Application of fuzzy logic control system for regulation of differential pressure in Liquid Zone Control System

In this paper, fuzzy control scheme has been proposed for generating regulating signals to feed and bleed control valves, which are used in Liquid Zone Control System (LZCS) for maintaining constant pressure difference between gas outlet header and delay tank. The LZCS and the existing PI controller are briefly described. It is followed by the design of Fuzzy Controller. It consists of seven symmetric triangular input membership functions and output membership functions each. Mamdani implication has been used to infer output contribution from each rule. The centroid type of defuzzification method is employed to get the crisp output values. The fuzzy logic controller for feed and bleed control valves so designed has been validated by performing a variety of experiments on a full scale LZCS test setup at Bhabha Atomic Research Centre and its performance is analyzed and compared to that obtained with existing PI controller. In comparison with the existing PI controller, the fuzzy logic controller’s performance is superior in all cases considered.     

Sensor monitoring using a fuzzy neural network with an automatic structure constructor

The performance of fuzzy neural networks applied to sensor monitoring strongly depends on the selection of input signals. A large number of input signals may be involved to estimate an output signal for failure detection. However, as the number of input signals increases, the required training time increases exponentially and the uncertainty of the model increases significantly due to the irrelevant and/or the redundant inputs. In this paper, a fuzzy neural network with an optimal structure constructor has been successfully developed to achieve a reliable and efficient sensor monitoring system. A fuzzy neural network is used to estimate an output signal from the selected input signals. Correlation analysis and genetic algorithm (GA) are combined for automatic input selection. In addition, the optimal number of fuzzy rules is accomplished automatically by the GA integrated along with the automatic input selection. The status of sensor health is determined by applying sequential probability ratio test to the residuals between the measured signals and the estimated signals. The proposed sensor monitoring system has been validated by using a variety of sensor signals acquired from Yonggwang units 3 and 4 pressurized water reactors.     

Iterative PET Image Reconstruction Using Translation Invariant Wavelet Transform

The present work describes a Bayesian maximum a posteriori (MAP) method using a statistical multiscale wavelet prior model. Rather than using the orthogonal discrete wavelet transform (DWT), this prior is built on the translation invariant wavelet transform (TIWT). The statistical modeling of wavelet coefficients relies on the generalized Gaussian distribution. Image reconstruction is performed in spatial domain with a fast block sequential iteration algorithm. We study theoretically the TIWT MAP method by analyzing the Hessian of the prior function to provide some insights on noise and resolution properties of image reconstruction. We adapt the key concept of local shift invariance and explore how the TIWT MAP algorithm behaves with different scales. It is also shown that larger support wavelet filters do not offer better performance in contrast recovery studies. These theoretical developments are confirmed through simulation studies. The results show that the proposed method is more attractive than other MAP methods using either the conventional Gibbs prior or the DWT-based wavelet prior.     

Efficient reliability estimate of passive thermal hydraulic safety system with automatic differentiation

An approach for efficient estimation of passive safety system functional reliability has been developed and applied to a simplified model of the passive residual heat transport system typical of sodium cooled fast reactors to demonstrate the reduction in computational time. The method is based on generating linear approximations to the best estimate computer code, using the technique of automatic reverse differentiation. This technique enables determination of linear approximation to the code in a few runs independent of the number of input variables for each response variable. The likely error due to linear approximation is reduced by augmented sampling through best estimate code in the neighborhood of the linear failure surface but in a sub domain where linear approximation error is relatively more. The efficiency of this new approach is compared with importance sampling MCS which uses the linear approximation near the failure region and with Direct Monte-Carlo Simulation. In the importance sampling MCS, variants employing random sampling with Box-Muller algorithm and Markov Chain algorithm are inter-compared. The significance of the results with respect to system reliability is also discussed.     

