Parameterless evolutionary algorithm applied to the nuclear reload problem

In this work, an evolutionary algorithm with no parameters called FPBIL (parameter free PBIL) is developed based on PBIL (population-based incremental learning). Moreover, the analysis reveals how the parameters from PBIL can be replaced by self-adaptable mechanisms which appear from the radically different form by which the evolution is processed. Despite the advantages, the FPBIL reveals itself compact and relatively modest in the use of computational resources. The FPBIL is then applied to the nuclear reload problem. The experimental results observed are compared to those of other works and corroborate to affirm the superiority of the new algorithm.     

Multiprocessor modeling of parallel Particle Swarm Optimization applied to nuclear engineering problems

Particle Swarm Optimization (PSO) is a population-based metaheuristic (PBM), in which solution candidates evolve through simulation of a simplified social adaptation model. Putting together robustness, efficiency and simplicity, PSO has gained great popularity. Many successful applications of PSO are reported, in which PSO demonstrated to have advantages over other well-established PBM. However, computational costs are still a great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time consuming objective functions. To overcome such difficulty, parallel computation has been used. The default advantage of parallel PSO (PPSO) is the reduction of computational time. Master-slave approaches, exploring this characteristic are the most investigated. However, much more should be expected. It is known that PSO may be improved by more elaborated neighborhood topologies. Hence, in this work, we develop several different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by communication strategies in multiprocessor architectures. The proposed PPSOs have been applied to two complex and time consuming nuclear engineering problems: i) reactor core design (CD) and ii) fuel reload (FR) optimization. After exhaustive experiments, it has been concluded that: i) PPSO still improves solutions after many thousands of iterations, making prohibitive the efficient use of serial (non-parallel) PSO in such kind of real-world problems and ii) PPSO with more elaborated communication strategies demonstrated to be more efficient and robust than the master-slave model. Advantages and peculiarities of each model are carefully discussed in this work.     

QACO_Alpha applied to the nuclear reactor core fuel reload optimization

The nuclear reload optimization is an important issue to nuclear engineering. It consists on maximizing the length of the operation cycle of the power plant. The aim is to find a configuration of the fresh fuel assemblies and remnants in order to keep the power plant running at full power by the largest time as possible.Quantum-inspired evolutionary algorithms are optimization tools based in artificial intelligence developed to simulate the quantum processing in classical computers. In this work is introduced one of these tools, which adds quantum concepts to the biological metaphor of collective learning of the real ants, named QACO_Alpha. It uses mechanisms developed in order to avoid premature convergence problems like a pheromone evaporation step besides a new updating method.To show its effectiveness, QACO_Alpha was applied to the optimization of 7th cycle of Angra 1. Experimental results were confronted to that obtained with other optimization methods, qualifying QACO_Alpha as a valid optimization tool for this kind of problem.     

Multistart Simulated Annealing applied to a Reduced Scale Thermo-Hydraulic Loop of a Pressurized Water Reactor core

In this paper we investigate the reduced scale design of a third generation Pressurized Water Reactor core, with single phase flow under natural circulation, based on the Loss-of-Fluid Test facility. Recent works approach this issue applying metaheuristics such as Genetic Algorithms and Particle Swarm Optimization. Both approaches have, as a drawback, the high computational time to obtain an acceptable solution. Here, we propose an alternative method when computational time is critical. We approach the problem applying a Multistart Simulated Annealing method in order to obtain an acceptable solution in a lower computational time. Our results indicate a 98.7% computational time improvement over the state-of-the-art Particle Swarm Optimization method. Moreover, the Multistart Simulated Annealing results are 1.36% better than state-of-the-art Particle Swarm Optimization method. Thus, Multistart SA shows promising results and is a suitable method when time is critical.     

含可燃毒物的压水堆堆芯装料优化

含可燃毒物的压水堆堆芯装料优化是燃料管理优化研究中的难点。应用通常的优化算法效率低、全局性差,特征统计算法更适合求解该优化问题。本研究克服了原特征统计算法装料优化将组件布置(LP)优化和新组件可燃毒物配置(BP)优化脱耦处理的缺陷,对LP和BP同时进行优化,结合堆芯分析程序CYCLE2D,成功地研制了压水堆LP和BP耦合优化程序CSALPBP。用该程序对大亚湾2号机组第10循环进行了堆芯装料优化计算。结果表明：CSALPBP程序具有很高的搜索效率和很好的全局性。     

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.     

