Stereoscopic PIV on multiple color-coded light sheets and its application to axial flow in flapping robotic insect wings

Non-scanning volume flow measurement techniques such as 3D-PTV, holographic and tomographic particle image velocimetry (PIV) permit reconstructions of all three components (3C) of velocity and vorticity vectors in a fluid volume (3D). In this study, we present a novel 3D3C technique termed Multiple-Color-Plane Stereo Particle-Image-Velocimetry (color PIV), which allows instantaneous measurements of 3C velocity vectors in six parallel, colored light sheets. We generated the light sheets by passing white light of two strobes through dichroic color filters and imaged the slices by two 3CCD color cameras in Stereo-PIV configuration. The stereo-color images were processed by custom software routines that sorted each colored fluid particle into one of six gray-scale images according to its hue, saturation, and luminance. We used conventional Stereo PIV cross-correlation algorithms to compute a 3D planar vector field for each light sheet and subsequently interpolated a volume flow map from the six vector fields. As a first application, we quantified the wake and axial flow in the vortical structures of a robotic insect (fruit fly) model wing. In contrast to previous findings, the measured data indicate strong axial flow components on the upper wing surface, including axial flow in the leading-edge vortex core. Collectively, color PIV is robust against mechanical misalignments, avoids laser safety issues, and computes instantaneous 3D vector fields in a fraction of the time typical for other 3D systems. Color PIV might thus be of value for volume measurements of highly unsteady flows.     

Improvement of measurement accuracy in micro PIV by image overlapping

Micro PIV uses volume illumination; therefore, the velocity measured at the focal plane is a weighted average of the velocities within the measurement volume. The contribution of out-of-focus particles to the PIV correlation can generate significant measurement errors particularly in near wall regions. We present a new application of image overlapping, which is shown to be very effective in improving the accuracy of time-averaged velocity measurements by effectively reducing the measurement depth. The performance of image overlapping and correlation averaging were studied using synthetic and experimental images of micro channel flow, both with and without image pre-processing. The results show that for flows without particle clumping, image overlapping provides the best measurement accuracy without any need for image pre-processing. For flows with particle clumping, image overlapping combined with band-pass filtering provides the best measurement accuracy. When overlapped images are saturated with particles due to a large number of image pairs, image overlapping measurement still does not show any visible pixel-locking effect. Image overlapping was found to have comparable or slightly reduced pixel-locking effects compared to correlation averaging. In addition, image overlapping utilizes significantly fewer computational resources than the other techniques.     

3D particle streak velocimetry by defocused imaging

Particle streak velocimetry (PSV) has become one of the important branches of flow filed measurements. It extracts velocity information from particle trajectories captured by single frame long exposure images. Since the defocus of moving particle is inevitable during a long exposure time and under a large magnification, a novel three-dimensional (3D) velocity measurement method named defocusing particle streak velocimetry (DPSV) is proposed in this paper. On the one hand, an extension from two-dimensional (2D) to 3D velocity measurement with a monocular system is carried out. The depth information of the particle, which reflects the position in the third dimension, is indicated by the defocusing degree (characteristic parameter σ) of the particle images. The variation of σ along the trajectory is recognized by surface fitting of the gray value distribution of particle images, assuming that σ varies linearly along the trajectory. On the other hand, based on the linear fitting for the straight trajectory, an arc fitting model is developed for curved trajectories which are commonly captured in turbulent flow. The relationship between σ and the particle depth position z is experimentally calibrated using a LED light and a diaphragm. Finally, the DPSV method is verified in a submerged jet flow field as well as in a microchannel flow field to obtain the 3D velocity field with single monocular system.     

A method for automatic estimation of instantaneous local uncertainty in particle image velocimetry measurements

The uncertainty of any measurement is the interval in which one believes the actual error lies. Particle image velocimetry (PIV) measurement error depends on the PIV algorithm used, a wide range of user inputs, flow characteristics, and the experimental setup. Since these factors vary in time and space, they lead to nonuniform error throughout the flow field. As such, a universal PIV uncertainty estimate is not adequate and can be misleading. This is of particular interest when PIV data are used for comparison with computational or experimental data. A method to estimate the uncertainty from sources detectable in the raw images and due to the PIV calculation of each individual velocity measurement is presented. The relationship between four error sources and their contribution to PIV error is first determined. The sources, or parameters, considered are particle image diameter, particle density, particle displacement, and velocity gradient, although this choice in parameters is arbitrary and may not be complete. This information provides a four-dimensional “uncertainty surface” specific to the PIV algorithm used. After PIV processing, our code “measures" the value of each of these parameters and estimates the velocity uncertainty due to the PIV algorithm for each vector in the flow field. The reliability of our methodology is validated using known flow fields so the actual error can be determined. Our analysis shows that, for most flows, the uncertainty distribution obtained using this method fits the confidence interval. An experiment is used to show that systematic uncertainties are accurately computed for a jet flow. The method is general and can be adapted to any PIV analysis, provided that the relevant error sources can be identified for a given experiment and the appropriate parameters can be quantified from the images obtained.     

