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关键词： 叶片扩压器   流动分离   流动控制   合成射流   流动损失  

摘要： 本文在离心压气机叶片扩压器叶片吸力面施加合成射流,采用URANS方法研究了合成射流对叶片吸力面流动分离的影响,并从流动损失和扩压能力两方面分析了合成射流对扩压器的性能改善。结果表明,合成射流有效抑制了叶片吸力面的流动分离,并消除了由于分离而产生的闭式分离泡,减少了由于分离泡和周围高速流体的剪切作用以及下游分离泡破碎、掺混所造成的流动损失。由于闭式分离泡的消失,叶片流道的有效通流面积增加,叶片扩压器的扩压能力也因此增强。从控制机理上讲,吹气阶段的高动量射流增加射流下游近端壁流体的动量,并"带动"边界层流体,使之切向动量增加;吸气阶段的抽吸作用使得上游边界层流体切向动量得以增加,并且加强主流和下游边界层流体的掺混,边界层流体动量因此增强。由于边界层流体切向动量的增加,抵抗逆压梯度的能力增强,因而流动分离得以抑制。

关键词： AKNS   系统   势函数   反问题  

摘要： 考虑定义在[0,1]区间上AKNS算子的逆谱问题。证明了假设2组势函数在区间[a,1](a∈(0,1/2])上已知且它们的差属于Lp空间,若2个系统的共同特征值的数量足够大则这2组势函数相等,且共同特征值的数量与系数p和a有关。

关键词： 生物型表面活性剂   润湿规律   吸附规律   表面张力   接触角  

摘要： 为了探讨生物型表面活性剂溶液在煤表面的润湿吸附规律,选定茶皂素、蔗糖酯、无患子和脂肽这4种结构性能不同的生物型表面活性剂作为研究对象,通过对4种溶液在煤表面接触角和表面张力进行测定,研究这4种生物型表面活性剂在煤表面的铺展系数、黏附功、黏附张力等润湿吸附参数。研究结果表明:脂肽的表面张力、接触角和黏附功均低于其他3种溶液,而其铺展系数高于其他3种溶液,说明脂肽在煤表面的润湿吸附性要强于茶皂素、蔗糖酯和无患子,但是黏附性则低于茶皂素、蔗糖酯和无患子。脂肽对煤体的润湿吸附性能优于另外3种生物型表面活性剂。

关键词： 电脱水器   绝缘棒   三维圆柱绕流   FLUENT软件   数值模拟   漩涡   卡门涡街  

摘要： 使用FLUENT软件数值模拟研究原油电脱水器绝缘棒的三维圆柱绕流问题,旨在寻找合适的原油流速,使得漩涡区域最小。采用标准k-ε模型对来流速度为0.5、1.0和2.0m/s三种情况进行了模拟计算,并详细分析了计算结果,包括压力云图和速度云图等。对不同流速下三维圆柱体绕流的各个截面漩涡脱落形式和速度、压力的变化情况进行分析,从而研究圆柱体绕流问题的三维效应,研究发现,通过改变区域的速度就可达到改变甚至消除卡门涡街的目的,同时也证明了FLUENT软件对不可压缩绕流计算的可行性和准确性。

关键词： 植物油   表面张力   皂化反应  

摘要： 油脂的皂化反应及其相关产品在人们的日常生活与工业生产中起着至关重要的作用。本文将油脂的皂化反应拟人化,介绍了皂化反应的物理化学原理和油脂的相关性质,让读者在趣味阅读中了解相关化学理论和知识。

关键词： imaging flow cytometry   phytoplankton   taxonomic resolution   morphology   microscopy   cyanobacteria  

摘要： Phytoplankton are aquatic, microscopically small primary producers, accounting for almost half of the worldwide carbon fixation. As early indicators of environmental change, they play a crucial role in water quality management. Human activities like climate change, eutrophication, or international shipping traffic strongly impact diversity of these organisms. Phytoplankton monitoring is a crucial step in the recognition of changes in community composition. The common standard for monitoring programs is manual microscopic counting, which strongly limits sample number and sampling frequency. In contrast, high-throughput technologies like standard flow cytometry (FCM) are restricted to a low taxonomic resolution, which makes them unsuitable for the identification of indicator species. Imaging flow cytometers (IFC) could overcome these limitations as they combine microscopy and high-throughput analysis. In comparison to single fluorescence values, image information not only allows for a wide variety of possibilities to characterize different species as well as immediate and fast measurements but also provides an archivable data output. Taxonomic resolution of IFC (ImageStream X Mk II) was proven comparable to standard FCM (FACSAria II) by the help of numerical evaluations. This is demonstrated on different levels of taxonomic differentiation of laboratory grown cultures in this study. Phytoplankton species discrimination by an imaging flow cytometer could be useful as supportive tool to make machine-learning classifications more robust, reliable, and flexible. Furthermore, this study provides examples, demonstrating the possibility of discrimination between species with similar fluorescence properties, strains, and even subpopulations. In contrast to standard FCM, each cell is not only represented as a dot in a cytogram but is also linked to microscopic brightfield and the author presents a new way to visualize this as image-based cytograms. The source code is supplied

