基于GDT的聚变裂变混合堆堆芯参数初步设计研究

基于Gas Dynamic Trap(GDT)装置的实验进展,提出了用于驱动聚变裂变混合堆包层的聚变堆芯参数设计。基于零维堆芯物理模型,计算分析给出了一套聚变功率为50MW的初步堆芯参数方案。利用GDT装置的实验结果对该物理模型进行计算对比校验,显示该物理模型和设计参数的可靠性。     

聚变发电反应堆概念设计研究

在广泛分析聚变能相关领域研究发展状况和国际热核聚变实验堆(ITER)物理与技术基础上,提出了一个考虑了技术可行性的聚变发电反应堆概念(称之为FDS Ⅱ)。这个概念具有ITER参数适量外推的等离子体物理与技术水平的聚变堆芯和具有发展潜力的液态锂铅氚增殖包层,在对这个概念进行中子学、热工水力学、力学、安全与环境影响和经济学等一系列计算分析的基础上,给出了初步的概念设计和进一步设计优化的共性原则。     

混合堆水冷快裂变包层的中子学设计研究

本工作以国际热核实验堆(ITER)的等离子体参数和堆芯结构为基础,对水冷、球床结构的快裂变包层混合堆作了一维和二维中子学设计研究,并与纯聚变堆的功能作了对比。说明混合堆作为聚变能的前期应用是必要的和可能的。     

次监界聚变嬗变堆高性能双冷包层中子学概念设计研究

基于聚变实验装置近期可达到的堆芯ＤＴ等离子体参数水平,在一维燃耗计算和优化分析的基础上,验证了氚自持、年约处理２９ 个同等热功率压水堆年产的长寿命锕系元素和一定数量裂变产物的次临界聚变嬗变堆带有双冷却系统高性能包层的中子学可行性,并给出了初步的设计方案。     

聚变驱动次临界堆经济性分析

利用自主开发的聚变系统分析软件SYSCODE对聚变驱动次临界堆的经济性进行了计算和分析。首先,对典型的环径比(标准环径比)设计方案,从堆芯几何参数、物理及工程参数等两个方面分析它们与成本的关系,然后,详细分析低、标准、高环径比三套候选设计方案的经济性与中子学参数的关系。研究发现,其经济性主要取决于包层中子学参数,另外,其经济性与几何、物理及工程参数的关系在一定条件下有别于纯聚变系统。     

加速器驱动次临界嬗变堆的参数敏感性分析

采用自主开发的加速器驱动次临界堆芯稳态分析程序LAVENDER,对加速器驱动次临界系统(ADS)的冷却剂种类、堆芯次临界度、燃料组件几何尺寸以及堆芯热功率进行敏感性分析。计算结果表明,针对加速器质子束流、次锕系核素(MA)嬗变率或热工安全裕量等单一堆芯性能参数进行优化时,往往导致其他堆芯性能参数的恶化。因此在ADS堆芯设计时,应做出折中选择以平衡不同设计目标参数。     

FEB-E3维中子学屏蔽分析和计算

FEB-E(Fusion Experimental Breeder)是聚变实验增殖堆的工程概要设计。FEB的主要目标是:(1)演示混合堆工程特性和裂变燃:抖和氚的增殖性能;(2)试验混合堆关键部件和聚变结构材料。环向场线圈TFC位于真空室及屏蔽层外侧,是FEB-E关键部件之一,其造价约占整个堆的40％。TFC由超导(Nb_3Sn)、绝缘体(聚酰亚胺)、稳定剂(Cu)和结构(316SS)等材料组成。由于TFC的超导、绝热和绝缘等材料易受来自堆芯聚变中子的辐照损伤,从而会严重影响混合堆的经济、稳定及安全运行,因此需要在等离子体堆芯与TFC之间设置一个屏蔽层把TFC所受的辐照损伤和核热沉积严格要求在允许范围以内。     

