基于VHTRC的棱柱式高温气冷堆核设计程序验证北大核心CSCD

高温气冷堆是国际公认的固有安全性高的反应堆堆型。针对高温气冷堆包覆颗粒燃料引入的燃料组件的双重非均匀性以及棱柱式堆芯布置的非均匀性和强空间耦合效应,提出基于蒙特卡罗均匀化-确定论输运方法的RMC-SaraGR程序系统作为棱柱式高温气冷堆的核设计程序。基于日本棱柱式高温气冷堆临界实验装置VHTRC基准题,针对此套核设计程序系统开展了均匀化模型研究和初步验证。研究结果表明,基于蒙特卡罗均匀化方法,采用全堆模型、合适的能群结构和分区方式产生组件群常数,并经过超级等效均匀化方法进行等效均匀化修正,可以保证堆芯多群均匀计算具有较高的计算精度。     

西安脉冲堆堆芯燃料管理正交优化计算

根据西安脉冲堆工程的实际需要,研制了脉冲堆堆芯燃料管理计算软件包HEX ICFM,并建立了正交优化模型,研制了换料优化计算软件包HEX ORTH.软件包中栅元计算采用了WIMS D 4程序,堆芯扩散计算程序采用了二维六角形节块程序SIXTUS 2.应用HEX ICFM对西安脉冲堆首循环堆芯参数进行了计算,并用HEX ORTH对第3循环末堆芯燃料装载和30根备用新燃料元件进行了堆芯优化分析,得到了目标函数为max(K_eff^BOC)的最佳堆芯倒换料方案.     

高温气冷堆堆芯球床的流动与换热分析

球床式高温气冷堆的堆芯堆积有大量的燃料球,具有多孔介质特征。本文利用计算流体力学商用软件ANSYS Fluent及其多孔介质模型模拟了球床内的流动与换热现象。通过高温气冷堆满功率稳态运行及失冷不失压事故工况的模拟,分析了球床的流场和温度场,并与高温气冷堆的热工设计计算结果对比,说明了计算流体力学软件及其多孔介质模型对于球床式高温气冷堆热工水力分析的适用性,为后续使用计算流体力学软件开展高温气冷堆的堆芯旁流等复杂热工水力学现象的分析奠定基础。     

高温气冷堆堆芯中子时空动力学模拟计算

研究开发三维圆柱几何堆芯多群中子时空动力学改进准静态方法模拟计算程序.对给定的模块式高温气冷堆模型进行模拟计算.初始状态下,计算结果与中子扩散程序CITATION吻合很好.动态情况下,模拟堆芯反应性、堆内各能群中子平均注量率和堆芯相对功率等物理量随时间变化,计算结果与理论分析一致.     

我国高温气冷堆技术及产业化发展

正一、前言1.高温气冷堆简介高温气冷堆属于热中子裂变反应堆,用氦气作冷却剂,石墨作慢化材料,采用包覆颗粒燃料以及全陶瓷的堆芯结构材料。模块式高温气冷堆安全性好、氦气堆芯出口温度高,是目前国际核能领域公认的新一代核能系统,在工艺供热、核能制氢、高效发电、空间电源甚至军用领域都有广泛的应用前景。高温气冷堆的高温特性和安全性能首先源于其独特的包覆颗粒燃料(图1)。目前压水堆使用的燃料由氧化铀陶瓷芯块和锆合金包壳组成,高温气冷     

球形托卡马克堆嬗变中子学计算的比较研究

基于对球形托卡马克（ST）聚变堆的研究，提出了ST聚变－嬗变堆的设计概念。运用一维输运燃耗计算程序BISON3.0进行了优化设计，确定了适合于嬗变少额锕系MA核素的堆芯等离子体参数、包层结构及合适的换料周期。在一维计算的基础上，运用二维中子学程序TWODANT进行了二维中子输运计算；结合TWODANT给出的中子通量，运用一维放射性计算程序FDKR进行了燃耗计算，并给出了有关的计算结果。     

高温堆球流模拟在堆物理中的应用

介绍了高温气冷球床反应堆物理计算中燃料元件流动特性模拟的方法,对10MW高温堆进行了计算,与未考虑堆芯中燃料曲线流动的简化计算结果进行了比较。     

气冷快堆概述

正一、前言与工作原理气冷快堆,英文名称为Gas Cooled Fast Reactor,简称为GFR,是一种高温气体冷却快谱反应堆。顾名思义,气冷快堆采用气体作为堆芯冷却剂。由于气体工质密度一般相对较低,对中子的慢化能力较弱,在不显著添加其他慢化剂的情况下,中子能谱为快谱。气体工质的慢化能力一般也弱于金属冷却剂,因此气冷快堆的中子能量要高于金属快堆,也就是说气冷快堆的中子能谱更"硬"。     

