. Jet length is the most influential jet variable which affects the Hp. Research limitations/implications: The outcomes of the models efficiency are based on actual laboratory conditions and the evaluation capability of the regression models may vary beyond the availability of the existing data range. Practical implications: The depth of penetration of plunging hollow jets can be used in the industries as well as in environmental situations like pouring and filling containers with liquids (e.g. molten glass, molten plastics, molten metals, paints etc.), chemical and floatation process, wastewater treatment processes and gas absorption in gas liquid reactors. Originality/value: The comprehensive analyses of the depth of penetration through the plunging hollow jet using machine learning and classical models is carried out in this study. In past research, researchers were used the predictive modelling techniques to simulate the depth of penetration for the plunging solid jets only whereas this research simulate the depth of penetration for the plunging hollow jets with different jet variables.