基于最优化线抽样法的XAPR自然循环功能失效研究与分析

针对多维不确定性参数、小失效概率的功能可靠性分析,提出了一种优化线抽样的可靠性分析方法。该方法采用遗传算法求解约束条件的优化模型来寻求最优化重要方向,进而得到失效概率的高效估计。以西安脉冲堆（XAPR）自然循环冷却堆芯能力的可靠性评价为例,考虑模型与输入参数的不确定性,对中破口失水事故下的自然循环功能失效概率进行了量化分析。结果表明：与其他概率评估方法相比,本文方法具有很高的计算效率,同时又能保证很好的计算精度;对隐式非线性的功能可靠性分析是有效可行的,具有很强的适应性。     

基于BP神经网络的核探测器故障诊断方法研究

核探测器是核设施放射性监测的重要设备,为了保障该设备的持续稳定运行,本研究针对闪烁体探测器提出了一种基于BP神经网络的在线智能故障诊断方法。采用小波包变换将探测器输出信号从时域变换至频域后提取特征向量,将得到的特征向量作为BP神经网络故障诊断模型的输入,再通过误差梯度下降法对该模型的参数进行优化,最终利用最优的诊断模型完成故障类型的智能识别与分类,并将该方法与统计诊断方法和基于支持向量机的故障诊断方法进行横向的对比研究。研究结果表明,新方法的平均诊断准确率均优于上述两种方法。因此,该方法的应用能有效地提高核探测器的故障诊断准确率。     

基于平稳小波变换的毛刺检测算法

核爆炸地震监测技术研究中,数据质量检测是地震数据自动处理的基本内容,毛刺是影响数据质量的主要问题数据。基于平稳小波变换和非线性能量检测算法,给出一种毛刺自动检测算法。平稳小波变换弥补了正交小波变换存在的不足,可以使尺度分解结果的长度和原始数据保持一致,具备时移不变性。非线性能量检测算法可以增强记录中的高频信号,对平稳小波变换的结果应用非线性能量检测算法,提高了记录中毛刺检测的准确性,非常适合连续地震监测数据自动处理的需要。实验结果表明,给出的这种算法特别有利于记录中小毛刺的检测,从而能够减小信号检测的误检率。     

基于频域数据注意力机制的核电厂水泵故障模式识别模型研究

针对核电厂水泵共性的异常振动、转子部件摩擦与磨损等故障模式,利用水泵最容易获取的泵壳加速度信号的频域数据为输入,提出了一种结合卷积神经网络和注意力网络的频域数据注意力机制方法,并建立了核电厂水泵故障模式识别模型。研究结果表明:相对于传统方法,利用频域数据作为输入、基于频域数据注意力网络算法建立的水泵故障模式识别模型输入的数据长度更短,能够有效提升模型训练的效率,该故障模式识别模型在测试集上的故障模式识别准确率达到100%,优于其他基于深度学习算法建立的故障诊断模型,证明了本文提出方法的优势。      

Fault diagnosis and classification based on wavelet transform and neural network

In this paper we present a novel method in fault recognition and classification in Nuclear Power Plant (NPP) using wavelet transform based Artificial Neural Network (ANN). We first simulate 10 design basis accidents (DBA) of a VVER-1000 using 15 input parameters with employing a Multilayer Perceptron (MLP) Neural Network with Resilient Backpropagation (RBP) algorithm. Afterwards we present the application of wavelet transform for its temporal shift property and multiresolution analysis characteristics to reduce disturbing perturbations in input training set data. Simulation of Artificial Neural Network and wavelet transform was performed using MATLAB software. The results show an enhanced accuracy and speed in fault recognition and high degree of robustness.     

适用于核电厂BEPU的高效全局敏感性分析方法开发及应用

最佳估算加不确定性（BEPU）分析是IAEA推荐用于核电厂事故安全分析的方法,该方法中一个关键步骤为评估输入参数对目标输出的影响大小,即定量敏感性分析。传统BEPU分析中常使用基于线性或单调假设的局部敏感性分析方法,其难以适用于复杂的核反应堆系统,而全局敏感性分析则由于计算成本过高而难以在实际工程中应用。本研究中针对矩独立全局敏感性分析方法开展了优化研究,使用高阶模型表示、高斯求积公式等方法降低矩独立敏感性度量的计算成本,得到了一种高效的敏感性分析方法。使用了多个例题对优化方法的可靠性进行了验证,并将其应用于LOFT（loss of fluid test）大破口事故的敏感性分析。结果表明,该高效敏感性分析方法能准确识别核反应堆事故工况中的重要参数,并能对参数重要度进行定量排序。     