CEFR在ULOF工况下的自然循环能力分析

自然循环能力是衡量钠冷快堆固有安全性的重要指标,堆芯布置、回路设计及工况参数等都会影响堆芯自然循环能力,因此不同堆型的自然循环能力有很大差异。为了保证堆芯事故得到有效缓解,中国实验快堆(CEFR)的设计中通过优化系统布置,重点考虑了堆芯自然循环。本文采用SAS4A程序对CEFR进行系统建模,分析了CEFR在无保护失流(ULOF)工况下的堆芯热工水力参数瞬态特性,验证了CEFR利用自身自然循环和负反馈设计进行事故缓解的能力,本文还对一回路流动阻力和二回路钠装量对堆芯自然循环的影响进行分析。计算结果表明,CEFR具有良好的自然循环特性,在ULOF工况下可以依靠其负反馈停堆,并能够建立起稳定的自然循环从而导出堆芯余热。     

混合能源堆包层中子学初步概念设计

提出了以天然铀或压水堆乏燃料的锆合金为燃料、轻水冷却、后处理铀钚不分离、具有良好防核扩散性能的能源堆概念。利用MCNP程序与ORIGENS程序相耦合的方法设计了包层中子学初步方案,给出了初步换料方案。能源堆对聚变堆芯参数的要求不高于国际热核聚变实验堆(ITER),可以实现利用~(238)U的目的。     

固态钍基熔盐堆堆芯物理参数计算

针对中国科学院设计的2 MW固态钍基熔盐堆(TMSR-SF)堆芯,采用蒙特卡罗程序MCNP精确描述堆芯TRISO包覆燃料颗粒、燃料球排布,建立了包含燃料元件、熔盐冷却剂、石墨反射层、中心石墨通道、控制棒及反射层通道的三维全堆芯模型,计算了TMSR-SF初始有效增殖因数、中子能谱、功率分布、控制系统价值、停堆裕量、反应性系数、中子动力学参数等堆芯物理参数,为TMSR-SF的物理优化及热工安全分析提供必要的参数。     

基于径向基代理模型与小生境遗传算法的铅铋反应堆堆芯智能优化

为解决铅铋反应堆多因素耦合影响下的复杂非线性多维优化问题,构建了基于径向基（RBF）代理模型预测、正交拉丁超立方抽样（OLHS）和小生境遗传算法（NGA）寻优的堆芯智能优化方法,开发了包含抽样、蒙卡程序耦合处理、堆芯参数预测寻优等功能的铅铋反应堆设计优化平台,并以堆芯最小燃料装载量为优化目标进行方案寻优验证。研究结果表明:RBF代理模型可准确快速地预测铅铋反应堆堆芯特性参数,与蒙卡程序计算值比较,其预测的堆芯有效增殖因子（k_eff）相对误差在±0.1%以内;该智能优化方法应用于铅铋反应堆堆芯优化是可行的,能找到多因素共同变化约束下的最优目标方案,且极大缩减了设计方案的搜索计算时间。本研究建立的堆芯智能优化方法可为铅铋反应堆多物理、多变量、多约束耦合影响的优化设计提供思路。      

自然循环反应堆流量分配优化研究

由于自然循环反应堆一回路产生的驱动力有限,回路循环总流量较小,因此堆芯流量分配设计与优化非常重要。合理的堆芯流量分配不仅能满足热工安全要求,还能直接提高堆芯的性能。基于以上原因,本文对自然循环反应堆流量分配优化问题进行了初步研究,对闭式并联通道,采用一维流动传热模型,建立了入口阻力系数优化初值求解模型并设计了精确解搜索算法,并耦合堆芯热工分析程序COBRA编写了相应的堆芯流量分配优化程序。选择一自然循环反应堆算例,采用该程序对堆芯寿期内的流量分配优化进行了计算和分析。结果表明,将各典型寿期节点流量分配优化得到的入口阻力优化设置方案取平均值,可获得相对整个循环寿期达到较好优化效果的入口阻力设置方案。针对取平均值这种人工设计方法难以获得全局最优解的缺点,参考现代优化计算方法,提出了一种自动实现循环寿期内流量分配最优化的方法。     

Low Radioactivity Fusion Reactor Based on the Spherical Tokamak with a Strong Magnetic Field

The most important feature of the spherical tokamak is the possibility of high-β plasma confinement (β is the ratio of plasma pressure to magnetic field pressure). So, spherical tokamak can be considered as a possible confinement system for D–³He fusion reactor. Present paper study the ability to develop powerful D–³He reactor based on a spherical tokamak (fusion power about 3 GW). The following parameters are considered as optimization criteria: (1) the ratio of confinement time to the value predicted by ITER98y2 scaling; (2) the neutron flux from the plasma.     