模拟退火算法在压水堆堆芯换料优化中的应用研究

针对核反应堆堆芯换料优化中的具体问题，提出了新的编码解码方法，成功地应用了模拟退火算法。以先进核反应堆为例，进行了模拟退火算法优化计算，并与遗传算法的优化结果进行了比较。结果表明．模拟退火算法和遗传算法对反应堆换料优化问题同样有效。     

压水堆堆芯可燃毒物装载的优化计算

成功地将遗传算法和禁忌搜索算法应用于核反应堆堆芯可燃毒物装载优化研究。以大亚湾核电厂堆芯第10循环为例,利用遗传算法对硼玻璃、Gd2O3和IFBA三种常用的可燃毒物进行了优化计算。结果表明,遗传算法对反应堆堆芯可燃毒物装载优化问题是有效的．IFBA可燃毒物的优化结果最好。最后应用由遗传算法和禁忌搜索算法混合而成的杂交优化策略进行了优化计算．该方法可大大提高优化速度。     

Optimization of nuclear reactor core fuel reload using the new Quantum PBIL

An issue of great interest in nuclear engineering is to optimize the reload of fuel assemblies in the reactor core, which means to find the best configuration of shuffling between the fresh fuel and the remnants ones from previous cycles.Quantum inspired evolutionary algorithms were developed as an alternative to make the conventional evolutionary algorithms more efficient regarding future hardware implementations. This paper presents a new quantum inspired evolutionary algorithm, named Quantum PBIL (QPBIL). It combines the basic concepts of Population-Based Incremental Learning (PBIL) with the concepts of quantum computing as quantum bit and the linear superposition of states used in evolutionary algorithms with quantum inspirations.To prove its effectiveness as an optimization tool, QPBIL was applied to the optimization of cycle 7 of Angra 1, and the results obtained were comparable to those of efficient optimization techniques based on artificial intelligence currently available.     

压水反应堆负荷跟踪的最优控制

按照最优控制理论 ,把压水反应堆的负荷跟踪控制问题描述为一个非线性二次型最优控制问题。反应堆芯部模型采用了一维分布参数模型。为求解这个最优控制问题 ,提出了一个行之有效的最优控制算法DDPSR。研究结果表明 ,DDPSR不仅能在大的优化区间收敛 ,而且迭代次数极少 ,速度相当快 ;大型压水堆能够以最优方式负荷跟踪运行 ,且轴向偏移 (AO)始终满足工程实际要求 ,此外还对硼的控制特性进行了特别的讨论     

压水反应堆蒸汽发生器泄漏监测系统优化研究

为了优化蒸汽发生器泄漏监测系统性能,分析了放射性物质在二回路和蒸汽发生器内的迁移规律,建立了蒸汽发生器传热管发生破损后炉水和主蒸汽管道内放射性核素的计算模型,获得了利用排污水和主蒸汽管道两测点数据计算蒸汽发生器传热管破损部位和泄漏率的方法,并利用蒙特卡罗法仿真排污水γ能谱,确定了排污水放射性探测器的类型。     

线性化模型在压水堆堆芯系统稳定性分析中的应用

对压水堆堆芯系统动力学的非线性模型进行微小摄动下的线性化处理,得到了线性时不变系统模型。对该模型进行了仿真实验验证,表明该模型在小扰动情况下能够很好地近似非线性系统。基于李雅普诺夫第一方法,利用该模型对压水堆堆芯系统进行稳定性分析,得到了令人满意的结果。     

压水堆核电站锆水反应微观机理

压水堆核电站严重事故下的氢气行为研究需建立氢气生成的动力学模型,氢气生成反应的微观机理和反应速率常数是建立动力学模型的基础。本工作采用量子化学理论,应用量子化学软件包Gaussian03,在HF/3-21G理论模型上研究了压水堆严重事故下锆水反应的微观机理,并计算了反应速率常数。计算结果表明,锆水反应是由4个基元反应组成的总包反应。第2步基元反应的正反应速率最小,是锆水反应的速控步。在微观上研究减少或控制氢气生成的措施应从第2步基元反应入手。文中的计算结果偏于保守,以该方法建立起的动力学模型模拟压水堆核电站严重事故下的氢气行为是安全的。     

基于PSA的压水堆LBLOCA不确定性分析

为了结合确定论与概率论分析开展更加真实的核反应堆事故工况安全分析,提出了一种结合概率安全分析（PSA）和最佳估算加不确定性（BEPU）分析的方法,并以典型三环路压水堆冷管段双端断裂大破口失水事故（LBLOCA）的极限事故为对象,首先基于PSA开展了应急堆芯冷却系统的事故失效分析,而后结合BEPU分析评估了事件树中各事故序列的包壳峰值温度（PCT）分布及条件堆芯损坏概率（CCDP）,最终确定了压水堆在该事故工况中的堆芯损坏频率（CDF）。分析结果表明,压水堆在冷管段双端断裂工况中应急堆芯冷却系统能够保证反应堆的安全,且一列低压安注系统足以排出堆芯余热及保证反应堆安全。      

