Analysis of flow distribution from high-speed flow actuator using particle image velocimetry and digital speckle tomography

A variety of active flow control (AFC) methods are typically used in low-speed applications; however, the AFC techniques that are available for high-speed, supersonic applications are very limited. Under AFOSR (Air Force Research Laboratory) sponsorship, The Johns Hopkins University Applied Physics Laboratory (JHU/APL) is investigating a device that is intended for high-speed flow control; it is called the SparkJet actuator, which manipulates high-speed flows without active mechanical components. To date, actuator characterization has included computational and experimental techniques including parametric studies and flow visualization techniques to investigate the operation of the SparkJet device under various conditions. This paper focuses on the experimental flow measurement techniques that have been implemented. The results will be used for validating prospective computational studies that investigate the detailed characteristics of the SparkJet’s discharge and cooling stages after an energy deposition pulse. Current efforts include the use of high- resolution particle image velocimetry (PIV) to quantify the quiescent air operation of a single SparkJet pulse. However, the proper seeding of the SparkJet cavity continues to be challenging and has led to the use of digital speckle tomography (DST) to measure the temperature distribution in the core of the SparkJet plume. In this study, improved PIV techniques were used to acquire a higher-resolution image of the SparkJet-entrained flow. These PIV results show that the peak velocity in the entrained flow is around 53 m/s and the plume is sustained for 75–100 μs. Additionally, the DST data show a peak temperature of 1616.3 K at 75 μs and provide supporting information for interpreting the PIV data. These results are intended to calibrate and build confidence in a computational model.     

Form drag coefficient quantification on rising bubbles using particle image velocimetry

Two-phase flow transport heavily depends on the generalized interfacial drag force term in the two-fluid model. The impact of accurate design and prediction associated with thermal energy systems is highly sensitive to multi-phase heat transfer characteristics. Because of this, the interfacial drag force has been studied with rigor for some time. The steady state drag force component in particular has been well characterized for rising single bubbles but has not been previously experimentally separated into its skin and form drag components. Historically, experimental studies were unable to measure the pressure distribution around a bubble to determine the form drag force along the bubble interface. This paper presents the outcomes of an experimental study wherein a new experimental method was developed which, for the first time, separates the form and skin drag coefficients on rising bubbles. Eleven air bubbles sizes representing spheroidal, ellipsoidal, and transition to spherical cap regimes (10²<Re<10⁴) were studied in a water test loop with velocity fields measured via particle image velocimetry; pressure fields were then synthesized from these velocity fields through the Queen2 algorithm. The skin and form drag coefficients were separated for single bubbles which showed a nominal trend of increasing form drag contribution with increasing Reynolds number. This work presents a new method and new outcomes for rising bubbles over several bubble regimes and includes a comprehensive uncertainty characterization of the resulting data.     

Particle image velocimetry in a centrifugal pump: Details of the fluid flow at different operation conditions

Centrifugal pumps are present in the daily life of human beings. They are essential to several industrial processes that transport single- and multi-phase flows with the presence of water, gases, and emulsions, for example. When pumping low-viscous liquids, the flow behavior in impellers and diffusers may affect the centrifugal pump performance. For these flows, complex structures promote instabilities and inefficiencies that may represent a waste of energetic and financial resources. In this context, this paper aims at characterizing single-phase water flows in one complete stage of a centrifugal pump to improve our understanding of the relationship between flow behavior and pump performance. For that, a transparent pump prototype was designed, manufactured and installed in a test facility, and experiments using particle image velocimetry (PIV) were conducted at different conditions. The acquired images were then processed to obtain instantaneous flow fields, from which the flow characteristics were determined. Our results indicate that the flow morphology depends on the rotational speed of the impeller and water flow rate: (i) the flow is uniform when the pump works at the best efficiency point (BEP), with streamlines aligned with the blades, and low vorticity and turbulence in the impeller; (ii) the velocity field becomes complex as the pump begins to operate at off-design conditions, away from BEP. In this case, velocity fluctuations and energy losses due to turbulence increase to higher numbers. Those results bring new insights into the problem, helping validate numerical simulations, propose mathematical models, and improve the design of new impellers.     