快堆中子学计算程序NECP-SARAX的改进与验证

NECP-SARAX是西安交通大学NECP团队开发的用于快中子反应堆的中子学程序系统。为准确处理快中子反应堆中中等质量核素散射共振以及非弹性散射导致的复杂的中子慢化效应,SARAX程序最初采用连续能量的蒙特卡罗方法计算中子能谱从而获得堆芯计算使用的有效多群截面。由于蒙特卡罗程序计算效率低,且在低能量段统计偏差较大,提出采用基于点截面的超细群方法计算中子慢化能谱,避免了蒙特卡罗方法产生参数时存在的缺陷。堆芯计算采用多群中子输运,通过优化简化几何建模,改进了程序的实用性。采用多种微扰方法计算堆芯各种反应性系数,提出了基于中子输运微扰理论的虚拟密度方法以计算堆内组件变形导致的反应性变化。在进行堆芯瞬态计算时,采用了点堆和改进准静态两种方法,可用于一般快堆和快谱ADS的典型事故分析。OECD发布的一系列快堆基准题测试表明,SARAX程序在快堆计算中具有良好的精度,达到了与国外著名快堆程序相当的水平。有效增殖因子与连续能量的蒙卡计算结果相比偏差在300 pcm以内。同时,由于引入了虚拟密度理论和三维时空动力学模型,程序功能更加完善,可以更好地满足快堆工程设计的需求。     

气冷微型堆可燃毒物研究北大核心CSCD

为分析气冷微型堆可燃毒物布置策略,分别建立长寿期(15 MW-20 a)、短寿期(5 MW-1 a)、较长寿期(5 MW-3~10 a)不换料堆芯模型,利用通用蒙卡程序,研究气冷堆中常用可燃毒物核素种类、可燃毒物布置方案对堆芯反应性、寿期等特性的影响。研究结果表明:长寿期堆芯中,整体型Er2O3可以有效控制堆芯剩余反应性,但在寿期末会造成一定的反应性惩罚;整体型B4C可以较好地控制堆芯剩余反应性,并在寿期末几乎不会造成反应性惩罚,通过分区布置还可以优化功率分布;分离型B4C可以使燃耗特性曲线在寿期初和寿期中变化很平坦。短寿期堆芯中,分离型Gd2O3毒物棒可以很好地控制剩余反应性且不会缩短堆芯寿期;常见的B4C布置方式并不合适,但B4C弥散在堆芯石墨内可以起到较好的毒物效果。较长寿期堆芯中,分离型Gd2O3毒物棒不仅可以有效控制剩余反应性,还可以保证堆芯具备仅依靠温度负反馈实现自动停堆的固有安全性。研究结果将为后续气冷微堆型号研发提供指导。     

Evolutionary computation and multimodal search: A good combination to tackle molecular diversity in the field of peptide design

Summary The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks. For example, EC is prone to focus a search in the first good region found. This is a problem not only because of the undesirable and automatic rejection of potentially good search space regions, but also because the found solution may be extremely difficult to synthesize chemically or may even be a false docking positive. In order to avoid rejecting potentially good solutions and to maximize the molecular diversity of the search, we have implemented evolutionary multimodal search techniques, as well as the molecular diversity metric needed by the multimodal algorithms to measure differences between various regions of the search space.     

基于Kriging代理模型的铅铋反应堆智能优化方法北大核心CSCD

铅铋反应堆广泛应用的需求要求研究人员在现有堆芯方案的基础上开展大量优化设计工作。针对铅铋反应堆多物理、多变量、多约束耦合影响的多维非线性约束优化设计问题,基于Kriging代理模型、正交拉丁超立方抽样和SEUMRE空间搜索技术构建铅铋反应堆智能优化方法,耦合物理蒙卡计算/热工分析程序,开发包含抽样、耦合程序前后处理、反应堆优化分析功能的优化平台,并以铅铋反应堆SPALLER-4,URANUS为原型分别开展最小燃料装载量的方案寻优与参数优化验证。验证结果表明,该智能优化方法用于铅铋反应堆设计方案寻优和堆芯参数优化可行、有效,相比传统蒙卡程序计算寻优,在保证预测精度前提下极大地降低了计算成本,与URANUS初始模型比较,燃料装载量、堆芯总质量、活性区体积、堆芯总体积分别优化10.8%,11.5%,18.1%,17.1%,为基于代理模型的智能优化方法应用于铅铋反应堆的优化设计提供参考。     

高温气冷堆堆芯球床流动与温度分布均匀性研究

由于燃料球的随机分布和球床的壁面效应,球床式高温气冷堆堆芯孔隙率分布会有一定的不均匀性。深入认识壁面漏流、随机孔隙率对球床温度分布均匀性的影响对进一步提高高温气冷堆冷却剂出口温度及其安全性具有重要意义。本文采用多孔介质模型实现了对堆芯球床壁面漏流、随机孔隙率效应的数值模拟。结果表明,由于壁面漏流效应,壁面附近局部区域冷却剂最大速度会比中心高50%,对球床温度影响则不大。中心区域局部极小、极大孔隙率只对很小区域内流速和温度有影响,但温度变化幅值很小。球床中心随机孔隙率使冷却剂速度波动小于13%,对球床温度影响很小。     

