盘管式蓄冷装置蓄冰过程的模拟研究

本文对盘管式蓄冷装置的蓄冷过程建立了集总参数的数学物理模型,并进行了理论计算.分析了该系统的换热效果,讨论了某些参数的变化对蓄冷器换热特性的影响.计算结果对该类换热器的设计和性能优化有一定指导作用.     

相变贮能球堆积床结合夜间自然风蓄冷的理论研究

本文对相变贮能球堆积床结合夜间自然风的蓄冷过程建立了数学物理模型,并进行了数值计算.分析了该系统的换热效果和充冷过程的特点及某些流动参数的变化对堆积床换热特性的影响.计算结果合理,所得结论对该类贮能系统的设计和性能优化有一定指导作用.     

温棚系统中传热问题的理论与实验研究

给出了描述温棚系统中传热问题的数学物理模型及计算方法，并进行了实验研究。模型中考虑了气象环境参数、土壤的热湿物性以及塑料棚膜的太阳光学性质和辐射特性，可预报温棚内空气和土壤中的温度和含湿量的分布及变化规律。模型的数值模拟结果与实测值在数值和变化趋势上符合得较好，从而表明模型的正确性与可靠性。     

绝缘介质局部放电过程及仿真模型构建

本文针对绝缘劣化与局部放电相互影响作用,分析了影响绝缘寿命的因素和局部放电的机理过程.重点阐述不良绝缘下的放电规律和特征,同时也分析介质的结构、特性对等效参数的影响.并以常见的绝缘缺陷为例,展示了三电容放电的物理模型.以放电的物理过程为基础建立仿真模型,根据实例计算仿真参数.该模型考虑放电通道的等效阻抗,构造了基于电阻-电感-电容的新的物理模型,同时对模型的拓扑结构给予改进.     

河水滴灌重力沉沙过滤池的清水流场模拟

为研究河水滴灌重力沉沙过滤池的水力特性及流场变化情况,采用多孔介质模型、κ-ε模型及VOF多相流模型对其进行了数值计算,并将计算结果与物理模型实测数据进行了对比,发现数值计算结果与物理模型结果吻合较好,说明采用的耦合模型具有一定的准确性和可靠度,并采用该耦合模型对沉淀池内水深方向的流速分布规律和流场进行了分析。结果表明,水流流速沿水深方向上的分布规律包含流速迅速增加和流速恒定2个阶段;沉淀池中存在横向水流流动和环形漩涡,并在尾部形成回流现象,不利于泥沙沉降,其结构有待进一步优化。     

基于梯度算子的煤耗与辅助汽水流量的通用关系式

以火电机组热经济性分析的统一物理模型和数学模型为基础,借助偏微分理论和梯度算子,建立了辅助汽水流量变化对机组经济性影响的强度系数计算模型,只要通过强度系数乘以辅助汽水流量的变化值就可以方便地得到辅助汽水流量变化对煤耗率的影响,大大简化了计算.通过对某300MW、600MW和1 000 MW机组典型工况的计算表明,应用强度系数模型计算时计算量小、精度高,为火电机组节能降耗以及热力系统定量分析提供了一种新的思路和方法.     

抽水蓄能电站侧式双进/出水口三维数值模拟

采用Realizable k-ε紊流数学模型对抽水蓄能电站侧式双进/出水口进流水流特性进行数值模拟,并将计算结果与物理模型试验的测量结果进行比较,验证了三维数值模拟计算的准确可靠性.模拟结果表明,该方法计算流体力学方便快捷,并可达到物理模型试验的精度.     

基于遗传算法的核电站蒸发器高阶水位模型的降阶方法

针对核电站蒸汽发生器的高阶水位输出模型,提出了次最优降阶算法与遗传算法相结合的模型降阶方法.利用该方法对典型负荷下的核电站蒸发器水位系统模型进行了降阶,并在Matlab平台上对降阶后的模型进行了仿真.结果表明:采用次最优降阶算法与遗传算法相结合的降价方法得到的优化模型比传统降阶方法得到的模型更接近原始模型,利用该方法可以解决高阶线性控制系统模型优化降阶的问题.     

应急柴油发电机组的建模与瞬态仿真

建立了某柴油发电机组柴油机的瞬态仿真模型,在Matlab/Simulink仿真平台编制了仿真计算程序,根据核电站反应堆安全停堆的负荷特性,进行了柴油机突加负载瞬态仿真计算,预测了柴油机的转速、喷油量、进气压力以及齿条位置等参数的变化。将计算结果与国外某公司的计算结果进行了对比分析,验证了所建立仿真模型的准确性及实用性。     

钢板热处理过程温度模拟与验证

以中厚板辊底式常化炉为研究对象,建立了炉内钢板热处理过程的物理模型和数学模型,并利用“黑匣子板温跟踪实验”对所建立的数学模型进行了验证.对比分析结果表明,模型计算误差在3％内,可用于生产中热处理工艺制度的计算分析.分析了钢板在低温热处理过程中温度场随时间的变化规律,所做工作对炉温制度的制定及优化有一定的参考价值.     

