NUMERICAL SIMULATION OF SINGLE-GROUP, STEADY STATE AND ISOTROPIC NEUTRON TRANSPORT EQUATION IN DIFFUSIVE REGIMES

The single-group,steadystate,isotropic for mofthe neutron transport equationis given by[1]Ω·+σtI-σsPψ(x,Ω)=q(x,Ω)(x,Ω)∈D×Sψ(x,Ω)=g(x,Ω)x∈Din={x∈D,γ(x)·Ω<0(1)whereσtis the total cross section,σSis the scatteringcross section,andψ(x,Ω)is the angular flux to bedeter mined for all pointsx∈D,D Rn(n=2,3)and all possible travel directionsΩ,ΩS(Sis a u-nit disk or a unit sphere),γ(x)denotes the out wardunit nor mal atx∈D,Idenotes the identity opera-tor,the operator…     

一类阻尼陀螺系统特征值反问题

本文考虑一类阻尼陀螺系统特征值反问题。在假定分析质量矩阵是精确的情况下,运用测量的不完全复模态数据给出了反问题有解的充分必要条件及解的显式表示。在此基础上研究了一个逼近问题,得到了Frobenius范数意义下的最优矩阵。     

Modeling techniques for periodic vehicle routing problems

This paper presents a continuous approximation model for the period vehicle routing problem with service choice (PVRP-SC). The PVRP-SC is a variant of the period vehicle routing problem in which the visit frequency to nodes is a decision of the model. This variation can result in more efficient vehicle tours and/or greater service benefit to customers. We present a continuous approximation model to facilitate strategic and tactical planning of periodic distribution systems and evaluate the value of service choice. Further, results from the continuous model can provide guidelines for constructing solutions to the discrete PVRP-SC.     

c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation

The simultaneous perturbation stochastic approximation (SPSA) algorithm has been used in the literature for the solution of the dynamic origin–destination (OD) estimation problem. Its main advantage is that it allows quite general formulations of the problem that can include a wide range of sensor measurements. While SPSA is relatively simple to implement, its performance depends on a set of parameters that need to be properly determined. As a result, especially in cases where the gradient of the objective function changes quickly, SPSA may not be as stable and even diverge. A modification of the SPSA algorithm, referred to as c-SPSA, is proposed which applies the simultaneous perturbation approximation of the gradient within a small number of carefully constructed “homogeneous” clusters one at a time, as opposed to all elements at once. The paper establishes the theoretical properties of the new algorithm with an upper bound for the bias of the gradient estimate and shows that it is lower than the corresponding SPSA bias. It also proposes a systematic approach, based on the k-means algorithm, to identify appropriate clusters. The performance of c-SPSA, with alternative implementation strategies, is evaluated in the context of estimating OD flows in an actual urban network. The results demonstrate the efficiency of the proposed c-SPSA algorithm in finding better OD estimates and achieve faster convergence and more robust performance compared to SPSA with fewer overall number of function evaluations.     

Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization

Simultaneous Perturbation Stochastic Approximation (SPSA) has gained favor as an efficient optimization method for calibrating computationally intensive, “black box” traffic flow simulations. Few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but at a very slow rate. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, have limited time to reach a high-quality solution. The existing literature indicates that SPSA provides the potential for upgrading both off-line and on-line solutions alike, by delivering high-quality solutions within seconds. This article describes an extensive set of optimization tests involving SPSA and genetic algorithms (GAs). The final results suggest that GA was slightly more efficient than SPSA. Moreover, the results suggest today's signal timing solutions could be improved significantly by incorporating GA, SPSA, and “playbooks” of preoptimized starting points. However, it may take another 5–10 years before our computers become fast enough to simultaneously optimize coordination settings (i.e., cycle length, phasing sequence, and offsets) at numerous intersections, using the most powerful heuristic methods, at speeds that are compatible with real-time adaptive solutions.     