A robust filtering algorithm based on the estimation of tracer visibility and stability for large scale particle image velocimetry

Image velocimetry for open channel is safe, efficient and environmentally friendly and large-scale particle image velocimetry (LSPIV) is one of the most adopted methods. Furthermore, robust LSPIV algorithms strongly rely on the error vector filtering strategy. However, previous works mainly conduct filtering by median filtering, main orientation filtering and maximum filtering, and these strategies are not stable enough due to the lack of taking both the visibility and stability of tracer particles into consideration. Meanwhile, statistical property like SNR (Signal-to-Noise Ratio) and peak cross-correlation are unable to estimate the tracer visibility. In order to improve the accuracy, we propose a robust and effective filtering strategy called PPSR (Peak-Peak-Sidelobe-Ratio) from the image matching perspective to ensure the visibility and stability of tracers. We conduct a serial of experiments on the public dataset Brenta and Tiber to prove the effectiveness of the proposed algorithm.     

Application of spherical-rod float image velocimetry for evaluating high flow rate in mountain rivers

Spherical-rod float image velocimetry (SFIV) is a convenient technique combining the positive functions of a rod float velocimetry (RFV) and large-scale particle image velocimetry (LSPIV) for measuring high flow rate in mountain rivers. The SFIV is the principle that the sphere allowing little image distortion according to the orientation is used as a floating tracer for LSPIV. The drifting distances of a spherical-rod float were calculated by geometrical interpretation of spherical images recorded in an experimental open channel and mountain rivers. The depth-reflecting velocities estimated by SFIV in the rivers as in the open channel coincided approximately with the velocities by visual observation from river bank despite of the long shooting distance, weather impact, and flow complicated by topography and bed materials. The velocity coefficients obtained from the experimental channel were used to evaluate depth-averaged velocity for river discharges. The high discharges estimated by SFIV in mountain rivers distributed mostly within the range of the rating curve established by RFV. The results show that the safe and efficient SFIV is a highly applicable technique in mountain rivers with the high flow rate and complex flow. In order to practically use SFIV in mountain rivers, additional studies are required for velocity coefficients depending on the water depth and draft.     

Deep particle image velocimetry supervised learning under light conditions

Particle image velocimetry (PIV) is an important fluid visualization technology which extracts the velocity field from two successive particle images. Recently, some researchers have begun to use convolutional neural network (CNN) to tackle the PIV problem successfully. Some supervised learning methods make use of the PIV dataset with ground truth for network training. However, the existing dataset is composed of pairs of particle images under ideal light conditions and does not take into account the changes in actual experimental conditions. In this paper, we firstly generated a new and more challenging dataset called Light-PIV which fully simulates the change of the brightness of particle images in the real PIV experiment. Secondly, we present here a novel approach for fluid motion estimation which is based on an optical flow network LiteFlowNet. The proposed approach is verified by the application to a diversity of synthetic and experimental PIV images. We not only improve the structure, but also combine the traditional prior assumptions knowledge with the loss function to better guide the network training. The proposed approach is verified by the application to a diversity of synthetic and experimental PIV images. The experimental results show that our proposed method has advantages of high accuracy, obtaining detailed information and strong robustness in our PIV dataset compared with classical PIV methods such as HS optical flow and WIDIM, and even outperforms these existing approaches in some flow cases.     

Recent applications of particle image velocimetry in aerodynamic research

Particle image velocimetry (PIV) is increasingly used to investigate unsteady velocity fields instantaneously. For the first time the PIV technique allows the recording of a complete velocity field in a plane of the flow within a few microseconds. The PIV technique thereby provides information about unsteady flow fields which is difficult to obtain with other experimental techniques. The short acquisition times and fast availability of data reduce the operational time, and hence cost, in large scale wind tunnels and test facilities.

At DLR a variety of PIV systems for use in industrial wind tunnels has been developed in the past decade. The flexibility of these portable systems is illustrated by presenting several results of recent PIV applications. More recently the original photographic means of PIV image recording has been partially replaced by high resolution electronic imaging which can provide PIV data nearly on-line. Images recorded by either system use the same multiple-pass, cross-correlation analysis software, whose algorithms are briefly described. Several examples of actual applications are given: the flow issuing from a jet nozzle was imaged by a specially developed high-speed video camera at close proximity. A high resolution dual-frame digital camera was applied in the study of helicopter rotor aerodynamics and wake vortex measurements of an airplane model. Further, large image sequences exceeding 100 PIV recordings provided detailed information on the structure of a turbulent boundary layer.     