Use of Sobol's Quasirandom Sequence Generator for Integration of Modified Uncertainty Importance Measure

Sensitivity analysis of model output is relevant to a number of practices, including verification of models and computer code quality assurance. It deals with the identification of influential model parameters, especially in complex models implemented in computer programs with many uncertain input variables. In a recent article a new method for sensitivity analysis, named HIM* based on a rank transformation of the uncertainty importance measure suggested by Hora and Iman was proved very powerful for performing automated sensitivity analysis of model output, even in presence of model non-monotonicity. The same was not true of other widely used non-parametric techniques such as standardized rank regression coefficients. A drawback of the HIM* method was the large dimension of the stochastic sample needed for its estimation, which made HIM* impracticable for systems with large number of uncertain parameters. In the present note a more effective sampling algorithm, based on Sobol's quasirandom generator is coupled with HIM*, thereby greatly reducing the sample size needed for an effective identification of influential variables. The performances of the new technique are investigated for two different benchmarks.     

An on-line data acquisition system of oscilloscope-embedded input/output controller for cavity BPM measurement

Cavity beam position monitor(BPM) is widely used in a precise electron beam position measurement. Based on high performance oscilloscope-embedded EPICS input/output controller,we developed an on-line cavity BPM signal processing system for fast data acquisition solution when designing a cavity BPM.Also,methods for extracting the position information from cavity pickup signals and calibration algorithm are included in this solution.     

The minimization of uncertainties in reactor theory

This paper deals with the question of the requirements associated with the uncertainties in nuclear data. This question arises when given target accuracies are required for selected performance parameters of a nuclear reactor. This problem is also known as the ‘inverse problem’. There are two ways to deal with the inverse problem as presented in this paper. The first way is to consider the nuclear data only in what we call the ‘differential approach’. The second way is to consider the nuclear data as well as integral experiments in what we call the ‘integro-differential approach’. When there are no specific target accuracies, minimization of the uncertainties in the nuclear data can be considered by minimization of the reactor input parameters' entropy.     

基于频率补偿小波的屏蔽主泵裂纹转子识别

针对转子裂纹振动信号不易被发现的问题,利用小波包分析方法,在无动不平衡频率的频带内进行分析,从中找出裂纹振动的特征频率。在分析中,针对小波算法存在的频率混淆、幅值失真、主频偏移的缺陷,在前人研究基础上,提出了一种频率补偿小波算法,利用该方法分析了核用屏蔽主泵转子裂纹振动信号,并得出了其振动的特征频率。结果表明,相比一般小波包,频率补偿小波包能识别出振动的特征频率,具有良好的实用价值。     

Classification of two-phase flow regimes via image analysis and a neuro-wavelet approach

A non-intrusive method of two-phase flow identification is investigated in this paper. It is based on image processing of data obtained partly from dynamic neutron radiography recordings of real two-phase flow in a heated metal channel, and partly by visible light from a two-component mixture of water and air. Classification of the flow regime types is performed by an artificial neural network (ANN) algorithm. The input data to the ANN are some statistical moments of the wavelet pre-processed pixel intensity data of the images. The pre-processing used in this paper consists of a one-step multiresolution analysis of the 2-D image data. The investigations of the neutron radiography images, where all four flow regimes are represented, show that bubbly and annular flows can be identified with a high confidence, but slug and churn-turbulent flows are more often mixed up in between themselves. The reason for the faulty identifications, at least partially, lies in the insufficient quality of these images. In the measurements with air-water two-component mixture, only bubbly and slug flow regimes were available, and these were identified with nearly 100% success ratio. The maximum success ratio attainable was approximately the same whether the raw data was used without wavelet preprocessing or with a wavelet preprocessing of the input data. However, the use of wavelet preprocessing decreased the training time (number of epochs) with about a factor 100.     

