分流叶片对离心泵内部固液两相流的影响

为了研究分流叶片对离心泵内部固液两相流的影响,建立固液两相流模型,对无分流叶片离心泵和3种带分流叶片的离心泵进行全流道非定常数值模拟。重点分析了添加分流叶片对离心泵外特性、叶片表面压力脉动、叶片表面颗粒体积分数分布、叶片表面颗粒滑移速度的影响。分析结果表明:添加分流叶片以后,离心泵的扬程和效率均有显著的提高,叶片表面的颗粒分布更加均匀,叶片表面颗粒的滑移速度有不同程度的下降,使颗粒对叶片表面的磨损程度得以降低;当分流叶片的进口直径为0.65D2时,叶片出水边处的压力脉动幅值最小,减小了离心泵在运行过程中的振动和噪声。     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts

The Yellow River Estuary area of China is under great pressure from both human intervention and natural processes. For analysis of the changes in this area, this article presents a novel change-detection method based on a local fit-search model and kernel-induced graph cuts in multitemporal synthetic aperture radar images. Change detection involves assigning a label to every pixel. This task is naturally formulated in terms of energy minimization, which can be effectively solved by graph cuts. The difference image is transformed implicitly by a kernel function so that an alternative to complex modelling of the original data makes the piecewise constant model become applicable for graph cuts formulation. An issue is that graph cuts are sensitive to the initial estimate. The local fit-search model is proposed to approximate to the local histogram while selecting an optimal threshold for the initial labelling, which leads to an effective constraint for graph cuts and computational benefits as well. Visual and quantitative analyses obtained on the Yellow River Estuary data set confirm the effectiveness of the proposed method and that it outperforms the other state-of-the-art methods of change detection.     

钢渣的基本性质和道路工程中的利用前景

通过对钢渣及钢渣混合料的物理力学及化学性能分析，对钢渣在公路工程各个方面的应用可能性作了分析与探讨，并针对目前应用中存在的技术经济问题提出了解决的思路与办法。     

注塑模具标准摸架数据库的开发

注塑模ＣＡＤ中机械设计模具种类繁多，但基本上还是趋于标准化的，为使模具的模架及标准部件有序调用，模架数据库管理系统（ＭＤＢＭＳ）及标准部件数据库管理系统（ＳＰＤＢＭＳ）成为注塑模ＣＡＤ系统中的关键问题。本文介绍了注塑模ＣＡＤ中的ＭＤＢＭＳ及ＳＰＤＢＭＳ的目标和任务，ＭＤＢＭＳ和ＳＰＤＢＭＳ应具有的功能和结构，其中的几何关键技术以及今后的展望。     

高水头混流式水轮机内部固液两相流数值分析

针对泥沙直径对混流式水轮机蜗壳和导水机构内部流场产生影响的问题进行了初步研究,采用固液两相流模型对某电站的高水头混流式水轮机进行了全流道数值模拟,通过单一变量法来研究不同来流泥沙的直径对蜗壳和导水机构内部压力及泥沙分布产生的影响。研究结果表明:与清水相比,含沙水流会不同程度地使蜗壳和导水机构内部的水压力载荷增加,在相同工况下,蜗壳底部、蜗壳鼻端、固定导叶和活动导叶头部的泥沙浓度较大;小直径下泥沙分布均匀,随着泥沙直径的增加,泥沙浓度的梯度变大,泥沙分布更加集中,从而会使磨损加剧。该研究成果可为在含沙水流中运行的水轮机设计提供参考。     

HEC-RAS模型在桥墩壅水计算与影响分析中的应用

HEC-RAS是一种一维水动力模型,采用HEC-RAS中的恒定流模型对上犹江特大桥施工期和建桥后进行壅水分析和计算.计算得上犹江特大桥施工期5年一遇洪水最大壅高值为0.02 m;建成后10年一遇、20年一遇以及50年一遇洪水最大壅高值分别为0.01 m、0.02 m和0.03 m;大桥的兴建引起的壅水较小,因此对河道行洪安全影响较小,对河段上游河段排涝影响较小.HEC-RAS中的恒定流模型基于能量方程,利用该模型对桥墩进行壅水计算及防洪影响进行分析,参数设置简单,断面布设方便,值得推广.     

生物质型煤的燃烧特性和影响因素

利用热分析仪和自行设计的实验台研究了生物质型煤的燃烧特性.结果表明:无烟煤生物质型煤中生物质分解释放速率快于烟煤生物质型煤,且无烟煤生物质型煤出现两个放热峰,分别为生物质分解、挥发分燃烧和固定碳分解所致,而烟煤生物质型煤仅一个放热峰.原料煤煤种和炉膛温度对生物质型煤的燃烧特性有明显影响,烟煤型煤燃烧特性优于无烟煤型煤;炉膛温度越高,生物质型煤燃烧失重率越大.     

Improved RM-MEDA with local learning

In this paper, local learning is proposed to improve the speed and the accuracy of convergence performance of regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), a typical multi-objective optimization algorithm via estimation of distribution. RM-MEDA employs a model-based method to generate new solutions, however, this method is easy to generate poor solutions when the population has no obvious regularity. To overcome this drawback, our proposed method add a new solution generation strategy, local learning, to the original RM-MEDA. Local learning produces solutions by sampling some solutions from the neighborhood of elitist solutions in the parent population. As it is easy to search some promising solutions in the neighborhood of an elitist solution, local learning can get some useful solutions which help the population attain a fast and accurate convergence. The experimental results on a set of test instances with variable linkages show that the implement of local learning can accelerate convergence speed and add a more accurate convergence to the Pareto optimal.     

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

The existing algorithms to solve dynamic multiobjective optimization (DMO) problems generally have difficulties in non-uniformity, local optimality and non-convergence. Based on artificial immune system, quantum evolutionary computing and the strategy of co-evolution, a quantum immune clonal coevolutionary algorithm (QICCA) is proposed to solve DMO problems. The algorithm adopts entire cloning and evolves the theory of quantum to design a quantum updating operation, which improves the searching ability of the algorithm. Moreover, coevolutionary strategy is incorporated in global operation and coevolutionary competitive operation and coevolutionary cooperative operation are designed to improve the uniformity, the diversity and the convergence performance of the solutions. The results on test problems and performance metrics compared with ICADMO and DBM suggest that QICCA has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.