Experimental Study on Inducement and Development of Flow Instabilities in a Centrifugal Compressor with Different Diffuser Types

A centrifugal compressor is a typical compressed air energy storage device. In order to ensure the safety of the compressed energy storage process in the compressor, the internal unsteady flow phenomena need to be closely monitored, especially some serious ones like stall and surge. It is necessary to explore the mechanism of flow instabilities under different conditions. A centrifugal air compressor was tested with a vaneless diffuser and a variable vaned diffuser with five different vane setting angles, respectively. Various diffuser types resulted in various modes of flow instabilities prior to surge. The vaneless region between the impeller and the diffuser was focused on. Multiple high-speed sensors were arranged along the circumferential direction. The pressure signals at all these positions were being measured and collected in real time as the compressor was slowly throttled into surge. This paper emphasizes on the influence of matching between the impeller and the diffuser on the flow instability. The experimental results showed that the diffuser vane setting angle affected the stall characteristics. Due to the asymmetry of the volute, the circumferential pressure distribution was always severely distorted prior to surge. A high-pressure region appeared near the volute tongue, and a low-pressure region was formed away from the volute tongue. In the case of the vaned diffuser with non-design installation angle and the vaneless diffuser, the rotating stall signal was originated in the low-pressure region and propagated circumferentially. However, in the case of the vaned diffuser with the design installation angle, the circumferential high-pressure region became the most sensitive region for the generation of stall, and another form of instability occurred there. Both the inducement and development of these flow instabilities have been studied. The dynamic experimental research on the compressor matching different types of diffusers could be a good case supplement.     

贝叶斯网络查询语义扩展的专家发现方法

专家发现是实体检索领域的一个研究热点，针对经典专家发现模型存在索引术语独立性假设与检索性能低的缺陷，提出一种基于贝叶斯网络模型的专家发现方法。该方法模型采用四层网络结构，能够实现图形化的概率推理，同时运用词向量技术能够实现查询术语的语义扩展。实验结果显示该模型在多个评价指标上均优于经典专家发现模型，能够有效实现查询术语语义扩展，提高专家检索性能。     

汽蚀对离心泵性能影响的实验研究

离心泵长时间运转后,会出现危害严重的汽蚀现象,造成泵性能下降,严重时影响泵的效率、寿命,甚至造成离心泵内部部件损坏。文章通过改变进口压力的方法对单泵、双泵并联、双泵串联等工况下汽蚀对离心泵性能影响进行实验研究。结果表明:当进口压力小于输送液体的饱和蒸汽压时,液体开始发生气化并伴随气泡产生,表明泵内发生汽蚀;汽蚀初期离心泵的效率开始下降,但表现不明显;随着汽蚀不断增大至严重汽蚀区时,Q 1~η曲线出现陡坡,效率出现明显下降并伴随嗡响。实验证明采用提高泵的进口压力等措施可适当减小汽蚀对泵性能的不利影响。     

基于并行多通道卷积长短时记忆网络的轴承寿命预测方法

在预测轴承剩余使用寿命时，数据间的时序特性是一个可以利用的重要隐藏信息。为了更好地提取具有时序信息的特征用于预测，提出了一种基于并行多通道卷积长短时记忆网络（PMCCNN-LSTM）的剩余使用寿命预测模型。该模型主要由两部分组成：前端为并行多通道卷积网络（PMCCNN），提取信号特征，挖掘数据的时序特性，并采用逐层训练和微调的方式提升参数的收敛性；后端为长短时记忆（LSTM）网络，基于特征进行剩余使用寿命预测，并采用加权平均的方法对预测结果进行平滑处理。在一个轴承加速寿命实验的公开数据集上使用留一法验证了该模型的准确性，实验结果表明：所提模型的平均误差与最大误差分别比传统的卷积神经网络（CNN）低23.38%和15.84%，比传统的LSTM低24.14%和19.01%，比卷积长短时记忆网络（CNN-LSTM）低30.32%和23.09%。     

基于RG 1.183的先进小堆选址源项模型初步研究

在《用于评估核动力反应堆设计基准事故的替代放射性源项》RG 1.183所述的假想事故场景情况下,考虑目前大多数的先进小型压水堆地上-地下布置的设计特点,对传统大型压水堆选址源项计算模型做了改进:在原安全壳内放射性物质守恒方程的基础上,考虑辅助厂房的阻滞作用,建立辅助厂房内放射性物质守恒方程。并以某先进小型反应堆核电厂为例,利用新模型计算了代表核素的释放,与现有模型进行了对比。     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

Fermatean fuzzy linguistic set and its application in multicriteria decision making

In this paper, we propose the concept of Fermatean fuzzy linguistic term sets based on linguistic term sets and Fermatean fuzzy sets. The basic operational laws, the score function, and the accuracy function of Fermatean fuzzy linguistic numbers are provided. Then we propose the Fermatean fuzzy linguistic weighted aggregation operator, the Fermatean fuzzy linguistic weighted geometric operator, and the Fermatean fuzzy linguistic distance measures. Furthermore, we extend the TOPSIS method to the proposed distance measures, and the ranking of alternatives is obtained under a Fermatean fuzzy linguistic environment. An example is provided to illustrate the feasibility and effectiveness of the proposed method, and a comparison with the existing method is also analyzed. Finally, the sensitivity analysis of the parameter in the proposed distance measure is also discussed.     

蜗壳进口宽度对离心泵非定常性能的影响研究

为分析蜗壳进口宽度对离心泵非定常性能的影响,以一台比转速为126的离心泵为研究对象,采用ANSYS CFX软件的标准k-ε湍流模型对同一叶轮、不同蜗壳进口宽度(56、63、66.5和70 mm)时,不同流量工况(0.8～1.2设计流量)条件下泵的外特性、内部流场以及压力脉动进行研究。结果表明:在设计流量下,适当改变蜗壳进口宽度对离心泵的扬程、效率影响不大;但随着蜗壳进口宽度的增大,蜗壳隔舌处的湍动能增大并向扩散段延伸;同时,蜗壳隔舌处压力脉动的压力值增大了4.2%,压力脉动幅值增大了3.4倍。设计时为提高中比转速离心泵的综合性能,应取蜗壳进口宽度为叶轮出口直径的1.8倍。     

Identification of Wiener systems based on the variable forgetting factor multierror stochastic gradient and the key term separation

The identification of Wiener systems is very difficult because of the output nonlinearity and the parameter product term. To identify the Wiener system, a novel stochastic gradient algorithm based on the multierror and the key term separation is proposed. Firstly, the Wiener system is parameterized as a pseudo-linear model to avoid the products of the parameters. Secondly, a parzen window is used to estimate the probability density function of the error. Thirdly, a stochastic information gradient algorithm with the multierror is adopted to estimate the parameters. The multierror takes the place of the scalar error by the stacked error, which accelerates the algorithm greatly. Fourthly, a variable forgetting factor considering errors is integrated to further speed the algorithm up. Finally, the proposed algorithm is validated by a numerical example and an industrial case. The estimation results indicate that the proposed algorithm can obtain accurate estimates with fast convergence speed.