Particle image velocimetry investigations on multiphase flow in fluidized beds: A review

Fluidized Beds (FBs) are widely employed in the petroleum and coal energy sector because they offer excellent contact, both in terms of high surface area and long times. The last two decades has seen measurement on multiphase flows shift from conventional pressure sensors to direct flow image acquisition and processing. Particle Image Velocimetry or PIV, and PIV coupled with Digital Image Analysis or DIA, are used to directly and instantaneously acquire flow field data to make hidden flow patterns and flow structures discoverable. Research abounds on Gas-Solid FB hydrodynamics using PIV, but Liquid-Solid and Gas-Liquid-Solid systems are only slowly catching up. Similarly, the use of Geldart B and D particles for such studies is very common, whereas A and C type particle hydrodynamics is as yet largely unexplored by using imaging. Turbulence, high temperature, particle clusters, particle agglomeration and dense particle flows pose particular challenges to using PIV in FB. The two-zone FB and micro-FB warrant further attention. Small sized A & C type particles of rod-like, plate-like and angular shape provide huge scope for PIV investigations on FBs in the future. This review provides a concise account of several PIV studies on all types of FBs with focus on the past two decades, and also details the limitations of PIV measurements with future scope of work.     

Statistical characterization of the interfacial behavior of the sub-regimes in gas-liquid stratified two-phase flow in a horizontal pipe

The aim of the present study was to identify the interfacial behavior of the sub-regime of gas-liquid stratified two phase flow by using the pressure differential signal data. Here, the probability distribution function (PDF), power spectral density (PSD), kolmogorov entropy and discrete wavelet transform (DWT) were used to analyze the differential pressure signals. The indicators of each flow sub regime were analyzed on the basis of the quantitative values of the statistical curves, which were also validated by visual observation of the video images. The results indicated that there are six identified sub-regimes of stratified flow namely stratified smooth (S), two-dimensional (2-D) wave, three-dimensional (3-D) wave, roll wave (RW), entrained droplet + disturbance wave (ED + DW) and pseudo slug (PS). Next, the increase of liquid viscosity will shift the transition line from the RW to ED + DW to a lower both of J_L and J_G. The increase of the liquid viscosity provides a stabilizing effect to reduce the chaos of the pressure gradient fluctuation. For the RW and the ED + DW sub-regime, the increase of the liquid viscosity shifts the wavelet energy to a larger scale and lower frequency. For the PS sub-regime, the increase of liquid viscosity shifts the wavelet energy to a smaller scale with a higher frequency. For the RW sub-regime, the increase of J_G will increase the wavelet energy at the small-scale and high-frequency decomposition levels.     

3D Particle image velocimetry test of inner flow in a double blade pump impeller

The double blade pump is widely used in sewage treatment industry,however,the research on the internal flow characteristics of the double blade pump with particle image velocimetry(PIV) technology is very little at present.To reveal inner flow characteristics in double blade pump impeller under off-design and design conditions,inner flows in a double blade pump impeller,whose specific speed is 111,are measured under the five off-design conditions and design condition by using 3D PIV test technology.In order to ensure the accuracy of the 3D PIV test,the external trigger synchronization system which makes use of fiber optic and equivalent calibration method are applied.The 3D PIV relative velocity synthesis procedure is compiled by using Visual C++ 2005.Then absolute velocity distribution and relative velocity distribution in the double blade pump impeller are obtained.Test results show that vortex exists in each condition,but the location,size and velocity of vortex core are different.Average absolute velocity value of impeller outlet increases at first,then decreases,and then increases again with increase of flow rate.Again average relative velocity values under 0.4,0.8,and 1.2 design condition are higher than that under 1.0 design condition,while under 0.6 and 1.4 design condition it is lower.Under low flow rate conditions,radial vectors of absolute velocities at impeller outlet and blade inlet near the pump shaft decrease with increase of flow rate,while that of relative velocities at the suction side near the pump shaft decreases.Radial vectors of absolute velocities and relative velocities change slightly under the two large flow rate conditions.The research results can be applied to instruct the hydraulic optimization design of double blade pumps.     

