不同接线方式下介质阻挡放电的电气特性

为了研究不同接线方式对介质阻挡放电(DBD)电气特性的影响,降低表面放电等离子体的能耗,设计了一种新型微等离子体发生器,并对其在3种不同接线方式下的放电特性(包括Lissajous图形,放电功率、放电传输电荷、气隙电压等参数)进行了测量和比较.结果表明:有一个正面电极悬浮时,放电传输电荷和功率是两极接线方式下的一半,有效减少了放电能耗,并且此方式不等同于增大电极间距;背面电极悬浮时,起始放电电压和放电气隙电压都较高,但是放电传输电荷和放电功率均较小.     

DBD等离子体研究及其环境工程方面应用

阐述了介质阻挡放电(DBD)等离子体的放电机理、影响参数、反应器等效电路模型及电源电路的研究,介绍了介质阻挡放电臭氧合成、汽车尾气净化、温室气体转化、处理气体污染物等环境工程方面的研究进展,分析了传统方法在这些方面应用的优缺点,并展望了DBD等离子体技术的发展前景和研究方向。     

同轴介质阻挡放电功率和等效电容特性研究

以同轴—针放电结构为研究基础,采用Lissajous图像法,研究了介质阻挡放电管径、载气流速变化对放电功率和介质等效电容的影响关系;设计了正交实验,进一步研究载气流速和放电管径对周期传输电荷量显著性影响的大小.研究结果表明:随着载气速度的提升,放电功率和介质等效电容都呈减小趋势;随着放电管径的增大,介质等效电容减小.正交实验中,管径的F值为22.15,相比载气流速对传输电荷量的影响更加显著.     

煤介质阻挡放电脱硫的影响因素分析

依据介质阻挡放电理论,对原煤进行煤介质阻挡放电脱硫实验。实验结果表明,煤介质阻挡放电脱硫的影响因素与电极结构、介质成分和气隙距离等因素有关,如将介质放到放电间隙中间时脱硫的效果没有将介质直接放在放电极板上的脱硫效果好,加煤的情况下微放电的强度明显大于不加煤的微放电过程强度;脱硫效果呈"U"字型变化,由此推论,在一定的外界条件下,煤介质阻挡放电除硫有一个最佳的施加电压;随着电压的增加,放电脉冲的相位范围逐渐扩展。     

微腔结构介质阻挡放电的仿真与实验研究

为了深入研究微腔结构介质阻挡放电(MDBD)的放电机理,搭建了基于介质板表面网格微结构电极装置的实验平台.在分析MDBD微放电过程的基础上,提出了基于电压控制电流源(VCCS)的等效电路来模拟MDBD微放电气隙的动态变化.利用Matlab建立动态仿真模型,并对MDBD的放电特性进行仿真,得到了MDBD放电时的电压电流波形和Lissajous图形.同时,对MDBD的放电特性进行实验研究,并将仿真与实验结果进行对比,验证了MDBD动态仿真模型的准确性.进而利用仿真模型分析计算了不同电压幅值下平均放电功率和放电通道传输电荷量,结果表明,通过仿真计算和实验测量得到的结果及其变化规律是一致的.     

原煤脱硫模型仿真与实验研究

搭建了介质阻挡放电脱硫模型,在此基础上,通过仿真分析了影响脱硫放电效果的介质种类、介质厚度、气隙间距、电源电压、电源频率等因素,确定了各因素的最佳配合方案。对介质阻挡放电原煤脱硫实验数据进行了处理分析,发现实验结果与仿真结果基本一致。通过对比分析放电电流频谱图和Lissajous波形,做出进一步推论:加煤能提高放电功率,介质阻挡放电对原煤的脱硫效果体现在低次谐波上。     

低温等离子体灭活核糖核酸酶研究

该文主要探讨了低温等离子体技术在核糖核酸酶灭活方面的应用,分析了作用气体类型和等 离子体发生器结构对酶灭活性能的影响。实验结果表明,低温等离子体能够有效地灭活核糖核酸酶, 混有氧气或水汽的作用气体能够明显提高对酶的灭活能力,且悬浮式电极介质阻挡放电装置的灭活效 果优于表面介质阻挡放电装置。     

超级电容器ESR的测试方法研究

等效串联内阻(ESR)是超级电容器的主要电气参数之一,是影响超级电容性能的一个重要因素,准确测量ESR对研究超级电容器的特性和应用具有重要意义.测量ESR的主要方法有利用放电电压的跃变计算ESR、恒流充电法和时间常数法.本研究在对超级电容器等效模型进行分析和简化的基础上,利用超级电容器RC滤波电路的电压纹波计算出ESR,经过仿真比较,该方法方便准确.     