粒子图像测速技术研究进展

粒子图像测速技术(PIV)作为一种全新的无扰、瞬态、全场速度测量方法,在流体力学及空气动力学研究领域具有极高的学术意义和实用价值.本文对PIV技术的原理、分类作了简要地介绍,详细归纳和评述了现有的各种速度信息的提取方法,并对拓扑图论、神经网络、遗传算法、模糊聚类等新技术在PIV中的应用以及三维PIV技术、两相流PIV测试技术进行了介绍.指出当前PIV技术除了向三维和多相流方向发展外,如何提高PIV的测量精度以及缩短计算时间仍然是目前研究的主要目标.PIV技术随着计算机技术、激光技术和CCD性能的发展,必将取得更大的发展与突破      

Evaluation of aero-optical distortion effects in PIV

Aero-optical distortion effects on the accuracy of particle image velocimetry (PIV) are investigated. When the illuminated particles are observed through a medium that is optically inhomogeneous due to flow compressibility, the resulting particle image pattern is subjected to deformation and blur. In relation to PIV two forms of error can be identified: position error and velocity error. In this paper a model is presented that describes these errors and particle image blur in relation to the refractive index field of the flow. In the case of 2D flows the model equations can be simplified and, furthermore, the background oriented schlieren technique (BOS) can be applied as a means to assess and correct for the optical error in PIV. The model describing the optical distortion is validated by both computer simulation and real experiments of 2D flows. Two flow features are considered: one with optical distortion normal to the velocity (shear layer) and one with optical distortion in the direction of the flow (expansion fan). Both simulation and experiments demonstrate that the major source for the velocity error is the second derivative of the refractive index in the direction of the velocity vector. The aero-optical distortion effect is less critical for shearing interfaces in comparison with compression/expansion fronts, the most critical case being represented by shock waves. Based on the results from the simulated experiments, it is concluded that for the 2D flow case the BOS method allows a measurement of the mean velocity error in PIV and can reduce it to a large extent.     

A generalized processing technique in digital particle image velocimetry with direct estimation of velocity gradients

A technique is proposed for the processing of digital particle image velocimetry (PIV) images, in one single step providing direct estimates of fluid velocity, out-of-plane vorticity and in-plane shear rate tensor. The method is based on a generalization of the standard PIV cross-correlation technique and substitutes the usual discrete cross-correlation of image pairs with a correlation of interpolated two-dimensional image intensity functions, being subject to affine transformations. The correlation is implemented by using collocation points, on which image intensity values are interpolated. The resulting six-dimensional correlation function is maximized using a general purpose optimization algorithm. The use of the method is demonstrated by application to different types of synthetically generated image pairs constructed with known particle displacement functions. The resulting errors are assessed and compared with those of a representative standard PIV method as well as with those of the present technique using no differential quantities in the search of the peak location. The examples demonstrate that significant improvements in accuracy can be obtained for flow fields with regions containing strong velocity gradients.     

Interfacial PIV to resolve flows in the vicinity of curved surfaces

PIV measurements near a wall are generally difficult due to low seeding density, low velocity, high velocity gradient, and strong reflections. Such problems are often compounded by curved boundaries, which are commonly found in many industrial and medical applications. To systematically solve these problems, this paper presents two novel techniques for near-wall measurement, together named Interfacial PIV, which extracts both wall-shear gradient and near-wall tangential velocity profiles at one-pixel resolution. To deal with curved walls, image strips at a curved wall are stretched into rectangles by means of conformal transformation. To extract the maximal spatial information on the near-wall tangential velocity field, a novel 1D correlation function is performed on each horizontal pixel line of the transformed image template to form a “correlation stack”. This 1D correlation function requires that the wall-normal displacement component of the particles be smaller than the particle image diameter in order to produce a correlation signal. Within the image regions satisfying this condition, the correlation function yields peaks that form a tangential velocity profile. To determine this profile robustly, we propose to integrate gradients of tangential velocity outward from the wall, wherein the gradient at each wall-normal position is measured by fitting a straight line to the correlation peaks. The capability of Interfacial PIV was validated against Particle Image Distortion using synthetic image pairs generated from a DNS velocity field over a sinusoidal bed. Different velocity measurement schemes performed on the same correlation stacks were also demonstrated. The results suggest that Interfacial PIV using line fitting and gradient integration provides the best accuracy of all cases in the measurements of velocity gradient and velocity profile near wall surfaces.     

