生命线工程网络抗震可靠性分析方法的比较研究

生命线工程网络抗震可靠性分析是进行生命线工程系统规划、设计、改造与优化的基础。因此 ,寻找一种高效的适应性强的系统可靠性分析方法对实际工程应用具有重要的意义。本文主要介绍两种高效的网络系统两终端连通可靠性分析方法———递推分解算法和有序二分决策图算法。首先 ,分别对两种算法的原理及实现过程进行了详细的论述和说明 ,并且用c语言编制了二分决策图算法的计算程序。然后 ,利用这两种方法及随机模拟算法对 2 0个以往研究中的经典算例和两个实际工程网络———河南省电力网和上海市浦西供水管网 ,进行了网络系统抗震连通可靠性分析。通过对实例分析结果的比较研究 ,得到了一些经验性的结论 ,以期为优化设计工程网络时选择系统可靠性的分析工具提供参考。研究表明 ,由于递推分解算法具有适应性强和能够求得问题近似解的优点 ,因此有良好的实际工程应用前景。     

Discrimination of different social companions in spectacled parrotlets (Forpus conspicillatus): evidence for individual vocal recognition

Individual recognition is generally assumed to be a prerequisite for establishing and maintaining a complex social system. Indeed, there is good evidence that highly social species have complex systems of vocal communication with individual recognition by acoustic cues. In this study, we provide experimental evidence that vocal class and individual recognition is present in a non-passerine bird, the spectacled parrotlet (Forpus conspicillatus). Spectacled parrotlets live in a complex system of social relationships. Soon after fledging, the young establish close sibling relationships which are important for successful socialization, pairing and reproduction. In a series of playback experiments we tested if spectacled parrotlets use contact calls for vocal recognition. The results showed that spectacled parrotlets discriminate between the contact calls of different social categories. Adult birds preferred to respond to the contact calls of their mates. Subadult individuals recognized the contact calls of their siblings. During the period of pair bond formation, the affiliative contacts to the siblings decrease, but the parrotlets continue to respond to the calls of their siblings. This is the first evidence that vocal sibling recognition might outlast the period of strong sibling interaction and extends into the period of pair bond formation. In cases of mate loss or divorce, the acoustic contact to their siblings might facilitate the re-establishment of close sibling relationships. Received: 29 October 1997 / Accepted after revision: 5 April 1998     

不锈钢板式热沉传热特性的实验与模拟研究

目的 研究流量、胀高及焊点排布方式对不锈钢板式热沉传热特性的影响。方法 搭建实验平台,加工出结构参数不同的板式热沉实验件,同时采用静力隐式算法建立相应的几何模型,通过实验与数值模拟相结合的方法,研究流量、胀高和焊点排布方式的变化对板式热沉热阻的影响规律,并分析热沉流道内部流场状态。结果 首先计算了6个工况下的热阻,发现实际换热量为540 W时,数值模拟与实验结果的最大相对误差不大于25%。然后分析了误差来源,最后得到了热沉全流道速度场与温度场的分布情况。结论 实验与模拟结果有较好的符合程度,证明了数值模拟方法的可靠性。在换热量相同时,板式热沉的热阻随流量与胀高的增大而减小,焊点为菱形排布的热沉传热性能相对更好。板式热沉内部的流场具有周期性,焊点使流体扰动增强,并形成射流和旋涡,起到了强化传热的效果。     

基于机器学习分类算法解析EIS数据的有机涂层性能评价方法

目的 基于机器学习分类算法快速评估有机涂层的防腐性能。方法 通过实验室加速试验模拟涂层真实的退化过程,并根据测得的电化学数据,分析不同退化阶段的等效电路元件参数。随后,采用随机抽样方法获取大量数据,用于机器学习模型训练。通过对比支持向量机(SVM)、k最近邻(k-NN)和随机森林(RF)3种不同的机器学习算法,以及多种输入特征集训练的涂层性能分类器模型的准确率,分析最适合用于涂层性能快速评估的机器学习算法和电化学特征。结果 根据不同输入特征训练的k-NN和RF模型均表现出良好的预测效果,而SVM模型的预测效果相对较差。根据不同频率范围训练的分类器模型中,在低频区表现最佳,而在高频区表现较差。结论 基于阻抗虚部、虚部+实部和阻抗模值3种输入特征训练的RF分类器模型的预测效果最准确。不同频率区间内,低频区的阻抗特征更能准确表征涂层性能。     

Numerical model of flow and pollutant transportation in non-orthogonal curvilinear coordinates

The planar 2D k-ε double equations‘ turbulence model was adopted and transformed into non-orthogonal curvilinear coordinates. The concentration convection-diffusion was introduced to planar 2D SIMPLEC algorithm of flow in non-orthogonal curvilinear coordinates. The numerical model of pollutant transportation in non-orthogonal curvilinear coordinates was constructed. The model was applied to simulate the flow and pollutant concentration fields. In the testing concentration field, two optimal operations of contamination discharging both along bank and in the centerline at the first bend of the meandering channel were adopted. Comparison with available data showed the model developed was successful, was valuable to engineering application.     

区域联防联控挥发性有机物(VOCs)最优减排方案研究

VOCs区域性联防联控治理是当前彻底治理VOCs污染的有效手段,为了实现VOCs区域性联防联控治理,建立了以减排总量最大、减排成本最低、VOCs减排限产产生的经济影响最小为目标的多目标最优减排模型,提出了基于自适应变异率的分解多目标进化算法(Decomposition Multi-objective Evolution...     

环境管理过程中对锅炉型号的识别

通过对锅炉型号的简要介绍,提高对锅炉的认识,增强在污染源动态更新调查表的填报过程中锅炉技术指标的填报能力是十分必要的。     

面源模式的通用算法探讨

提出了矩形面源扩散模拟计算的数值积分法和中心虚点源法,以及二者结合的综合法,并用算例进行了计算精度、计算速度和结果连续性的对比.结果表明:与传统的后置虚点源法及其改进算法——谷清经验法相比,该算法与理论真值的相对误差可从20%以上降低到3%以下,并且可以准确地反映风向和面源的形状,但在计算量上要增大十几倍到上百倍.对于任意不规则面源,提出了正方形分割算法,并对分割正方形的取舍采用简便的重心判断法,以准确方便地模拟其形状.综合运用笔者提出的适于机算的通用面源算法,对不同的计算任务权衡速度和精度采用合适的算法,可以处理各种气象条件下任意形状面源的扩散计算,并可取得理想的计算精度.      

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.