DBD电路参数的在线测量的实现和研究

准确而简单地测量DBD型负载电气参数一直以来都是研究重点,传统的测量方法是通过重构李萨茹图形读取坐标点进行人工计算,费时费力,效率低下且操作复杂。基于李萨如图形测量原理,设计了DBD型负载电气参数在线测量控制系统。该系统通过采集DBD负载上的关键点数据,重新构建李萨如图形,通过软件计算介质阻挡电容、气隙电容、放电维持电压以及放电功率。该控制系统具有操作简单、安全、效率高、误差小和实时在线测量的优点,并通过实验验证了所提出在线测量系统的正确性。     

表面介质阻挡放电致动器的配置特性研究

沿面介质阻挡放电(SDBD)等离子体发生器在等离子体的产生中起着重要的作用.通过实验和仿真计算相结合的的方法,研究了大气压环境下等离子体发生器的结构对正弦波电源作用下放电的影响.结果表明:相同条件下,对称结构发生器电场强度最大,产生的等离子体分布在正面电极的两侧,等离子体层面积大,亮度高,但消耗功率较大;不对称结构发生器电场强度最低,产生的等离子体在正负电极处对称分布,整体看上去比较均匀;不对称结构底面电极封装之后正面电极处电场强度增强,等离子体分布在正面电极的一侧,亮度居中,底面电极附近的电场降低,抑制了低电极放电消耗能量,但均匀性最差.证明了等离子体激励器的封装抑制了底面电极的放电,同时降低了功耗.     

Optimization and predictive modeling using S/N,RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel

In present work, micro-deep holes on AISI 304 stainless steel were drilled via electrical discharge machining (EDM) method. In the first phase of this work, the effect of test parameters on the drilling performance and the profile of drilled holes were investigated experimentally. Test parameters including discharge current, dielectric spray pressure and electrode tool rotational speed were taken and then the machining rate (MR), electrode wear rate (EWR), average over-cut (AOC) and taper angle (TA) were measured in order to assess the drillability of EDM. After experimental study, an analysis of variance was performed to identify the effect of the importance of test parameters on experiment outputs. In the second phase of this study, optimum process parameters were determined using signal-to-noise analysis and response surface methodology (RSM) for mono-optimization and multi-response optimization, respectively. In the last phase, regression analysis and artificial neural network (ANN) models for predicting the MRR, EWR, AOC and TA. As a result of experimental analysis, discharge current was the most important parameter for micro-drilling with EDM. It was found out that this parameter influenced positively MR, while it has negatively an effect on EWR, AOC and TA. Mathematical model based on ANNs exhibited a successful performance for predication of outputs. Optimum process parameters which were discharge current of 10.18 Å, dielectric liquid pressure of 58.78 bar and electrode tool rotational speed of 100 rpm for multi-objective optimization were determined through RSM with desirability function analysis in micro-deep hole EDM drilling of AISI 304 stainless steel.

     

Microstructuring of non-conductive silicon carbide by electrical discharge machining

The electrical discharge machining process is an established process for machining materials regardless of their mechanical properties. Thus this process is especially attractive for materials which are hard to machine with conventional machining methods. The only requirement a material has to fulfil is having a certain electrical conductivity. Ceramic materials, (e.g. zirconia, silicon nitride or silicon carbide) exhibit excellent mechanical properties but are mostly electrically non-conductive. This can be compensated by an applied, electrically conductive assisting electrode. With this modification, the electrical discharge machining of non-conductive ceramic material is enabled. In this study the micro electrical discharge machining of non-conductive sintered silicon carbide is investigated. The drilling process shows instabilities due to the excessive generation of carbon products. A stabilisation of the process up to the maximum depth of 420 μm is realized by two approaches: adapting process parameters and adapting the tool electrode geometry. An analysis of the amount of infeed used in a milling process shows that an infeed of 15 μm has the best material removal rate to tool wear rate ratio. A maximum material removal rate of 3.58 × 10^?3 mm³/min is achieved. Detached microstructures with an aspect ratio of 30 are machined. A conducted surface analysis indicates that the present removal mechanism is thermally induced spalling. Furthermore no heat affected zone is present in the machined near-surface area.     