Application of the edge of chaos in combinatorial optimization

Many problems in science, engineering and real life are related to the combinatorial optimization. However, many combinatorial optimization problems belong to a class of the NP-hard problems, and their globally optimal solutions are usually difficult to solve. Therefore, great attention has been attracted to the algorithms of searching the globally optimal solution or near-optimal solution for the combinatorial optimization problems. As a typical combinatorial optimization problem, the traveling salesman problem(TSP) often serves as a touchstone for novel approaches. It has been found that natural systems, particularly brain nervous systems, work at the critical region between order and disorder, namely,on the edge of chaos. In this work, an algorithm for the combinatorial optimization problems is proposed based on the neural networks on the edge of chaos(ECNN). The algorithm is then applied to TSPs of 10 cities, 21 cities, 48 cities and 70 cities. The results show that ECNN algorithm has strong ability to drive the networks away from local minimums.Compared with the transiently chaotic neural network(TCNN), the stochastic chaotic neural network(SCNN) algorithms and other optimization algorithms, much higher rates of globally optimal solutions and near-optimal solutions are obtained with ECNN algorithm. To conclude, our algorithm provides an effective way for solving the combinatorial optimization problems.     

Modeling and optimization of non-periodic grating couplers

The higher degree of freedom available for non-periodic gratings (as compared with their periodic counterparts) is investigated. These non-periodic structures may be employed to design novel light couplers with increased functionality. Optimizing such devices requires a complex search in a huge parameter space. The success in the solution of this task depends on the availability of a fast forward solver and a reliable search algorithm. Here, a fast forward solver based on the multiple multipole (MMP) method together with a near-to-far field transformation and a multiple scattering calculation is presented. Thanks to the efficiency of our approach, non-periodic gratings are evaluated with a speed comparable to commonly used periodic grating approximations. This allows our solver to be combined with a heuristic global search scheme, namely an evolutionary algorithm. The procedure is demonstrated with the optimization of a non-periodic grating output coupler that suppresses an unwanted second diffracted order.     

A novel power allocation strategy for cooperative PDMA systems

Cooperative communication technology is of great importance for increasing the user reachable rate, further improving throughput and reducing the outage probability of non-orthogonal multiple access (NOMA) systems. This paper mainly studies the power allocation optimization method based on amplify-and-forward (AF) pattern division multiple access (PDMA) to obtain the maximum achievable throughput. We formulate an optimization problem of user power allocation in a downlink PDMA system with cooperative relaying, the exact expressions of system throughput and user outage probability of the AF-PDMA system are derived, and a novel power allocation optimization method based on uniform distribution and restricted constraints is proposed. The effectiveness of the restricted constraints and optimization method is verified by theoretical analysis and simulation. The studies we have performed showed that the proposed scheme with uniform distribution and restricted constraints can be significantly improved in terms of the system throughput in comparison to the case with a genetic algorithm (GA) and fixed power allocation scheme. Concerning the proposed method, the search space is reduced to 1/3 of the original feasible region, and the runtime of the algorithm accounts for only 20% of the GA runtime.     

Unsupervised neural networks for solving Troesch’s problem

In this study, stochastic computational intelligence techniques are presented for the solution of Troesch’s boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm(GA) and pattern search(PS) techniques are used as the global search methods and the interior point method(IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM(GA-IPM) and PS hybridized with IPM(PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs.     

Directed Dominating Set Problem Studied by Cavity Method: Warning Propagation and Population Dynamics

The minimal dominating set for a digraph (directed graph) is a prototypical hard combinatorial optimization problem. In a previous paper, we studied this problem using the cavity method. Although we found a solution for a given graph that gives very good estimate of the minimal dominating size, we further developed the one step replica symmetry breaking theory to determine the ground state energy of the undirected minimal dominating set problem. The solution space for the undirected minimal dominating set problem exhibits both condensation transition and cluster transition on regular random graphs. We also developed the zero temperature survey propagation algorithm on undirected Erdös-Rényi graphs to find the ground state energy. In this paper we continue to develope the one step replica symmetry breaking theory to find the ground state energy for the directed minimal dominating set problem. We find the following. (i) The warning propagation equation can not converge when the connectivity is greater than the core percolation threshold value of 3.704. Positive edges have two types warning, but the negative edges have one. (ii) We determine the ground state energy and the transition point of the Erdös-Rényi random graph. (iii) The survey propagation decimation algorithm has good results comparable with the belief propagation decimation algorithm.     

连续面型高效率光栅分束器件的设计

阐述了用虚源法设计连续面型光栅分束器件的原理 ,提出了一种局部搜索遗传算法 ,并将其用于优化器件的性能。局部搜索遗传算法结合了局部搜索算法和遗传算法的优点 ,可以有效地克服遗传算法的“早熟收敛”现象 ,具有更强的全局收敛能力。用文中给出的方法可以得到具有较好均匀性的、高衍射效率的连续面型光栅分束器件。