柴油机SCR催化器载体结构参数优化

提出一种柴油机选择催化还原（SCR）催化器载体结构参数优化的设计方法,根据车用柴油机排量,将SCR催化器载体分为4类,选取载体体积、长度、目数、壁厚和涂层厚5个结构参数为设计变量,以高NO。转化率及低压力损失为优化目标,利用拉丁超立方实验设计选择样本点进行数值模拟,在构建的Kriging近似模型基础上,对载体结构参数采用改进的非支配排序遗传算法NSGA—II（non—dominatedsortinggeneticalgorithm—II）进行优化设计。结果表明：Kriging近似模型的拟合精度较高,结合NSGA—II算法对SCR催化器载体结构参数进行优化是可行的、有效的,不同排量下的优化结果均能够较好地满足设计要求。     

基于自适应调整蚁群-RBF神经网络模型的中长期径流预测

径流预测历来是水利部门的一项重要工作,针对水库和河流中长期径流预测精度不高,提出了自适应调节人工蚁群算法(ARACS),对RBF神经网络参数进行优化,建立了自适应调节人工蚁群-RBF神经网络组合算法(ARACS-RBF)预测模型,综合考虑影响径流预变化因素,对安康水库进行中长期径流预测。对预测效果进行检验,结果证实该模型可真实地反映河川径流变化的总体趋势, 并为判断时间序列数据的非线性提供了一种新方法。与RBF神经网络模型、人工蚁群-RBF神经网络模型预测结果进行对比,结果表明,应用ARACS-RBF模型对中长期径流量进行预测,预测精度更高、效果更好。该方法克服了RBF神经网络和人工蚁群算法易陷于局部极值、搜索质量差和精度不高的缺点,改善了RBF神经网络的泛化能力,收敛速度快,输出稳定性好,提高了径流预测的精度,置信度为98%时的预测相对误差小于6.5%。可有效用于水库和河川中长期径流预测。     

Collision of convex objects for calculation of porous mesh in gas explosion simulation

We investigate the coupling of the flamelet combustion model with the collision distance algorithm based on Minkowski addition. The collision algorithm is coded to calculate the porosity of the geometry based on the PDR (Porosity Distributed Resistance) approach for modelling of complex geometries. The turbulent field generated by the interaction of the flow with the porous objects is used to calculate the wrinkling length scale of the flame via the fluctuating velocities. The turbulent fluxes are amended in accordance with assigned porosities at the cell faces. The combustion and porosity models are implemented in the framework of an in house Fortran code that solves the full set of Navier-Stokes equations. The code was named STOKES - Shock Towards Kinetic Explosion Simulator). Results are presented for non-reacting flows and reacting flows over different geometries. Numerical findings are compared with standard commercial CFD tools.     

Real-time leak detection in oil pipelines using an Inverse Transient Analysis model

Based on Inverse Transient Analysis (ITA) method, a real-time leak detection method is proposed to capture leak location and the associated leak rate in oil pipe conveyance systems. In the proposed approach, location and flow rate of leak (if any), the fluid properties, as well as physical parameters of the system, are calculated in consecutive periods through minimizing the discrepancy between the calculated and measured flow parameters of the system. The method of characteristics is employed to numerically calculate the transient responses of the system and the genetic algorithm is utilized as the optimization engine. The proposed approach was applied to several real pipeline systems in which the required transient flow data are either directly collected from the field or fabricated with a third-party numerical software. Extensive numerical explorations were conducted to investigate the performance of the proposed method in real-time leak detection and to determine the extent to which field data errors, stemming from Supervisory Control and Data Acquisition (SCADA) systems and measurement equipment, affect the leak flow rate and location detectability of the proposed approach. The results show that the proposed approach provides promising results under a variety of transient and steady-state flow conditions even in the case with small leak flow rate of around 2% of the line rate. The results also reveal that the noises in the measurement data and the errors originated from SCADA systems do not significantly compromise the leak detectability of the proposed approach, confirming that the proposed approach can be utilized in practice.     

基于改进Apriori算法的电力通信设备缺陷关联性分析

为了保障大规模电力通信设备接入电网的稳定运行,首先分析通信设备的物理模型,然后分析电力通信设备缺陷的影响因素和缺陷特征及两者之间的映射关系,接着对Apriori算法进行改进,降低算法的时间复杂度和内存空间,导入电力通信设备缺陷数据模型并结合关联规则,提出基于改进Apriori算法的电力通信设备缺陷关联性分析方法.结果表...     

一种基于改进的一致性算法数据融合技术

在重大工程结构健康监测中,随着研究对象复杂程度的提高,往往需要获得大量观测数据才能对结构进行有效的评估,因此采用多种或多个传感器进行测量已成为必然趋势。数据融合技术就是将多个传感器的测量结果进行综合处理,从而得出比单个传感器更为准确可靠的结果。本文基于一致性算法,提出一种改进的多传感器数据融合技术,该数据融合技术属于数据级融合,它克服了一致性算法中两传感器在测量精度不同时置信距离不同的缺点,并对支持矩阵进行模糊化处理,避免了人为定义阈值而产生的主观误差。文中通过算例,验证了此方法可获得较好的结果,并且能够有效地减小由于扰动因素造成的测量数据的变化。     

Cleaner production for continuous digester processes based on hybrid Pareto genetic algorithm

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基于遗传神经网络模型的大气环境质量评价方法

设计了用遗传算法训练神经网络权重的新方法,实验结果显示了遗传算法快速学习网络权重和全局搜索的能力,有效地解决了BP算法的局部收敛问题。误差反向传播的遗传——神经网络(GA—BP)模型用于大气环境质量综合评价,具有简便、准确、客观和适应性强等优点。     

人工神经网络在生态环境质量评价中的应用

以生态环境指标的各级评价标准作为训练样本,应用人工神经网络建立生态环境质量评价的BP网络模型。用训练好的BP网络对生态环境质量进行评价,并与模糊识别评价法进行比较。结果表明,人工神经网络用于生态环境质量的评价结果与环境质量实况相符。     

Optimal water quality monitoring network design for river systems

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.