基于混合狼群算法参数优选的泵站群运行优化

对于求解复杂的并联泵站群优化运行模型,狼群算法(wolf pack algorithm,WPA)存在收敛性和鲁棒性差等问题。为改善这些问题,该文以某典型并联泵站群为例,以泵站系统主机组运行能耗最低为优化目标,考虑流量、叶片角度、开机台数等约束条件,建立了并联泵站群优化运行模型。将模拟退火算法引入WPA算法中,提出混合狼群算法(hybrid wolf pack algorithm,HWPA)用于求解建立的优化模型。选择最小值、平均值和标准差作为算法性能的评价指标。相较于粒子群算法(particle swarm optimization,PSO)和WPA算法,HWPA算法求解典型并联泵站群优化运行模型得出的运行能耗最小值平均降低了15.60、10.23 kW,平均值平均降低了36.94、14.30 kW,标准差平均降低了84.82%、72.90%。在HWPA算法的基础上,对算法中的游走步长、奔袭步长、围攻步长的最小值和最大值4个参数进行单因素分析和拉丁超立方抽样设计计算,确定出4个参数的最优组合为0.33、1.53、0.672和4.8×10^5,进而提出改进混合狼群算法(improved hybrid wolf pack algorithm,IHWPA)。相较于HWPA算法,IHWPA算法求解典型并联泵站群优化运行模型得出的运行能耗最小值和平均值平均降低了4.66和13.26 kW,标准差平均降低了94.02%。应用IHWPA算法确定典型并联泵站群6个不同运行工况优化方案,结果表明,采用引入模拟退火算法、优选WPA算法参数的方法提高了算法的全局收敛性与计算鲁棒性,泵站运行最优决策方案较实际方案的运行能耗平均降低9.80%,可为泵站工程提供合理有效的运行方案,降低运行能耗。     

基于径向基神经网络的叶轮轴面投影图优化

为了提高余热排出泵的效率,采用拉丁超立方试验设计方法对叶轮轴面投影图上的前盖板圆弧半径、后盖板圆弧半径、前盖板倾角和后盖板倾角4个几何变量进行35组叶轮方案设计,应用ANSYS CFX 14.5软件对余热排出泵进行定常数值模拟,得到设计工况下的效率,应用径向基神经网络建立效率与轴面投影图的4个几何变量之间的近似模型,最后采用遗传算法对近似模型进行极值寻优,获得最优的轴面投影图几何参数组合。研究结果表明:对比原始泵的数值模拟性能曲线和试验外特性能曲线,两者吻合较好;径向基神经网络能较好地预测泵设计点效率;优化的轴面投影图使得余热排出泵的水力效率提高了6.18个百分点,改善了叶轮内流场特性。因此,叶轮轴面投影图的优化设计方法是可行的。     

超立方体多计算机上数据并行算法性能研究

讨论了超立方体多计算机上的数据并行算法，着重分析通信参数对算法性能的影响。提出了一个计算模型，并以并行归并算法为例，对数据元素数目和处理机数目等设计参数进行性能评价。     

离心泵叶轮轴面图的全自动CFD优化

叶轮轴面图设计的好坏直接影响离心泵的效率。以轴面图上的前盖板圆弧半径R0和R1、前盖板倾角T1、后盖板圆弧半径R2、后盖板倾角T2等5个几何参数为自变量,以泵水力效率最大为目标,采用Isight集成Pro/E、Gambit和Fluent等软件,实现了对离心泵叶轮轴面图进行CFD全自动优化。优化样本数据设计采用最优拉丁方试验设计方法确定。采用该方法对一比转数为84.8的离心泵进行优化。结果表明：优化后的扬程比优化前提高了4.85%,且优化后的水力效率从83.20%增加到84.51%,提高了1.31个百分点。因此,建立的叶轮轴面图全自动CFD优化方法是可行有效的,可为离心泵的水力优化设计提供参考。     

叶轮轴面图参数对离心泵性能的影响

为了研究叶轮轴面图上前、后盖板圆弧半径和倾角对离心泵性能的影响,选择一台比转数为84.8的离心泵双圆弧叶轮轴面图为研究对象,基于数值模拟计算结果,采用二阶响应面模型建立了前、后盖板圆弧半径和倾角与水力性能之间近似模型,其中24组数值试验方案由最优拉丁方方法确定。结果表明:前盖板倾角对设计工况下扬程、功率和效率的影响最大。该研究成果为离心泵叶轮轴面图的优化设计提供了依据。     

超立方体结构上的并行归并排序算法

This paper discusses the parallel merging sorting algorithm for hypercube architecture.Based on the analysis of communication complexity, the speedup of this algorithm is derived.     

Sampling Designs for Validating Digital Soil Maps: A Review

Sampling design (SD) plays a crucial role in providing reliable input for digital soil mapping (DSM) and increasing its efficiency. Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation. First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend:1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure; and 3) testing the feasibility and contribution of SDs in three-dimensional (3D) DSM with variability for multiple layers.     

Fault Tolerance Analysis of Regular Interconnection Multiprocessor System

The interconnection network used in a multiprocessor system provides an effective mechanism for the data exchange between the processors and is one of the dominating factors of performance of the system. The fault tolerance of an interconnection network can be measured by the probability that the network keeps functional in the presence of failures. The authors examine the fault tolerance of four regular interconnection networks (i.e. hypercube, crossed cube, Moius cube, and locally twisted cube) with the probabilistic approach. Experimental results show that all these interconnection networks display excellent fault tolerance. Moreover, the crossed cube enjoys the highest probabilistic of connectivity among them.     

基于特征代表性的土壤环境质量监测点布局优化方法

为提升土壤监测点代表性以更准确获取土壤信息并实施有效管理措施,本文提出一种基于特征代表性的土壤环境质量监测点布局优化方法。该方法基于多个与监测目标变量相关性强的辅助变量的属性分布构建特征空间,采用条件拉丁超立方体方法将特征空间分层并进行编码,并通过计算特征空间分层影响度以确定抽样顺序,逐点抽样优化获得高代表性的布样方案。本研究以北京顺义区为例,以土壤类型、土壤质地、土地利用类型和坡度作为辅助变量进行监测点布局优化,并与简单随机采样方法、空间分层采样方法、条件拉丁超立方体采样方法（cLHS）进行比较。结果显示：相较于其他3种方法,本文提出的方法的特征空间覆盖率平均提高15%左右,耗时远小于cLHS,略高于简单随机采样和空间分层采样,所获取的监测点布设方案的不确定性明显低于其他3种方法,重金属含量分布特征与总体数据更为接近。综上,本研究提出的方法能够显著提升监测点在特征空间的代表性,可有效反映调查区域土壤属性总体分布特征,能为后续调查监测土壤信息提供参考手段。