Particle Image Velocimetry Data Research Articles

Identification of free-stream and boundary layer correlating events in free-stream turbulence-induced transition

Boundary layer receptivity to free-stream disturbances plays a crucial role in forming coherent structures, whose breakup drives the laminar to turbulent flow transition. In the present work, an extended proper orthogonal decomposition (E-POD) procedure is applied to particle image velocimetry (PIV) data to identify correlating events between the free-stream velocity field and transitional boundary layers for flow configurations typical of low-pressure turbine blades. Data collected in two wall-parallel planes were ordered along the homogeneous spanwise coordinate so that the dominant POD coefficients provide the most energetic spanwise wavelengths in the free-stream and the near-wall regions. Then, the cross-correlation matrix of the POD spanwise coefficients computed independently in both measuring planes directly identifies the free-stream scales showing the highest degree of correlation with the boundary layer structures. Low-order reconstructions of the original PIV data show that the most correlating events are directly linked to the formation and the successive breakup process of streaky structures. Otherwise, larger-scale structures which are not involved in the transition process are filtered out. Interestingly, free-stream disturbances appear as organized wave packets with significant elongation in the streamwise direction when the velocity fields are reconstructed considering only the most correlating modes. The effect due to the Reynolds numbers, the pressure gradient, and the free-stream turbulence variation on the free-stream modes affecting the formation of coherent structures in the boundary layer is discussed in the paper.

Flow Transition Analysis of Multiple Jets in Narrow Parallel Walls using POD and DMD

We experimentally and numerically study the cavity oscillation of a compound jet in narrow parallel plates. Particle Image Velocimetry (PIV) obtains the time series velocity distribution with four jets. To perform the mode decomposition of the flow field, Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) are applied to the PIV data. The POD analysis reveals that the cavity oscillation involves two hierarchical structures: one corresponds to the cavity-scale oscillating of the jet, and the other to the advecting multiple smaller vortices. The DMD analysis classifies the oscillation modes into three categories: (i) oscillation mode on the container scale, (ii) pulsating mode on the container scale, and (iii) oscillation modes of individual jets. The results suggest that the superposition of these oscillation modes at the high flow rate is observed as cavity oscillation.

Two-time interval method to circumvent particle image velocimetry dynamic range

In the velocity range outside the velocity-dynamic range of a PIV (particle image velocimetry) system, the velocity measurements are known to result in noisy velocity vectors due to erroneous detection of correlation peaks, especially in the low-velocity region. Here a simple method is proposed that uses two different timings of a pulsed laser, in conjunction with a median-test of residuals based on the velocity data, to resolve both low and high velocities in a flow field. This method detects the erroneous vectors and replaces them with the correct vectors via a comparison of two sets of PIV data, procured simultaneously with an appropriate time-delay. The validation of the proposed method is demonstrated via a comparison of the present experimental data with previous experiments and DNS (direct numerical simulations) of a round turbulent jet.•The method uses two-time intervals to resolve two sets of velocities such that each set falls within the dynamic range of PIV corresponding to the time interval. (Dynamic range heavily depends on time interval).•A quantity called normalised residual median is defined based on “universal outlier detector” which is used to consolidate the two sets of data.•The strength of the method lies in its ability to obtain instantaneous entrainment velocity even in an unsteady flow.

Advancement of fluid measurement using mode decomposition

In this presentation, recent advanced fluid measurement based on active use of modal decomposition will be introduced. One is sparse processing particle image velocimetry(SPPIV) for real time estimation of flow fields. This technique uses proper orthogonal decomposition (POD), optimized processing point selection in the particle image velocimetry(PIV), and a Kalman filter, and achieves 2000 Hz real time flow observation of 10 m/s flow around airfoil. The other is spatio-temporal superresolution of supersonic flow. This technique combines the microphone data of high temporal but low spatial resolution and the PIV data of low temporal but high spatial resolution by reduced-order model, and the flow data of high temporal and spatial resolution is estimated. Those techniques pioneer the new frontier of the advanced fluid measurements.

Grid turbulence studied by Particle Image Velocimetry

We studied the grid-generated turbulence by using Particle Image Velocimetry (PIV) technique. We test on this already well studied flow the new ways of analyzing spatially resolved PIV data, such as the spatial spectra and structure functions. We compare some of the turbulence characteristics with results of Laser Doppler Anemometry (LDA).