关键词： Classified model   Radial basis function neural network   60 degrees Bend   90 degrees Bend   Flow velocity   Flow depth  

摘要： In this study, a comparative analysis is done to evaluate the ability of classified radial basis function neural network (CRBFNN) model in estimation of flow variables in sharp open-channel bends with bend angles of 60 degrees and 90 degrees. Accordingly, a RBFNN model is integrated with classification method to design a novel CRBFNN model to predict two velocity and flow depth parameters in a 60 degrees sharp bend. Furthermore, Gholami et al. (Neural Comput Appl 30:1-15, 2018a) pointed out to acceptable ability and more efficiency improvement of hybrid CRBFNN model in prediction of flow variables in 90 degrees sharp open-channel bend compared to simple RBF model. On the other hand, the flow pattern in sharp bends is more complicated than in mild open-channel bends. Moreover, the behavior of flow and its variables is varied in 60 degrees and 90 degrees sharp bends. Therefore, the present paper is aimed to evaluate the performance of RBF and CRBF models in two 60 degrees and 90 degrees (Gholami et al. 2018a) sharp open-channel bends. Available experimental data for velocity and flow depth at six different hydraulic conditions are used to train and test the CRBFNN and simple radial basis function neural network (RBFNN) networks in 60 degrees open-channel bend. Accordingly, efficiency of both RBFNN and CRBFNN models in different bend cross sections is evaluated and compared with each other. The results show that using classified model has improved the simple RBF model performance, as in the CRBFNN model, the error root mean square error and mean absolute error value, 18% and 15.3% for the flow depth prediction and 9% and 5% for the velocity prediction compared to the simple RBFNN model is reduced, respectively. Furthermore, the comparison of model performance in 60 degrees and 90 degrees bends represents that both RBFNN and CRBFNN models in all discharge values in velocity prediction have more ability in 60 degrees bend so that the mean absolute relative error (MARE) v

关键词： Non-spherical particles   Nano-iron powder metal fuel motor   Nozzle two-phase flow losses   Characteristics analysis   Combustion experiment  

摘要： Metal iron powder is a promising new energy source that is of significant practical and research interest for future automotive power systems. However, the shapes of the particles are typically assumed to be spherical or an equivalent sphere when estimating the specific impulse of motors. Such an assumption lacks objectivity and can result in unreasonable estimations of two-phase flow losses. In order to better optimize the design of an engine, this study focuses on the influence of non-spherical particles (such as ellipsoidal and cuboid particles) on the characteristics of nozzle two-phase flow. Models for the governing equations of nozzle two-phase flow are developed to conduct a theoretical study to analyze the combustion properties of iron oxide particles and flow in the nozzle. In addition, experimental studies involving nanometer iron-powder particle combustion and engine thrust measurements are conducted to validate the results obtained from numerical calculations that are conducted using a fourth order Runge-Kutta-Gill method. The results indicate that particle morphology, size, and coagulation content play a significant role in the motor performance and the two-phase flow losses. Specifically, the hypothesis of the ellipsoidal model is in better agreement with the experimental findings compared to the other particle models. (C) 2019 Elsevier Masson SAS. All rights reserved.

关键词： Cardiac pathology   Classification   Cine MRI   Motion   Deep learning   Semi-supervised learning   Neural network   Apparent flow  

摘要： We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricular cavities. This leads to the generation of time series of the radius and thickness of myocardial segments to represent cardiac motion. These time series of motion features are reliable and explainable characteristics of pathological cardiac motion. Furthermore, they are combined with shape-related features to classify cardiac pathologies. Using only nine feature values as input, we propose an explainable, simple and flexible model for pathology classification. On ACDC training set and testing set, the model achieves 95% and 94% respectively as classification accuracy. Its performance is hence comparable to that of the state-of-the-art. Comparison with various other models is performed to outline some advantages of our model. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Binary mixture   Computational fluid dynamics simulation   Internally circulating fluidized bed   Mixing   Solids circulation rate  

摘要： The process where solids circulate in an internally circulating fluidized bed (ICFB) with two or more chambers can be developed by applying unequal gas velocities in these chambers. In this study, a twin chamber ICFB with a binary mixture (two particles comprising G116 and P275, which differed in terms of their size and density) was simulated using the multi fluid Eulerian model. The effects of variations in the gas velocities, mixing of solids, and mixture composition were investigated, and the results were quantified in terms of the solids circulation rate (G(s)). An increase in the gas velocity in the reaction chamber (RC), U-R, at a constant gas velocity in the heat exchange chamber (HEC), U-H, resulted in an increase in the circulation rate for the binary mixture. Solid particles flowed upward in the RC and downward into the HEC. The ratio of the circulation rate for G116 relative to that for P275 was approximately equal to the ratio of their masses in the ICFB. An increase in U-H at constant U-R led to a decrease in the circulation rate of the binary mixture. Similar to U-R and U-H, the mixture composition played a critical role in controlling the circulation rate of the binary mixture in the ICFB. Furthermore, the ICFB exhibited a higher capacity for solids mixing compared with a conventional bubbling fluidized bed. (C) 2019 Elsevier Inc. All rights reserved.