A dental application of neural network computing: Classification of complex electrical impedance measurements to aid root canal treatment

Electrical impedance measurements provide an alternative diagnostic technique to the use of radiographs for aiding dental root canal treatment. Analysis of impedance data was based on Complex NonLinear Least Squares (CNLS) regression with electrical circuits as models. Different equivalent circuits were required to model the data at various depths within root canals. Therefore, it was not valid to compare directly the parameter values obtained for the same electrical components when different circuits were used. This problem was solved with a neural computing approach based on supervised training of the backpropagation algorithm to classify the data. Two strategies were investigated. The first produced a network output which indicated the electrode depth within the canal. The second approach employed the neural network as a preprocessor to establish which equivalent circuit was appropriate for the CNLS. Tests were also carried out to determine the minimum number of input nodes required by a neural network for this dental application.     

A design methodology for a bulk-micromachined two-dimensional electrostatic torsion micromirror

This paper presents a design methodology for a two-dimensional (2-D) electrostatic torsion micromirror fabricated with bulk-micromachining technology. The theoretical models in mechanical and electrostatic fields presented here provide insights into the influences of different design parameters on micromirror performance. Parametric numerical models built in ANSYS are used to more accurately predict its performance and further refine the design parameter values derived from the theoretical models. By use of the electrical analogy method, an equivalent electrical circuit is built in PSPICE to predict the static and dynamic performance of this micromirror, with the numerical simulation results as the input parameters. The equivalent electrical circuit has been demonstrated to be a simple and powerful approach to characterize the performance of this 2-D torsion micromirror. The test results for this micromirror reveal very good agreement between experimental and numerical results, taking into account fabrication tolerances and experimental accuracies. Incorporating the fabrication tolerances of bulk-micromachining technology, this design methodology can be readily applied to performance characterization and design optimization.     

基于Thevinin的锂离子动力电池建模实验研究

为了准确估算锂电池的荷电状态(SOC),对其等效电路模型进行了研究。通过充放电实验研究锂电池的电特性,利用充放电电压、电流数据辨识其欧姆内阻、极化内阻和极化电容参数,建立了较为精确的锂电池Thevenin模型。建立实验用磷酸铁锂电池的离散状态空间模型,在Matlab/Simulink环境下建立了该电池的仿真模型,并设计了放电实验。实验证明,建立的锂电池模型仿真数据与实测数据误差小于0.1 V,且随着充放电的进行误差逐渐减小,较好的跟随电池电压的变化,模型精度较高。     

Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes

In this work, the performance of rapid prototyping (RP) based rapid tool is investigated during electrical discharge machining (EDM) of titanium as work piece using EDM 30 oil as dielectric medium. Selective laser sintering, a RP technique, is used to produce the tool electrode made of AlSi10Mg. The performance of rapid tool is compared with conventional solid copper and graphite tool electrodes. The machining performance measures considered in this study are material removal rate, tool wear rate and surface integrity of the machined surface measured in terms of average surface roughness (Ra), white layer thickness, surface crack density and micro-hardness on white layer. Since the machining process is a complex one, potentiality of application of a predictive tool such as least square support vector machine has been explored to provide guidelines for the practitioners to predict various machining performance measures before actual machining. The predictive model is said to be robust one as root mean square error in the range of 0.11–0.34 is obtained for various performance measures. A hybrid optimization technique known as desirability based grey relational analysis in combination with firefly algorithm is adopted for simultaneously optimizing the performance measures. It is observed that peak current and tool type are the significant parameters influencing all the performance measures.