Assimilation and extension of particle image velocimetry data of turbulent Rayleigh\u2013B\xe9nard convection using direct numerical simulations

A novel method for assimilating and extending measured turbulent Rayleigh–Bénard convection data is presented, which relies on the fractional step method also used to solve the incompressible Navier–Stokes equation in direct numerical simulations. Our approach is used to make measured tomographic particle image velocimetry (tomo PIV) fields divergence-free and to extract temperature fields. Comparing the time average of the extracted temperature fields with the temporally averaged temperature field, measured using particle image thermometry in a subdomain of the flow geometry, shows that extracted fields correlate well with measured fields with a correlation coefficient of C_{Ttilde{T}}=0.84. Additionally, extracted temperature fields as well as divergence-free velocity fields serve as initial fields for subsequent direct numerical simulations with and without feedback which generate small-scale turbulence initially absent in the experimental data. Although the tomo PIV data set was spatially under-resolved and did not include any information on the boundary layers, the here-proposed method successfully generates velocity and temperature fields featuring small-scale turbulence and thermal as well as kinetic boundary layers, without disturbing the large-scale circulation contained in the original experimental data significantly. The latter is underpinned by high vertical and horizontal velocity correlation coefficients—computed from velocity fields averaged in time and horizontal x-direction obtained from the measurement and from the simulation without feedback—of C_{vtilde{v}}=0.92 and C_{wtilde{w}}=0.91 representing the large-scale structure. For simulations with feedback, the generated velocity fields resemble the experimental data increasingly well for higher feedback gain values, whereas the temperature fluctuation intensity deviates noticeably from the values obtained from a direct numerical simulation without feedback for gain values alpha ge 1. Thus, a feedback gain of alpha =0.1 was found optimal with correlation coefficients of C_{vtilde{v}}=0.96 and C_{wtilde{w}}=0.95 as well as a realistic temperature fluctuation intensity profile. The xt-averaged temperature fields obtained from the direct numerical simulations with and without feedback correlate somewhat less with the extracted temperature field (C_{Ttilde{T}}approx 0.6), which is presumably caused by spatially under-resolved and temporally oscillating initial tomo PIV fields reflected by the extracted temperature field.Graphical abstract

Continuum microhaemodynamics modelling using inverse rheology

Modelling blood flow in microvascular networks is challenging due to the complex nature of haemorheology. Zero- and one-dimensional approaches cannot reproduce local haemodynamics, and models that consider individual red blood cells (RBCs) are prohibitively computationally expensive. Continuum approaches could provide an efficient solution, but dependence on a large parameter space and scarcity of experimental data for validation has limited their application. We describe a method to assimilate experimental RBC velocity and concentration data into a continuum numerical modelling framework. Imaging data of RBCs were acquired in a sequentially bifurcating microchannel for various flow conditions. RBC concentration distributions were evaluated and mapped into computational fluid dynamics simulations with rheology prescribed by the Quemada model. Predicted velocities were compared to particle image velocimetry data. A subset of cases was used for parameter optimisation, and the resulting model was applied to a wider data set to evaluate model efficacy. The pre-optimised model reduced errors in predicted velocity by 60% compared to assuming a Newtonian fluid, and optimisation further reduced errors by 40%. Asymmetry of RBC velocity and concentration profiles was demonstrated to play a critical role. Excluding asymmetry in the RBC concentration doubled the error, but excluding spatial distributions of shear rate had little effect. This study demonstrates that a continuum model with optimised rheological parameters can reproduce measured velocity if RBC concentration distributions are known a priori. Developing this approach for RBC transport with more network configurations has the potential to provide an efficient approach for modelling network-scale haemodynamics.