Tomographic particle image velocimetry

This paper describes the principles of a novel 3D PIV system based on the illumination, recording and reconstruction of tracer particles within a 3D measurement volume. The technique makes use of several simultaneous views of the illuminated particles and their 3D reconstruction as a light intensity distribution by means of optical tomography. The technique is therefore referred to as tomographic particle image velocimetry (tomographic-PIV). The reconstruction is performed with the MART algorithm, yielding a 3D array of light intensity discretized over voxels. The reconstructed tomogram pair is then analyzed by means of 3D cross-correlation with an iterative multigrid volume deformation technique, returning the three-component velocity vector distribution over the measurement volume. The principles and details of the tomographic algorithm are discussed and a parametric study is carried out by means of a computer-simulated tomographic-PIV procedure. The study focuses on the accuracy of the light intensity field reconstruction process. The simulation also identifies the most important parameters governing the experimental method and the tomographic algorithm parameters, showing their effect on the reconstruction accuracy. A computer simulated experiment of a 3D particle motion field describing a vortex ring demonstrates the capability and potential of the proposed system with four cameras. The capability of the technique in real experimental conditions is assessed with the measurement of the turbulent flow in the near wake of a circular cylinder at Reynolds 2,700.     

A kilohertz frame rate cinemagraphic PIV system for laboratory-scale turbulent and unsteady flows

A kilohertz frame rate cinemagraphic particle image velocimetry (PIV) system has been developed for acquiring time-resolved image sequences of laboratory-scale gas and liquid-phase turbulent flows. Up to 8000 instantaneous PIV images per second are obtained, with sequence lengths exceeding 4000 images. The two-frame cross-correlation method employed precludes directional ambiguity and has a higher signal-to-noise ratio than single-frame autocorrelation or cross-correlation methods, facilitating acquisition of long uninterrupted sequences of valid PIV images. Low and high velocities can be measured simultaneously with similar accuracy by adaptively cross-correlating images with the appropriate time delay. Seed particle illumination is provided by two frequency-doubled Nd:YAG lasers producing Q-switched pulses at the camera frame rate. PIV images are acquired using a 16 mm high-speed rotating prism camera. Frame-to-frame registration is accomplished by imaging two pairs of crossed lines onto each frame and aligning the digitized image sequence to these markers using image processing algorithms. No flow disturbance is created by the markers because only their image is projected to the PIV imaging plane, with the physical projection device residing outside the flow field. The frame-to-frame alignment uncertainty contributes 2% to the overall velocity measurement uncertainty, which is otherwise comparable to similar film-based PIV methods. Received: 11 July 2000 / Accepted: 21 June 2001 Published online: 29 November 2001     

PIV measurements of flow in drying polymer solutions during solvent casting

An experimental method based on confocal microscopy and particle image velocimetry (PIV) is used to characterize the flow in a polymer solution during solvent casting. The flow inside a 200-μm-thick film of a poly(vinyl alcohol) (PVA) solution is visualized near a vertical wall of a mold using confocal microscopy of seed particles during solvent evaporation at 25, 35, and 45°C, and the corresponding velocity vector fields are determined from projections of the confocal images. Flow toward the vertical wall is observed inside the film as well as a slower Marangoni-type counter flow at the film surface during the initial phase of solvent evaporation, resulting from a polymer concentration gradient along the film due to a local variation in evaporation rate. Total volume of the polymer solution in the observation volume as well as solvent evaporation rate are determined as a function of time, both revealing close correlation to average horizontal velocity data from PIV. The PIV measurements show significant differences in the flow velocity fields at different temperatures. The PIV measurements correlate with the solvent evaporation rates as well as the final polymer thicknesses on the vertical wall of the mold. Surface tension and viscosity measurements are taken for different concentrations of PVA solution.     

