双叶片离心泵滑移系数修正实验

采用PIV技术对一双叶片离心泵叶轮出口处的滑移速度进行了实验研究,得到了7个工况、6个相位下的滑移系数,发现随着流量的增大6个相位下的平均滑移系数呈递减的趋势变化。采用斯托道拉公式、威斯奈尔公式、斯基克钦公式分别计算了双叶片离心泵的滑移系数,并与各个工况下的实验值进行了比较。根据实验值对计算误差最小的威斯奈尔公式进行了修正,并重新设计一个双叶片叶轮进行了验证。研究结果表明7个工况下修正后的滑移系数计算误差均在3.5%以内,其最大误差为3.23%。     

粒子速度场仪在泵装置内流测量研

介绍国内外利用粒子速度场仪（PIV）对泵装置内部复杂流动测量的研究进展，分析了针对泵装置内流测量的PIV系统的特点，概括了目前在泵装置测量方面已取得的研究成果，最后预期了PIV技术在泵装置测量应用方面未来的发展方向。     

动态水压下迷宫流道水流运动特性研究

采用粒子跟踪测速(PTV)技术,通过观测动态水压及恒定水压下迷宫流道内示踪粒子的运动规律,分析动态水压下迷宫流道内水流的运动特性,揭示动态水压滴灌抗堵机理。结果表明:相对于恒定水压,动态水压下流道内流量未出现明显下降,动态水压对滴灌系统的供水能力影响较小;动态水压下流道主流区水流流速始终保持大幅度上下波动,水流紊动更加强烈,大量粒子可以快速经主流区通过流道,粒子在流道内停留时间大大缩短;滞止区水流流速仍较低,但紊动强烈,进入滞止区的粒子随水流不断运动,不易发生沉积,部分粒子在水流紊动作用下能够迅速重返主流区,并最终通过流道,粒子在流道内沉积的概率明显降低。采用动态水压可以有效提高流道抗堵能力。     

层流炉二维PIV自动控制系统

在层流炉冷态粒子图像测速(PIV)系统中,相机与测量管的相对位置是保证试验数据精确的重要条件。为使试验过程中整个PIV系统操作起来更加方便、准确、快捷,设计了二维PIV自动控制系统,并在层流炉冷态模拟装置上对该系统的可靠性进行了试验验证。与没有使用二维PIV自动控制系统之前的试验结果相比:各测量段颗粒的轴向中心速度相互之间的衔接实现了平滑过渡,消除了跳跃性变化;收集距离为350 mm,主气流流量为1.5 m3/h时,层流炉内颗粒停留时间的相对误差为9.218%。说明该二维PIV自动控制系统能够满足层流炉冷态试验需要,实现了均匀、连续拍摄,减少了人为误差,提高了试验数据的准确性。     

过渡流下近壁双圆柱倾角对绕流结构影响

为了研究过渡流下近壁双圆柱倾角对绕流结构的影响,在雷诺数为300(基于圆柱直径D=15 mm)时,采用开式循环水槽试验台,结合粒子图像测速系统PIV对不同倾角的近壁双圆柱模型进行了试验研究.结果表明:当Re 为 300时,倾角γ改变对圆柱绕流结构影响显著.随着倾角γ增大,上游圆柱尾流由下侧剪切层发展被抑制逐渐演变为上侧剪切层发展被抑制,上游近壁区域加速效果先增强后减弱;下游圆柱尾流由上侧剪切层主导逐渐演变为下侧剪切层主导,下游近壁区域的加速效果和范围不断增大至稳定.当γ<7.6°时,倾角γ对绕流结构影响逐渐增大,近壁区域扰动逐渐增强;当γ>7.6°时, 倾角γ对绕流结构影响逐渐减弱,近壁区域扰动趋于稳定.详细分析了γ=-7.6°工况下的瞬时流动特性,发现近壁双圆柱尾流形成周期性的旋涡交替脱落现象,增强了壁面附近流动不稳定性.     

基于PIV技术的方形网箱二维流场分析

方形重力式网箱在水流中会产生一定的阻流效应,为此研究不同类型网箱在不同流速下网箱周围的减流情况非常重要。本研究通过水槽试验,设计了4组不同网目形状与网目尺寸的网箱模型,利用粒子测速技术(PIV)、与多普勒流速计(ADV)试验技术,分析方形网箱二维流场特性与、流速分布及其减速情况。首先,试验结果发现结果显示,水流经过4组网箱后均出现明显的流速衰减,衰减区的高度与网箱高度大致相当;而网箱底部则出现流速增大区。ADV与PIV测量结果基本一致,。其中,当来流速度为0.18 m/s时,4组网箱中心点的平均流速衰减系数在0.5–~0.8左右。其次,试验结果表明,装配菱形网目的网箱流速衰减略大于方形网目网箱;小网目网箱的流速衰减则明显大于大网目网箱,而网箱的平均流速衰减系数随流速变化趋势不明显。最后,结果表明,利用PIV技术给出经过网箱中心点铅垂面流场分布的云图呈现,可清晰看出网箱周围的水流流线分布疏密程度。     