Multiobjective fuel management optimization for self-fuel-providing LMFBR using genetic algorithms

One of the conceptual options under consideration for the future of nuclear power is the long-term development without fuel reprocessing. This concept is based on a reactor that requires no plutonium reprocessing for itself, and provides high efficiency of natural uranium utilization, so called Self-Fuel-Providing LMFBR (SFPR). Several design considerations were previously given to this reactor type which, however, suffer from some problems connected with insufficient power flattening, large reactivity swings during burnup cycles, and peak fuel burnup being significantly higher than recent technology experience, which is about 18% for U-10 wt%Zr metallic fuel to be considered. Yet, the mentioned core parameters demonstrate high sensitivity to the fuel management strategy selected for the reactor. Therefore, the aim of this study is to develop a practical tool for the improvement of the core characteristics by fuel management optimization, which is based on advanced optimization techniques such as Genetic Algorithms (GA). The calculation results obtained by a simplified reactor model can serve as estimates of achievable values for mentioned core parameters, which are necessary to make decisions at the preliminary optimization stage.     

Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors

To successfully carry out material irradiation experiments and radioisotope productions, a high thermal neutron flux at irradiation box over a desired life time of a core configuration is needed. On the other hand, reactor safety and operational constraints must be preserved during core configuration selection. Two main objectives and two safety and operational constraints are suggested to optimize reactor core configuration design. Suggested parameters and conditions are considered as two separate fitness functions composed of two main objectives and two penalty functions. This is a constrained and combinatorial type of a multi-objective optimization problem. In this paper, a fast and effective hybrid artificial intelligence algorithm is introduced and developed to reach a Pareto optimal set. The hybrid algorithm is composed of a fast and elitist multi-objective genetic algorithm (GA) and a fast fitness function evaluating system based on the cascade feed forward artificial neural networks (ANNs). A specific GA representation of core configuration and also special GA operators are introduced and used to overcome the combinatorial constraints of this optimization problem. A software package (Core Pattern Calculator 1) is developed to prepare and reform required data for ANNs training and also to revise the optimization results. Some practical test parameters and conditions are suggested to adjust main parameters of the hybrid algorithm. Results show that introduced ANNs can be trained and estimate selected core parameters of a research reactor very quickly. It improves effectively optimization process. Final optimization results show that a uniform and dense diversity of Pareto fronts are gained over a wide range of fitness function values. To take a more careful selection of Pareto optimal solutions, a revision system is introduced and used. The revision of gained Pareto optimal set is performed by using developed software package. Also some secondary operational and safety terms are suggested to help for final trade-off. Results show that the selected benchmark case study is dominated by gained Pareto fronts according to the main objectives while safety and operational constraints are preserved.     

Estimation of research reactor core parameters using cascade feed forward artificial neural networks

The pattern of the core reload program is very important for an optimize use of research reactors. Reactor safety issues and economic efficiency should be considered during pattern studies. In order to find the best core pattern for a research reactor, its reloading program should be solved as a multi-objective and constrained optimization problem. If considered objective functions of the optimization problem can be estimated in very short time, the optimal fuel reloading pattern can be used effectively. In this research a very fast estimation system for suggested core parameters has been developed using cascade feed-forward type of artificial neural networks (ANNs). Four main core parameters are suggested to optimize reactor core adequately. And also to get larger thermal fluxes in the desired irradiation box, a new flexible method was selected. A Software package has been developed to prepare and reform required data for ANNs training. The gradient descent method with momentum weight/bias learning rule has been used to train ANNs. To get the best conditions for considered ANNs training a vast study has been performed. It includes the effects of variation of hidden neurons, hidden layers, activation functions, learning and momentum coefficients, and also the number of training data sets on the training and simulation results. Some experimental convergence criteria are used to study them. A comparison selection rule has been used to adjust desirable conditions. Final training and simulation results show that developed ANNs can be trained and estimate suggested core parameters of research reactors very quickly. It improves effectively pattern optimization process of core reload program.     

