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使用人工神经网络(ANN)对HL-2A装置破裂放电进行了离线预测研究。采用了两种方法训练网络,一种方法是采用原始实验数据作为网络输入训练网络,另一种是把训练样本中的Mirnov原始实验信号进行预处理,目的是突出Mirnov原始信号隐含的破裂信息。比较这两种方法,结果表明第二种方法获得的网络对破裂放电能够做出更加准确的预测。     

HL-2A装置破裂放电期间逃逸电子产生过程的实验观测

大破裂放电不仅会在第一壁和偏滤器靶板上产生巨大的热负载沉淀和强烈的电磁力，而且会产生强烈的逃逸电子。逃逸电子的产生给托卡马克的运行造成很大的危害。它的巨大能量可以使装置的某些部份被击穿。等离子体电流熄灭时，环电压有明显上升。因为，等离子体电流的快速下降能引起感应电压，其值能通过环电压线圈测到。     

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HL-2A装置MHD不稳定性实时预测破裂系统采用了一种简单有效的方法来预测MHD不稳定性导致的等离子体大破裂。利用Mirnov线圈探测MHD信号,根据信号的振幅或频率特点设定计算方法,来预测等离子体破裂先兆,然后用激光吹气注入杂质来缓解等离子体破裂。研究结果表明,该系统能够实时预测破裂先兆,按量注入杂质后,可达到破裂缓解目的。     

脉冲放电破岩等离子体通道长度预测方法

针对高压脉冲放电破岩电弧等离子体通道长度难以预测的问题,构建了高压脉冲放电破岩综合试验平台,测量了花岗岩-自来水组合介质下电弧等离子体通道发展特性及典型电流、电压参数,提取了不同电极间距和脉冲放电次数下岩石表面形成的破碎区域。基于能量平衡方程建立了岩石中电弧等离子体通道的阻抗模型,采用迭代优化算法获取阻抗模型参数的近似最优解,模型计算结果与试验结果的相对误差小于7%。基于优化参数,利用实测电流电压数据预测了等离子体通道的长度。模型预测的等离子体通道长度与实测值的绝对误差均处于毫米量级,且两者的相对误差小于10%,为高压脉冲放电破岩系统电源-电极负载的匹配设计提供了理论支撑。     

等离子体破裂时HL-2M真空室的结构应力分析

利用ANSYS有限元程序分析计算了等离子体线性大破裂时HL-2M真空室上的感应电流及电磁力,并对真空室进行了结构应力分析。结果表明,真空室上主要产生环向感应电流和径向电磁力,且主要集中在真空室壳体的柱状段;在破裂产生的电磁力及其他机械载荷的作用下,真空室的结构设计能较好地满足强度和刚度要求。     

HL-2A装置高密度破裂的实验观测

大破裂发生时，不仅会在第一壁和偏滤器靶板上产生大的热负载沉积。并会由于晕电流而产生强烈电磁力。这种电磁力能够对偏滤器室及真空室内的部件造成损害。因此，如何避免大破裂放电是托卡马克运行中一个重要课题。为了减轻和控制大破裂，必须清楚地认识其产生机制和发生特性。     

HL-2M 装置初始等离子体放电阶段的直流辉光放电清洗系统研制

基于 HL-2M 托卡马克初始等离子体放电的工程需求,设计并研制了直流辉光放电清洗系统,包括电 极、馈线、电源、控制以及监测等关键部件和辅助子系统。研制完成后开展了系统装配和工程调试,并投入到首 次等离子体放电。实验结果表明,该直流辉光放电系统运行稳定、可靠,且此辉光放电清洗显著降低了真空室本 底杂质浓度,能满足 HL-2M 装置初始等离子体放电的壁条件需求。     

HL-2A装置破裂预报系统设计与研究

国际上现代聚变实验的大装置纷纷发现破裂放电而导致电流突然中止，造成装置遭受重大的危害。因而在HL-2A装置上建立一种预报放电破裂先兆的报警系统，设计并实现了MHD的实时检测与处理系统。研究结果表明，该系统可以实时地预报放电破裂先兆．避免能量与电流突然衰竭。     