先进压水堆燃料组件计算不确定性分析

采用广义微扰理论,研究核数据不确定性对先进压水堆AP1000燃料组件宏观截面参数计算不确定性的贡献与影响机理。通过比较、分析不同因素对组件参数计算不确定性的贡献,给出组件宏观截面参数相关系数矩阵;采用敏感性分析方法及分步比较的思路研究在不同堆芯运行状态下核数据对AP1000燃料组件宏观参数计算不确定性贡献的机理。研究结果表明:核数据自身不确定性通过组件输运计算最终传递给宏观截面参数的不确定性是基本恒定的。其中,~(235)U平均裂变中子数反应、~(238)U辐射俘获反应、~(238)U共振非弹性散射反应及~1H共振弹性散射反应对组件宏观截面参数计算不确定性贡献尤为突出。同时,温度升高导致组件kinf及宏观截面参数计算不确定性增加;燃料富集度降低及可燃毒物的存在均使组件kinf计算不确定性增加;组件快群截面计算不确定性远大于热群截面计算不确定性。其中~(238)U辐射俘获反应、共振非弹性散射反应等截面信息应重点关注并且需要进一步评价和改进。     

Optimization of fuel core loading pattern design in a VVER nuclear power reactors using Particle Swarm Optimization (PSO)

The two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor (K_eff) in order to extract the maximum energy, and keeping the local power peaking factor (P_q) lower than a predetermined value to maintain fuel integrity. In this research, a new strategy based on Particle Swarm Optimization (PSO) algorithm has been developed to optimize the fuel core loading pattern in a typical VVER. The PSO algorithm presents a simple social model by inspiration from bird collective behavior in finding food. A modified version of PSO algorithm for discrete variables has been developed and implemented successfully for the multi-objective optimization of fuel loading pattern design with constraints of keeping P_q lower than a predetermined value and maximizing K_eff. This strategy has been accomplished using WIMSD and CITATION calculation codes. Simulation results show that this algorithm can help in the acquisition of a new pattern without contravention of the constraints.     

基于粒子群优化算法的压水堆换料优化初步研究

基于大亚湾核电站压水堆堆芯燃料组件布置建立优化的数学模型,采用离散的多目标粒子群优化算法和基于有限元法的堆芯计算软件"Donjon",及组件计算软件"Dragon",将粒子群程序与堆芯物理计算软件结合,编制完整的堆芯燃料管理优化程序。应用此优化程序对大亚湾核电站初装料进行优化的结果表明:粒子群优化算法得到的优化方案与参考方案相比,keff增加11.9%,且功率峰因子低于1.4,满足安全限值。     

Damage evaluation of 500 MWe Indian Pressurized Heavy Water Reactor nuclear containment for aircraft impact

Safety assessment of Indian nuclear containments has been carried out for aircraft impact. The loading time history for Boeing and Airbus categories of aircrafts is generated based on the principle of momentum transfer of crushable aircrafts. The case studies include the analysis of BWR Mark III containment as a benchmark problem and analyses of Pressurised Heavy Water Reactor containment (inner and outer containment) for impulsive loading due to aircraft impact. Initially, the load is applied on outer containment wall model and subsequently the load is transferred to inner containment after the local perforation of the outer containment wall is noticed in the transient simulation. The analysis methodology evolved in the present work would be useful for studying the behavior of double containment walls and multi barrier structural configurations for aircraft impact with higher energies. The present analysis illustrates that with the provision of double containments for Indian nuclear power plants, adequate reserve strength is available for the case of an extremely low probability event of missile impact generated due to the commercial aircrafts operated in India.     

压水堆核电站铁水反应机理研究

从微观上研究压水堆核电站严重事故下减少或控制氢气生成的措施需研究氢气生成的微观机理。本工作采用量子化学理论,应用量子化学软件包Gaussian03,在B3LYP/6-311+G(d)理论模型上研究了压水堆严重事故下铁水反应的微观机理,并计算了活化能。结果表明,铁水反应是由两个基元反应组成的总包反应。第2步基元反应的正反应活化能较大,是铁水反应的速控步。在微观上研究减少或控制氢气生成的措施应从第2步基元反应入手。