世界与中国发电量和装机容量的预测模型

对国内外权威机构预测的发电量、煤电装机容量与核电装机容量的数据进行了分析,应用多项式拟合方法建立了预测模型以及准确性的检验方法.预测了2010～2034年间世界的发电量、燃煤发电装机容量和非经济合作与发展组织国家的核电装机容量以及中国的发电量、煤电和核电装机容量.误差分析和拟合精度检验结果表明,所建数学模型拟合现有数据的效果非常好.预测结果表明国内外电力发展前景看好.     

Availability growth models and verification of power equipment

The general availability growth models for large scale complicated repairable system such as electric generating units, power station auxiliaries, and transmission and distribution installations are presented. The calculation formulas for the maintenance coefficient, mathematical expressions for general availability growth models, ways for estimating, and fitting on checking the parameters of the model are introduced. Availability growth models for electric generating units, power station auxiliaries, and transmission and distribution installations are given together with verification examples for availability growth models of 320–1000 MW nuclear power units and 1000 MW thermal power units, 200–1000 MW power station auxiliaries, and 220–500 kV transmission and distribution installations. The verification results for operation availability data show that the maintenance coefficients for electric generating units, power station auxiliaries, transmission and distribution installations conform to the power function, and general availability growth models conform to rules of availability growth tendency of power equipment.     

基于3KEYMASTER软件的核电站汽轮机数学模型与仿真

以福建宁德1000 MW核电机组汽轮机为研究对象,经过系统划分和对具体物理设备进行合理简化和假设后,建立了汽轮机组的动态数学模型。在3KEYMASTER仿真平台上进行了论证,经测试该模型能够正确反映核电站汽轮机的动态特性。     

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

A novel and modern framework for energy modeling is developed in this paper with a focus on nuclear energy modeling and simulation. The framework combines multiphysics simulations and real data, with validation by uncertainty quantification tasks and facilitation by machine and deep learning methods. The hybrid framework is built on the basis of a wide range of physical models, real data, mathematical and statistical methods, and artificial intelligence techniques. The framework is demonstrated in different applications, including quantifying uncertainties in computer simulations, multiphysics coupling, analysis of variance using machine learning surrogate models, deep learning of time series phenomena, and propagating parametric uncertainties of nuclear data. The applications demonstrated are oriented to nuclear engineering simulations, even though majority of the methods are applicable to other energy sources (eg, renewable). Efficient utilization of this framework is expected to yield a much better understanding of the physical phenomena analyzed as well as an improvement in the performance of the energy design/model under construction.     

A multiple objective linear programming model for power generation expansion planning

The planning of new units for electrical power generation is a problem which involves different and conflicting aspects. Besides cost, security issues and environmental concerns must be explicitly incorporated into the models. In this way mathematical models become more realistic, and they enhance the decision maker's comprehension of the complex and conflicting nature of the distinct aspects of the problem. A multiple objective linear programming model for power generation expansion planning is presented. The model considers three objective functions (net present cost of the expansion plans, reliability of the supply system, and environmental impacts) and three categories of constraints (load requirements, operational restrictions and budget). Three generating technologies are considered for power system expansion: oil, nuclear and coal.     

Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden

The development of a model for any energy system is required for proper design, operation or its monitoring. Models based on accurate mathematical expressions for physical processes are mostly useful to understand the actual operation of the plant. However, for large systems like combined heat and power (CHP) plants, such models are usually complex in nature. The estimation of output parameters using these physical models is generally time consuming, as these involve many iterative solutions. Moreover, the complete physical model for new equipment may not be available. However, artificial neural network (ANN) models, developed by training the network with data from an existing plant, may be very useful especially for systems for which the full physical model is yet to be developed. Also, such trained ANN models have a fast response with respect to corresponding physical models and are useful for real-time monitoring of the plant. In this paper, the development of an ANN model for the biomass and coal cofired CHP plant of Västhamnsverket at Helsingborg, Sweden has been reported. The feed forward with back propagation ANN model was trained with data from this plant. The developed model is found to quickly predict the performance of the plant with good accuracy.     

Failure probability and optimum microstructure of pressure vessel steels

This paper presents a method for assessing the probability of brittle fracture in steels used for nuclear power generating equipment. The method is based on a statistical analysis of microstructural parameters and on physical models of the initiation and propagation of cleavage microcracks. The least probability of brittle failure ascertained by this method has been utilized for determining the optimum ferrite grain size. This method represents a foundation for systematic control of the brittle fracture characteristics of engineering steels employed for the components of nuclear power stations.     

The energy storage mathematical models for simulation and comprehensive analysis of power system dynamics: A review. Part i

Energy storage systems are increasingly used as part of electric power systems to solve various problems of power supply reliability. With increasing power of the energy storage systems and the share of their use in electric power systems, their influence on operation modes and transient processes becomes significant. In this case, there is a need to take into account their properties in mathematical models of real dimension power systems in the study of various operation modes, design, etc. In this article the main types of energy storage devices, as well as the fields and applications of their use in electric power systems are considered. The principles of realization of detailed mathematical models, principles of their control systems are described for the presented types of energy storage systems. The article is an overview and can help in choosing a mathematical model of energy storage system to solve the necessary tasks in the mathematical modeling of storage systems in electric power systems.Information is presented on large hydrogen energy storage units for use in the power system.