HL-2A装置反馈控制系统程序放电的实现

HL-2A装置反馈控制系统的主要任务是逐步实现对等离子体电流、位移、形状和密度的实时反馈控制，从而能够按照实验目的对托卡马克等离子体进行各种试验。主要介绍了HL-2A装置反馈控制系统的硬件组成以及软件的结构和特点。这个系统能够很好地满足HL-2A装置程序放电的要求。     

HL-2A tokamak disruption forecasting based on an artificial neural network

Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibility of developing a neural network predictor that intervenes well in advance for avoiding plasma disruption or mitigating its effects.     

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HL-2A和HL-1M装置采用了激光吹气注入高Z杂质来缓减大破裂中的等离子体电流衰竭,并给出了初步实验结果。在HL-2A装置上建立了利用MHD扰动的参量预报放电破裂先兆的报警系统,研制了MHD实时检测与处理系统,实现了放电破裂先兆的预报、快速触发激光吹气、形成阻性高辐射等离子体、消耗热能和磁能,缓减大破裂。实验证明,这是一种使得大型聚变实验装置在放电破裂之前显著减少等离子体中热能和磁能,而且能安全终止放电的简单、快速和有效的途径。     

Prediction of the plasma distribution using artificial neural network

In this work,an artificial neural network (ANN) model is established using a back-propagation training algorithm in order to predict the plasma spatial distribution in an electron cyclotron resonance (ECR)—plasma-enhanced chemical vapor deposition (PECVD) plasma system. In our model, there are three layers:the input layer, the hidden layer and the output layer. The input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is our target output neuron: the plasma density. The accuracy of our prediction is tested with the experimental data obtained by a Langmuir probe, and ANN results show a good agreement with the experimental data. It is concluded that ANN is a useful tool in dealing with some nonlinear problems of the plasma spatial distribution.     

Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models.     

Mitigation and prediction of disruption on the HL-2A Tokamak

Injection of high-Z impurities into plasma has been proved to be able to reduce the localized thermal load and mechanical forces on the in-vessel components and the vacuum vessel, caused by disruptions in Tokamaks. An advanced prediction and mitigation system of disruption is implemented in HL-2A to safely shut down plasmas by using the laser ablation of high-Z impurities with a perturbation real-time measuring and processing system. The injection is usually triggered by the amplitude and frequency of the MHD perturbation field which is detected with a Mirnov coil and leads to the onset of a mitigated disruption within a few milliseconds. It could be a simple and potential approach to significantly reducing the plasma thermal energy and magnetic energy before a disruption, thereby achieving safe plasma termination. The plasma response to impurity injection, a mechanism for improving plasma thermal and current quench in major disruptions, the design of the disruption prediction warner, and an evaluation of the mitigation success rate are discussed in the present paper.     

变参数混沌时间序列的神经网络预测研究

研究一类复杂变参数混沌系统时间序列的预测问题.首先构造一个变参数Logistic映射,分析变参数混沌系统的特点,指出动力学特征不断变化的这类系统不存在恒定形状的吸引子;结合Takens嵌入定理和神经网络理论,阐述神经网络方法预测具有恒定吸引子形状的混沌系统可行的原因,分析研究其用于预测变参数混沌系统的潜在问题.变参数Ikeda系统的神经网络预测试验验证了理论分析结果,试验还表明,简单增大预测训练样本数可能降低泛化预测精度,训练集的选择对这类系统的泛化预测效果影响极大,指出混沌时间序列预测实用化必须研究解决这类变参数混沌系统的预测. 关键词： 混沌 预测 神经网络 变参数系统     

超声探伤信号的人工神经网络识别

粗晶奥氏体不锈钢的超声探伤受到能否有效区分有用信号与背影噪声的限制，目前人们大多倾向使用频率分隔来提高缺陷回波比例。本文则介绍一种用傅里叶变换作特征提取，用前馈网络自动识别奥氏体钢中缺陷信号的方法。在作者的实验中，这种方法的正确识别率达到９０％。