Hunting stability analysis of high-speed train bogie under the frame lateral vibration active control

In this paper, we study a multi-objective optimal design of three different frame vibration control configurations and compare their performances in improving the lateral stability of a high-speed train bogie. The existence of the time-delay in the control system and its impact on the bogie hunting stability are also investigated. The continuous time approximation method is used to approximate the time-delay system dynamics and then the root locus curves of the system before and after applying control are depicted. The analysis results show that the three control cases could improve the bogie hunting stability effectively. But the root locus of low- frequency hunting mode of bogie which determinates the system critical speed is different, thus affecting the system stability with the increasing of speed. Based on the stability analysis at different bogie dynamics parameters, the robustness of the control case (1) is the strongest. However, the case (2) is more suitable for the dynamic performance requirements of bogie. For the case (1), the time-delay over 10?ms may lead to instability of the control system which will affect the bogie hunting stability seriously. For the case (2) and (3), the increasing time-delay reduces the hunting stability gradually over the high-speed range. At a certain speed, such as 200?km/h, an appropriate time-delay is favourable to the bogie hunting stability. The mechanism is proposed according to the root locus analysis of time-delay system. At last, the nonlinear bifurcation characteristics of the bogie control system are studied by the numerical integration methods to verify the effects of these active control configurations and the delay on the bogie hunting stability.     

多学科设计优化方法用于船舶设计

为了提供更好地满足不断提高的军方和商业市场需求的产品.现代船舶设计需要获得真正的最优设计.它需要综合考虑所有学科以及它们之间的相互关系.船舶设计中涉及到的学科数目是如此众多,它们之间的相互关系是如此复杂以至于需要寻求一种新的设计技术来替代传统的设计螺旋方法.多学科设计优化(MDO)方法正是一种新的广泛应用于复杂工程系统的设计方法.在本文中,我们主要介绍了中国船舶科学研究中心将MDO应用于船舶设计的研究情况.我们应用iSlGHT软件集成美国弗吉尼亚大学公布的CGX设计模块来演示将MDO方法应用到船舶设计的整个过程,获得了比他们更优化的解.CGX模块也用来说明我们提出的子系统划分的概念.最后,基于我们的研究经验,我们也总结了一个在船舶设计中应用MDO方法的一般应包括的内容.     

多学科设计优化研究应用现状综述

多学科设计优化是一种通过充分探索和利用系统中的相互作用的协同机制来设计复杂系统工程和子系统的方法论。综述了多学科设计优化研究和应用领域的当前进展,并针对多学科设计优化发展的两大难题——计算代价和组织复杂性,介绍了多学科设计优化的主要内容。然后分别介绍了当前处理多学科设计优化的两种方法——试验设计和多学科优化算法。船舶和海洋工程结构物设计也是一个涉及到多个学科的系统工程,然而多学科设计优化的应用却很少,因此实现船舶与海洋工程结构设计的多学科设计优化也必将具有十分广阔的前景。     

基于关键客流密度选择支路公交运营模式的方法

公交支线采用灵活式运营模式,可以通过接驳点将所服务区与交通网连接起来,为乘客提供更有效快捷的运行方式.但在实际的设计和运营过程中,往往很难选择运用何种模式更合适.鉴于此,提出公交运营模式选择模型以协助决策者做出更合理的选择.采用连续近似方法估算关键需求密度,通过分析单车辆和双车辆情况下的运营参数,比较模拟生成的值,验证分析建模方法的有效性.     

多学科设计优化算法比较及其在船舶和海洋平台设计上的应用

多学科设计优化(Multidiseiplinary Design Optimization,简称MDO)是一种通过充分探索和利用系统中的相互作用的协同机制来设计复杂系统工程和子系统的方法论.多学科设计优化算法是其核心部分,也是研究最活跃的领域.文中首先介绍了MDO算法的定义、分类和发展,然后从算法的来源和目的、优化过程、优缺点、改进方法和应用情况等五个方面对四种基于分解技术的MDO算法进行了综述,进而对比了这四种算法的异同点.最后,针对船舶和海洋平台设计的具体特点,归纳了适合于船舶或海洋平台多学科设计优化的MDO算法所需要具备的特征,并建议使用基于近似模型的协同优化算法或BUSS 2000算法进行船舶和海洋平台的多学科设计优化.