The optimum design of tight lattice reactor core with thin rod bundles

The fuel height, rod diameter, pitch, and the loading pattern are all important parameters in the reactor core design process. Based on the analysis of the core performance, optimization calculation is performed on the three objective functions of ABV-6M reactor, i.e., power density, coolant temperature difference between the inlet and outlet, and flow-induced vibration are proposed for optimization calculation. Then a multi-objective problem (MOP) model is applied and computed optimally by non-dominated sorting genetic algorithm (NSGA-II) with the aim of maximizing power density and temperature difference as well as minimizing the flow-induced vibration. The results of optimal designs called ‘Pareto-optimal solutions’ are a set of multiple optimum solutions, from which the final optimization can be chosen after sensitivity analysis is performed. On the basis of lattice parameters optimization, the radial one-dimensional fuel loading pattern was optimized for achieving the optimum fuel utilization. The typical optimum design considered to be safe in a verification check showed that tight lattice effectively improved the reactor performances and saved the fuel consumption.     

自然循环反应堆全寿期流量分配多目标优化研究

堆芯流量分配设计是自然循环反应堆堆芯结构优化的重点内容,对提升堆芯经济性和安全性具有重要意义。基于反应堆闭式并联多通道模型构建了局部最优流量分配计算模型,并对现有的流量分配方案进行分析,针对其局限性,提出了一种基于最佳时区的多目标综合评价法,可实现反应堆全寿期多目标流量分配优化计算;根据所提出的理论,结合TOPSIS综合评价法,以自然循环下最大输出功率、反应堆寿期内出口最大温差以及最大温差随时间变化标准偏差为属性值,开展小型长寿命自然循环铅铋快堆SPALLER-100的堆芯流量分配方案优化研究。研究结果表明,基于运行时间为3182 d功率分布所得SPALLER-100反应堆堆芯流量分配方案最佳,与基于寿期初功率分布所得流量分配方案相比,所得方案堆芯出口最大温差降低30 K,堆芯出口最大温差随时间变化的标准偏差降低41%,反应堆自然循环最大输出功率提高2.35%。      

热管冷却双模式空间堆堆芯瞬态安全特性分析

为研究热管冷却双模式空间堆(HP-BSNR)概念设计的可行性和推进模式下堆芯瞬态安全特性,本文基于堆芯结构和稳态程序计算的初始参数分布,建立了堆芯数学物理模型,并开发了适用于HP-BSNR的瞬态安全分析程序TTHA_HPBSNR,计算了HP-BSNR在推进模式下反应性引入和堆芯失流等不同瞬态事故工况下的安全特性,同时分析了反应堆关键参数对HP-BSNR堆芯瞬态安全特性的影响。结果表明,由于堆芯固有负反馈机制的作用,发生反应性引入事故时,堆芯功率最终达到一新的稳定值,且燃料最高温度并未超出安全限值。而发生失流事故时,反应堆能实现自动停堆,且负反馈系数的大小决定了自动停堆的响应时间。相较于反应性引入事故,失流事故对HP-BSNR的安全运行威胁更大。     

Some studies on differential evolution variants for application to nuclear reactor core design

During the design of nuclear reactor cores, reactor cell parameters such as dimensions, enrichment and materials must be adjusted considering restrictions such as the average thermal flux, criticality and sub-moderation. Due to the complexity of this problem, trial-and-error methods do not necessarily guarantee acceptable solutions. Thus, the necessity of formulating the design of a nuclear reactor core as an optimization problem, where the decision variables are optimized taking constraints into account. Once the problem is formulated in such way, global search methods may be used in order to generate candidate solutions, that is, sets of core design parameters, to be analyzed by programs that simulate the neutron interactions in the reactor core. The Differential Evolution (DE) algorithm is one of those state-of-art global search methods, whose application has outperformed competitive algorithms in several optimization problems including the optimization problem of nuclear core design. Modifications in the DE such as the Trigonometric Mutation Operator and the Opposition-based learning have been reported in the literature, with considerable advances in the results of many problems. This performance motivated us to apply these two variants of differential evolution to the nuclear core design problem, which is the aim of this paper. The results are promising, demonstrating that the improvements are applicable in nuclear science and engineering applications.