Wavefront sensing for single view three-component three-dimensional flow velocimetry

We present the application of wavefront sensing to particle image velocimetry for three-component (3C), three-dimensional (3D) flow measurement from a single view. The technique is based upon measuring the wavefront scattered by a tracer particle and from that wavefront the 3D tracer location can be determined. Hence, from a temporally resolved sequence of 3D particle locations the velocity vector field is obtained. Two approaches to capture the data required to measure the wavefronts are described: multi-planar imaging using a distorted diffraction grating and an anamorphic technique. Both techniques are optically efficient, robust and compatible with coherent and incoherent scattering from flow tracers. The depth (range) resolution and repeatability have been quantified experimentally using a single mode fiber source representing a tracer particle. The anamorphic approach is shown to have the greatest measurement range and hence was selected for the first proof of principle experiments using this technique for 3D particle imaging velocimetry (PIV) on a sparsely seeded gas phase flow.     

Power-filter technique for modifying depth of correlation in microPIV experiments

A new image filtering method, termed power-filtering, is proposed for use in microscopic particle image velocimetry (PIV) to control the depth of correlation, independent of the image acquisition system, particle size, and flow characteristics. An analytical model of the depth of correlation for the filtered images is developed and verified with a series of careful experiments. This model predicts that the depth of correlation can be increased or decreased by a factor of two by applying power-filter values of 0.63 and 2.0, respectively. Experiments show that the analytical model for the power filtering technique is generally accurate to within the measurement uncertainty.     

Visual hull method for tomographic PIV measurement of flow around moving objects

Tomographic particle image velocimetry (PIV) is a recently developed method to measure three components of velocity within a volumetric space. We present a visual hull technique that automates identification and masking of discrete objects within the measurement volume, and we apply existing tomographic PIV reconstruction software to measure the velocity surrounding the objects. The technique is demonstrated by considering flow around falling bodies of different shape with Reynolds number?~1,000. Acquired image sets are processed using separate routines to reconstruct both the volumetric mask around the object and the surrounding tracer particles. After particle reconstruction, the reconstructed object mask is used to remove any ghost particles that otherwise appear within the object volume. Velocity vectors corresponding with fluid motion can then be determined up to the boundary of the visual hull without being contaminated or affected by the neighboring object velocity. Although the visual hull method is not meant for precise tracking of objects, the reconstructed object volumes nevertheless can be used to estimate the object location and orientation at each time step.     

Large-scale tomographic particle image velocimetry using helium-filled soap bubbles

To measure large-scale flow structures in air, a tomographic particle image velocimetry (tomographic PIV) system for measurement volumes of the order of one cubic metre is developed, which employs helium-filled soap bubbles (HFSBs) as tracer particles. The technique has several specific characteristics compared to most conventional tomographic PIV systems, which are usually applied to small measurement volumes. One of them is spot lights on the HFSB tracers, which slightly change their position, when the direction of observation is altered. Further issues are the large particle to voxel ratio and the short focal length of the used camera lenses, which result in a noticeable variation of the magnification factor in volume depth direction. Taking the specific characteristics of the HFSBs into account, the feasibility of our large-scale tomographic PIV system is demonstrated by showing that the calibration errors can be reduced down to 0.1 pixels as required. Further, an accurate and fast implementation of the multiplicative algebraic reconstruction technique, which calculates the weighting coefficients when needed instead of storing them, is discussed. The tomographic PIV system is applied to measure forced convection in a convection cell at a Reynolds number of 530 based on the inlet channel height and the mean inlet velocity. The size of the measurement volume and the interrogation volumes amount to 750 mm × 450 mm × 165 mm and 48 mm × 48 mm × 24 mm, respectively. Validation of the tomographic PIV technique employing HFSBs is further provided by comparing profiles of the mean velocity and of the root mean square velocity fluctuations to respective planar PIV data.     

Measurement of fully-developed turbulent pipe flow with digital particle image velocimetry

A new and unique high-resolution image acquisition system for digital particle image velocimetry (DPIV) in turbulent flows is used for the measurement of fully-developed turbulent pipe flow at a Reynolds number of 5300. The flow conditions of the pipe flow match those of a direct numerical simulation (DNS) and of measurements with conventional (viz., photographic) PIV and with laser-Doppler velocimetry (LDV). This experiment allows a direct and detailed comparison of the conventional and digital implementations of the PIV method for a non-trivial unsteady flow. The results for the turbulence statistics and power spectra show that the level of accuracy for DPIV is comparable to that of conventional PIV, despite a considerable difference in the interrogation pixel resolution, i.e. 32 × 32 (DPIV) versus 256 × 256 (PIV). This result is in agreement with an earlier analytical prediction for the measurement accuracy. One of the advantages of DPIV over conventional PIV is that the interrogation of the DPIV images takes only a fraction of the time needed for the interrogation of the PIV photographs.     

