On-line neuro-expert monitoring system for Borssele Nuclear Power Plant

A new method for an on-line monitoring system for the nuclear power plants has been developed utilizing the neural networks and the expert system. The integration of them is expected to enhance a substantial potential of the functionality as operators support.

The recurrent neural network and the feed-forward neural network with adaptive learning are selected for the plant modeling and anomaly detection because of the high capability of modeling for dynamic behavior. The expert system is used as a decision agent, which works on the information space of both the neural networks and the human operators. The information of other sensory signals is also fed to the expert system, together with the outputs that the neural networks generate from the measured plant signals. The expert system can treat almost all known correlation between plant status patterns and operation modes as a priori set of rules.

From the off-line test at Borssele Nuclear Power Plant (PWR 480 MWe) in the Netherlands, it was shown that the neuro-expert system successfully monitored the plant status. The expert system worked satisfactorily in diagnosing the system status by using the outputs of the neural networks and a priori knowledge base from the PWR simulator. The electric power coefficient is simultaneously monitored from the measured reactive and active electric power signals.     

Application of advanced core process monitoring procedures in German power reactors

The nuclear reactor core design and the nuclear fuel management have been changed remarkable during the last few years. This development was initiated by increasing costs for the fuel recycling and nuclear waste storage. The fuel material, the fuel pellet fabrication, the fuel assembly structure and the core composition have been varied to get an effective fuel exploitation. Based on advanced core process conditions the reactor power and the fuel burn-up have been increased at German plants in recent years. Improved dynamic process monitoring procedures are required to get more information about the varied core process behaviour during the reactor operation. Since several years ISTec has been performed investigations to the process monitoring based on process signal measurements in German nuclear power plants. Using the standard instrumentation of the plants process signals have been measured and analysed by means of the digital data acquisition system SIGMA. The measured time signals are influenced by core process transients, global and local process fluctuations and by signal line transfer functions. Advanced time series analysis methods have been applied to separate different process effects in the multiple signal matrix. The separation of different process influences can improve significantly the information about the process condition in the reactor core.     

预电应力对硅橡胶改性环氧树脂固化物结构形态的影响

针对预电应力对硅橡胶改性环氧树脂固化物微观结构形态影响的问题，通过实验研究发现，在硅橡胶改性环氧树脂的固化过程中施加直流预电应力后，固化物中的硅橡胶颗粒粒径变小，数量变多，同时这种影响与预电应力的极性无关，而与预电应力强度有关．     

Measurement methods for LCoS displays

Abstract— Two measurement methods to characterize uniformity and the absolute cell gap of LCoS images will be reviewed. These are simple to use in a production environment and have allowed major improvements in manufacture to be achieved. For the purpose of this paper, the liquid‐crystal mode used is the 45° normally black mode.     

正压型防爆装置工作原理及实例分析

在遵循GB3836国家标准的基础上，通过对正压型防爆配电柜的实物分析，详细介绍了正压型防爆装置的防爆原理、电气控制原理、产品结构及组成部分。     

大型水轮发电机磁极铁芯的油压装置设计

该文详细介绍了一种大型水轮发电机转子磁极铁芯装压使用的专用设备,它是大电机转子磁极装压的新型工艺方法,对于大型水轮发电机的生产制造具有较大的应用价值。     

百纳秒脉冲下水压对水开关击穿特性的影响

以俄罗斯西伯利亚研究院通过实验总结出的液体介质气泡击穿理论的全新阐述作为理论基础,开展了300 NS短脉冲下高压强水介质开关的击穿实验,获得了该条件下水介质击穿场强及耐压时间与水中压强关系的数据。结果表明在300 NS脉冲电压实验平台下,当水介质场强变化率为2~3 MV/CM时,水介质击穿电压和耐压时间均随着水中压强的提高而呈上升趋势,且趋势越来越显著。     

垫脚加锚-高拱坝加固增稳的高效方法

本文首先介绍一种加固高拱坝的高效方法—垫脚加锚法 ,并阐述预应力锚索的锚固机理 ,然后对李家峡拱坝的垫脚加锚布置作了介绍。李家峡拱坝垫脚加锚前后的地质力学模型试验成果和整体三维非线性有限元分析成果表明垫脚加锚确有很好的加固增稳效果     

高线厂净循环水系统清洗预膜的实施

在高线厂净循环水系统投入运行之前，进行了管网及设备的清洗与预膜工作，使高线生产具备了良好的运行条件。     

Axial offset control of PWR nuclear reactor core using intelligent techniques

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the dynamic behavior of the core and a fuzzy critic based on the operator knowledge and experience for the purpose of decision-making during load following operations. Simulation results show that this method can use optimum control rod groups maneuver with variable overlapping and may improve the reactor load following capability.