A real-time Grad–Shafranov PDE solver and MIMO controller

Plasma control experiments require enormous computational power to solve large problems with critical time constraints. For tokamak control, the non-linear and constrained Grad–Shafranov equation needs to be solved in real-time with a cycle time of less than 1 ms. A new algorithm for the solution of this equation based on discrete sine transforms and a tridiagonal solver rather than the commonly used cyclic reduction algorithm is presented. Input signals from magnetic probes and flux loops are the constraints for the equation that must be continuously solved to calculate the magnetic equilibrium. A number of novel mathematical ideas were introduced and several generally applicable numerical strategies were developed using LabVIEW graphical dataflow programming to meet the critical timing goals. Benchmarks on CPUs are reported. Furthermore, the design of a MIMO (multiple input and output) controller to demonstrate the possibilities of tokamak position and shape control using graphical dataflow programming is discussed.     

Calculation of Neutron Albedos for Slab with Invariant Imbedding Method

The albedo data are important for use in solving the radiation streaming problem with albedo techniques, such as albedo Monte Carlo and albedo-Sn methods. This paper describes a method for calculating the energy-angle dependent doubly differential albedos for slab geomery with one-dimensional transport theory, based on the invariant imbedding method as well as the Sn method. Neutron albedo data calculated by the invariant imbedding method, are compared with those calculated by the Sn method and with the experimental data. It is found that the invariant imbedding method can be used to calculate the albedo data for a semi-infinitely thick slab several dozens of times faster than when using the Sn method. The calculated results have excellent agreement with the measured values.     

Study of recursive model for pole-zero cancellation circuit

The output of charge sensitive amplifier （CSA） is a negative exponential signal with long decay time which will result in undershoot after C-R differentiator. Pole-zero cancellation （PZC） circuit is often applied to elim- inate undershoot in many radiation detectors. However, it is difficult to use a zero created by PZC circuit to cancel a pole in CSA output signal accurately because of the influences of electronic components inherent error and environmental factors. A novel recursive model for PZC circuit is presented based on Kirchhoff＇s Current Law （KCL） in this paper. The model is established by numerical differentiation algorithm between the input and the output signal. Some simulation experiments for a negative exponential signal are carried out using Visual Basic for Application （VBA） program and a real x-ray signal is also tested. Simulated results show that the recursive model can reduce the time constant of input signal and eliminate undershoot.     

异构系统三维高保真堆芯中子输运计算程序ALPHA研发进展

ALPHA是哈尔滨工程大学核动力仿真研究中心研发的基于异构系统的三维高保真堆芯中子输运计算程序。ALPHA程序基于性能优化的二维特征线装载图形处理单元（GPU）并行计算核心,基于MPI+CUDA混合编程模型实现粗细粒度的异构系统多节点并行并应用通信掩盖优化。ALPHA的共振计算模型采用原创的细群 子群二级离散策略并采用多群求解核心适配异构系统。ALPHA采用MOC EX实现三维全堆芯中子输运异构并行计算及GPU并行的粗网有限差分加速。数值结果表明,ALPHA程序在保证计算精度的前提下,具备较高的并行效率和一定的可扩展性,有望实现数值反应堆中中子学计算的轻量化与工程化应用。     

基于自适应遗忘因子RLS算法的稳压器模型在线辨识

为提高稳压器时变系统模型辨识的准确性及其参数在线辨识的快速性和鲁棒性,并研究遗忘因子大小对遗忘因子递推最小二乘法算法特性的影响,提出了一种基于模糊算法的自适应遗忘因子递推最小二乘法算法。该算法以系统动态特性值与辨识模型值之间的残差时间序列平均值及其变化率作为模糊算法的输入,遗忘因子修正量为输出,从而实现遗忘因子的自适应调整。通过对某核电厂稳压器降压系统进行仿真,结果表明,该算法可实时调整遗忘因子大小,有效地解决了稳压器模型参数时变性的问题,得到了较精确的时变模型;有效地解决了参数辨识结果稳定性和收敛速度相互矛盾的问题。因此,该算法具有可行性和优越性。