Digital particle image velocimetry

Digital particle image velocimetry (DPIV) is the digital counterpart of conventional laser speckle velocitmetry (LSV) and particle image velocimetry (PIV) techniques. In this novel, two-dimensional technique, digitally recorded video images are analyzed computationally, removing both the photographic and opto-mechanical processing steps inherent to PIV and LSV. The directional ambiguity generally associated with PIV and LSV is resolved by implementing local spatial cross-correlations between two sequential single-exposed particle images. The images are recorded at video rate (30 Hz or slower) which currently limits the application of the technique to low speed flows until digital, high resolution video systems with higher framing rates become more economically feasible. Sequential imaging makes it possible to study unsteady phenomena like the temporal evolution of a vortex ring described in this paper. The spatial velocity measurements are compared with data obtained by direct measurement of the separation of individual particle pairs. Recovered velocity data are used to compute the spatial and temporal vorticity distribution and the circulation of the vortex ring.     

On the effect of particle image intensity and image preprocessing on the depth of correlation in micro-PIV

The depth of correlation (DOC) is an experimental parameter, introduced to quantify the thickness of the measurement volume and thus the depth resolution in microscopic particle image velocimetry (μPIV). The theory developed to estimate the value of the DOC relies on some approximations that are not always verified in actual experiments, such as a single thin-lens optical system. In many practical μPIV experiments, a deviation of the actual DOC from its nominal value can be expected, due for instance to additional components present in the optical path of the microscope or to the use of image preprocessing before the PIV evaluation. In the presented paper, the effect of real particle image intensity distribution and image preprocessing on the thickness of the measurement volume is investigated. This is performed studying the defocusing of tracer particles and the DOC-related bias error present in μPIV measurements in a Poiseuille flow. The analysis shows that the DOC predicted using the conventional formulas can be significantly smaller than its actual value. To overcome this problem, the use of an effective NA determined experimentally from the curvature of the image autocorrelations is proposed. The accuracy of this approach to properly predict the actual size of DOC is discussed and validated on the experimental data. The effectiveness of image preprocessing to reduce the DOC-related bias error is tested and discussed as well.     

Volumetric-correlation PIV to measure particle concentration and velocity of microflows

Volumetric-correlation particle image velocimetry (VPIV) is a new technique that provides a 3-dimensional 2-component velocity field from a single image plane. This single camera technique is simpler and cheaper to implement than multi-camera systems and has the capacity to measure time-varying flows. Additionally, this technique has significant advantages over other 3D PIV velocity measurement techniques, most notably in the capacity to measure highly seeded flows. Highly seeded flows, often unavoidable in industrial and biological flows, offer considerable advantages due to higher information density and better overall signal-to-noise ratio allowing for optimal spatial and temporal resolution. Here, we further develop VPIV adding the capability to measure concentration and increasing the robustness and accuracy of the technique. Particle concentrations are calculated using volumetric auto-correlations, and subsequently the velocities are calculated using volumetric cross-correlation corrected for variations in particle concentration. Along with the ability to calculate the particle concentration profile, our enhanced VPIV produces significant improvement in the accuracy of velocity measurements. Furthermore, this technique has been demonstrated to be insensitive to out-of-plane flows. The velocity measurement accuracy of the enhanced VPIV exceeds that of standard micro-PIV measurements, especially in near-wall regions. The 3D velocity and particle-concentration measurement capability of VPIV are demonstrated using both synthetic and experimental results.     

Three-component velocity field measurement in confined liquid flows with high-speed digital image plane holography

In the last years, several techniques have been developed for the measurement of the three velocity components in a fluid plane or volume. Techniques as stereoscopic particle image velocimetry (SPIV) or tomographic PIV need a complex set-up and present serious restrictions when applied to confined liquid flows. Other like digital holographic PIV has some limitations in the particle concentration that can be measured. In this work, high-speed digital image plane holography has been applied for the measurement of the three velocity components in a complex geometry brain aneurysm model, using a two-cavity high-speed laser, one double frame camera and normal visualization, like in regular PIV. A portable and compact system has been built for adapting the high-speed laser short coherence length to the measurement of larger areas.