On sampling bias in multiphase flows: Particle image velocimetry in bubbly flows

Measuring the liquid velocity and turbulence parameters in multiphase flows is a challenging task. In general, measurements based on optical methods are hindered by the presence of the gas phase. In the present work, it is shown that this leads to a sampling bias. Here, particle image velocimetry (PIV) is used to measure the liquid velocity and turbulence in a bubble column for different gas volume flow rates. As a result, passing bubbles lead to a significant sampling bias, which is evaluated by the mean liquid velocity and Reynolds stress tensor components. To overcome the sampling bias a window averaging procedure that waits a time depending on the locally distributed velocity information (hold processor) is derived. The procedure is demonstrated for an analytical test function. The PIV results obtained with the hold processor are reasonable for all values. By using the new procedure, reliable liquid velocity measurements in bubbly flows, which are vitally needed for CFD validation and modeling, are possible. In addition, the findings are general and can be applied to other flow situations and measuring techniques.     

Identification of gas-liquid two-phase flow patterns in a horizontal pipe based on ultrasonic echoes and RBF neural network

This paper proposes a novel flow pattern identification method using ultrasonic echo signals within the pipe wall. A two-dimensional acoustic pressure numerical model is established to investigate the ultrasonic pulse transmission behavior between the wall-gas and wall-liquid interface. Experiments were also carried out at a horizontal air-water two-phase flow loop to measure the ultrasonic echo pulse signals of stratified flow, slug flow, and annular flow. It is interesting to find that the attenuation of the ultrasonic pulse at the wall-liquid interface is faster than the attenuation at the wall-gas interface. An RBF neural network is constructed for online flow pattern identification. The normalized envelop area and the area ratios of the echo spectrum are selected as the input parameters. The results show that the stratified flow, slug flow, and annular flow can be identified with an accuracy of 94.0%.     

Analysis of two-phase flow regimes and parameter distribution in 5 x 5 rod bundle using image processing techniques

With the recent developments in image processing and analysis, this paper presents bubble characteristics distribution in adiabatic air-water two-phase flow through a 5 × 5 rod bundle. The experiment covered water superficial velocities (J_l = 0.012 m/s – 0.421 m/s) and air superficial velocities (J_g = 0.042 m/s – 0.987 m/s) in which three distinct flow regimes were identified. The flow regime map was compared with existing flow regime transition criteria for vertical rod bundles. Distinct features from the two-phase flow images were extracted to train a classifier model to distinguish between regimes from a separate experiment. The model distinguishes between the bubbly flow regime and others accurately. The void fraction and velocity distributions were also extracted from the R–CCN masked images. Bubble-induced turbulence that was dominant in the subchannel at (J_l = 0.28 m/s) shifted to the outer subchannels and gaps when the flow rate increased (J_l = 0.42 m/s). These methods over-predicted the void faction around the surfaces of the inner rods.     

河流水面成像测速研究进展

大尺度粒子图像测速(LSPIV)是一种用于测量大面积(数百平方米)水体表面流速的非接触式全场流速测量技术。不仅可用于常规条件下明渠紊动特性和时均特性的研究,更具有极端条件下河道水流监测的应用潜力。然而受复杂现场环境的影响,该技术在天然河流的应用中面临着诸多挑战。从水流示踪方式、流场图像采集、水面目标增强、运动矢量估计、时均流场重建、水面流场定标及不确定度评估七个方面综述了国内外对河流水面成像测速(RSIV)方法的研究进展。通过对现有方法的总结和讨论,提出改进技术方法的研究需求,以使其成为水动力学和水文学中有力的测量工具。     