全工况下双流道泵内流三维PIV测量

为了揭示双流道泵从零流量点到大流量点内部流动的变化规律,采用粒子图像测速仪对1个比转数为1107的双流道泵在8个工况下的内部流动进行了测量．采用基于光纤制作的外触发同步系统和三维等效标定方法等关键技术来保证3D PIV的测试精度．根据速度三角形,采用Visual C++2005编写了PIV速度分解程序,将测量的叶轮内的绝对速度分解成圆周速度相对速度．研究结果表明:叶轮流道内的流动并未随着流量的减小出现大范围的旋涡,仅当流量小于04Qd以后在叶轮出口靠近吸力面侧出现1个旋涡,并且随着流量的减小该旋涡越来越明显;另外,随着流量的减小,蜗壳扩散段上的绝对速度值逐渐减小,且当流量小于 02Qd时扩散段开始出现旋涡;在叶轮出口靠近隔舌处的区域上绝对速度值随着流量的减小先增大后减小,在02Qd工况下速度值达到最大;关死工况下叶轮和蜗壳内都存在着明显的旋涡和回流,泵内流态最差．     

气力辅助静电雾化的PIV试验研究

为了研究气力辅助作用下静电雾化的流场特性,采用粒子图像测速技术（PIV）对风速、荷电电压及通气管与喷头间距等参数影响下的喷雾流场进行测量,并结合Tecplot后处理软件对所储存图片进行处理和分析,获得了不同参数下的静电喷雾流场特性.结果表明：通气管和喷头的间距以及风速一定时,不同的荷电电压形成不同程度的卷吸,且电压越大,卷吸现象越明显,卷吸区域沿着喷雾的轴向逐渐向下延伸;通气管和喷头的间距以及荷电电压一定时,随着风速的增大,气动力的主导作用越来越明显,在气流区的卷吸程度逐渐减弱,卷吸现象逐渐消失,卷吸区域逐渐向喷雾核心区收缩;风速以及荷电电压一定时,随着通气管与喷头间距的拉大,雾滴的漂移现象越来越严重,同时喷雾流场的卷吸现象越来越明显并逐渐向喷雾核心区靠近.     

Development of fish spatio-temporal identifying technology using SegNet in aquaculture net cages

In marine aquaculture, fish populations constantly decrease throughout the cultivation period because of mortality and escape. Current production management systems provide limited opportunities to count the cultured fish, making it difficult to estimate accurately the fish population in the cage. To overcome this problem, an automatic fish identifying method based on particle tracking velocimetry (PTV) flow visualization technology is proposed in this paper. The proposed method utilizes an image processing unit that extracts individual fish from the acquired image and a motion analysis unit that calculates the motion vector for each individual. Thus, the accuracy of the extraction results in the image processing unit affects the system’s counting results. To validate the efficiency and robustness of the image extraction performed by the image processing unit, individuals were extracted from images using the open-source image deep learning semantic segmentation method (SegNet), which is able to distinguish between the background and foreground in the images via analysis at the pixel level. SegNet is able to improve the image discrimination performance by multiplying the learning paths, and the robustness of the detection results can be ensured by changing the layer structure according to the detection target. Accordingly, the use of SegNet was evaluated in terms of the number of layers and images in the training set. The results of this study indicate that the application of SegNet with PTV technology represents a promising method for the automatic identifying and behavioral tracking of fish in an aquaculture net cage.     

Analysis of the hydrodynamic characteristics of chain-link woven copper alloy nets for fish cages

Copper alloy nets have drawn attention from researchers and fishermen as an alternative to fabric nets for fish cages, due to such advantages as protection from predators, better cage volume retention, and good resistance to bio-fouling. The drag and lift coefficients of nine chain-link woven copper alloys and three knotless fabric nets were derived using a circulating water channel and structural analysis of the nets. The flow through one copper alloy net was visualized using particle image velocimetry (PIV) to understand better the hydrodynamic characteristics necessary for the use of the net in a fish cage. The trends in behavior of the drag and lift coefficients for the copper alloy nets based on various attack angles and current speeds were highly similar to those of the fabric nets. The coefficients of the copper alloy nets were larger than those of the fabric nets for attack angles less than 30°. For angles greater than 30°, the coefficients of the fabric nets were larger than those of the copper alloy nets. These results might be attributed to structural differences between the two nets, as well as the roughness of the surfaces. The hydrodynamic characteristics of the alloy nets were illustrated using PIV, and a difference in the structure of the net was observed to reduce the resistance when the net was set against the current with an attack angle of 90°.