Prediction of gas-liquid two-phase choke flow using Gaussian process regression

In the process of shale gas production, it is of great significance to select an appropriate mathematical method to accurately predict the gas flow rate of wellhead choke for the rational formulation of shale gas well production plan. Gas-liquid two-phase flow occurs in most of the time from the flowback to the production period in shale gas wells. Wellhead chokes play key roles in regulating the flowing rates of both the flowback fluid and shale gas. Therefore, it is important to study the law of two-phase choke flow clearly so as to accurately predict gas flow rate through wellhead chokes. Up to now, previous studies have proposed a variety of applicable empirical methods, including Gilbert-type correlation (GC), artificial neural network (ANN) and support vector machine (SVM). The analysis of training data and the establishment of accurate prediction models determine the accuracy of prediction. In this study, Gaussian process regression (GPR) was adopted to learn and predict the behavior of gas-liquid two-phase flow through wellhead chokes, and huge amounts of data collected from Chuannan Shale gas wells were used to verify the effectiveness of the GPR method. The prediction accuracy of the GPR method was compared with those of other methods like GC, ANN and SVM. In addition, we also compared the prediction accuracy of different kernel functions to select the best kernel function for GPR. The kernel functions considered are exponential function, squared exponential function, rational quadratic function and Matérn function. The results showed that GPR method is accurate and applicable for analyzing the behavior of gas-liquid two-phase flow through wellhead chokes, and GPR method with exponential kernel function could achieve greater prediction accuracy than other kernel functions.     

微球测速聚类分析的流式液路稳定性评估

提出了一种基于90°Mie散射的高速图像采集微球测速方法,用于准确评估流式细胞仪流动室内的层流状态及单细胞流的稳定性。利用流动室内微球速度的稳定性对流动室内单细胞流的稳定性进行了评估。首先,利用高速显微成像系统采集90°Mie散射光的图像,选取90°侧向散射光以避免激发光源直射光的干扰,同时去除背景光源并提高图像对比度;然后,利用基于梯形白化权函数的灰色聚类分析方法对微球拖尾图像进行分类,实现对不足、正常、衍射和重叠4种情况的准确分类;最后,利用中点法确定正常图像上升沿及下降沿的边界,提高拖尾长度计算的准确性。搭建了高速微球测速实验系统,对本文方法进行验证。结果表明,该方法能够获得清晰的微球拖尾图像并对微球拖尾图像进行准确分类。对本文实验系统测得的微球拖尾长度平均值为116.9个像素点,标准差为1.7。     

粒子图像测速（PIV）技术在烟丝流量检测中的研究

粒子图像测速技术（PIV）是一种全新的非接触式的,瞬时的,全场流速测量方法,广泛应用于流体力学中。本研究将PIV技术引入烟丝运动研究是一个尝试,旨在为烟丝流量的检测提供一个新的测量手段。通过分析烟丝在风送管道内的运动,利用PIV技术并结合PTV技术,采用图像处理得到烟丝的运动速度。粒子图像测速技术具有一定的优势,可以克服传统测量手段的不足。     

频域时空图像测速法的图像滤波器敏感性分析

时空图像测速法是以河流表面图像中测速线为分析区域、通过检测合成时空图像的纹理主方向计算得到一维时均流速的测量方法,具有空间分辨率高、实时性强的特点。在实际应用中纹理主方向的检测精度难免会受到水面紊流、倒影、耀光、障碍物、降雨等环境扰动的影响,导致测量出现粗大误差。频域滤波技术是一种抑制噪声的有效方法,能显著提高时空图像的纹理清晰度。但现有研究在滤波器参数的敏感性分析方面存在不足,使得该方法的适用性受限。对此通过在水文站搭建在线视频测流系统采集了不同条件的河流水面视频数据,分析了6种典型场景下时空图像的空域及频域特性,进而开展了频域扇形滤波器方向角、通带夹角及半径参数的敏感性分析实验。实验结果表明：采用提出的椭圆形积分区域检测方向角优于现有的单像素宽直线;当设置通带夹角为±5.3°且半径为R/2时,滤波器在上述场景下均能有效地滤除噪声干扰。使得时空图像纹理主方向的检测精度在正常场景下达到0.1°,在复杂含噪场景下控制在0.5°以内,表面流速测量的相对误差小于6.2%。     

PIV measurements of the time-averaged flow velocity downstream of flow conditioners in a pipeline

The flow downstream of three different flow conditioners, a tube bundle and two perforated plates, was investigated by measuring the time-averaged, axial velocity component with Particle Image Velocimetry (PIV). The conditioners were exposed to the flow disturbed by a 90° out-of-plane double-bend. The experiments were performed with air flow through a pipeline of 100 mm i.d. and at Reynolds numbers between 100 000 and 200 000. The axial development of the velocity profiles, without and with conditioner, is documented, and the performance of the three devices in conditioning the disturbed flow can be compared. Particular attention is given to the determination of time-averaged velocity values by means of PIV.