A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded

Particle image velocimetry (PIV) is essential in experimental fluid dynamics. In the current work, we propose a new velocity field estimation paradigm, which is a synergetic combination of cross correlation and fully convolutional network (CC-FCN). Specifically, the fully convolutional network is used to optimize and correct a coarse velocity guess to achieve a super-resolution calculation. And the traditional cross correlation method provides the initial velocity field based on a coarse correlation with a large interrogation window. As a reference, the coarse velocity guess helps with improving the robustness of the proposed algorithm. CC-FCN has two types of input layers, one is for the particle images, and the other is for the initial velocity field calculated using cross correlation with a coarse resolution. First, two pyramidal modules extract features of particle images and initial velocity field, respectively. Then the fusion module appropriately fuses these features. Finally, CC-FCN achieves the super-resolution calculation through a series of deconvolution layers to obtain the single-pixel velocity field. As the supervised learning strategy is considered, synthetic data sets including ground-truth fluid motions are generated to train the network parameters. Synthetic and real experimental PIV data sets are used to test the trained neural network in terms of accuracy, precision, spatial resolution and robustness. The test results show that these attributes of CC-FCN are further improved compared with those of other tested PIV algorithms. The proposed model could therefore provide competitive and robust estimations for PIV experiments.

TRANSITION AND LAMINAR FLOWS IN A REALISTIC GEOMETRY OF HUMAN UPPER AIRWAY

In this study, a realistic respiratory airway model extending from oral to the end of the trachea including all the key details of the passage was produced. A series of CT scan images were used to generate the topological data of airway cross-sections that were used to generate the computational model, as well as the three-dimensional (3D) printed model of the passage for experimental study. The airflow velocity field and pressure drop in the airway for different breathing rates of 5, 7.5, 10, and 12.5[Formula: see text]L/min were investigated numerically (by laminar and transition models) and experimentally. The velocity distributions, pressure variation, and streamlines along the oral–trachea airway model were studied. The maximum pressure drop was shown to occur in the narrowest part of the larynx region. It was also concluded that the laryngeal jet could significantly influence the airway flow patterns in the trachea. A comparison between the numerical results and experimental data showed that the transition [Formula: see text]–kl–[Formula: see text] model can give better predictions of pressure losses, especially for flow rates higher than 10[Formula: see text]L/min. The simulation results for the velocity profiles in the trachea were also compared with the available particle image velocimetry (PIV) data and earlier simulations. Despite inter-personal variability and difference in the flow regime, the qualitative agreement was found.

Large-Eddy Simulation of Supersonic Jet Noise with Discontinuous Galerkin Methods

We conduct large-eddy simulations to investigate the role of thermal nonuniformity on the development of instability modes in the shear layer of supersonic jets. We develop an arbitrary high-order discontinuous Galerkin scheme, build meshes with over 100 million degrees of freedom, and validate the model against particle image velocimetry (PIV) and microphone data. The analysis focuses on both numerical issues (such as convergence against the polynomial order of the mesh), modeling issues (such as the choice of subgrid model), and underlying physics (such as vortex stretching and noise generation). We consider the use of wall models to capture the viscous sublayer at the nozzle. We conclude that the Vreman subgrid-scale model leads to the most accurate predictions of both the near-field velocity and the far-field noise, while the Smagorinsky model is best in the nozzle and near the lip area. Injection in the shear layer is more effective at reducing sound generation in the near field than injection in the core. The difference in effectiveness is less substantial in the far field. Injection in the shear layer creates an unstable three-dimensional structure that leads to loss of coherency and three-dimensional effects in the core. Three-dimensionality in the core supports vortex stretching leading to fast mixing.

Experimental investigation of single helium bubbles rising in FLiNaK molten salt

An experimental investigation of single helium bubbles rising in a stagnant molten fluoride salt mixture was conducted in a 25.4 mm quartz tube at 600 °C and atmospheric pressure. The fluoride salt chosen for this research was the eutectic mixture of LiF-NaF-KF (46.5–11.5–42%), also known as FLiNaK. The images obtained using a high-speed camera were processed to estimate the bubble size, rising velocity, trajectory and shape. The shape of the bubble showed typical characteristics of wobbling bubble corresponding to published classifications. The trajectory of the rising bubble showed complex path oscillations and combinations of rectilinear, zig-zag and helical motion. The projected area-equivalent diameter and experimental terminal velocity varied from 5.26 to 6.23 mm and 232.35–259.57 mm/s, respectively. Several important dimensionless numbers were calculated and reported based on the experimental results, which are closely related to the phenomena involving a gas bubble rising in liquid. The measured terminal velocity was compared with the predictions of existing correlations that include the dimensionless numbers and showed reasonable agreement. The Particle Image Velocimetry (PIV) technique was applied to complement the experimental data set with high-fidelity measurements of the velocity field of the liquid molten salt surrounding the bubble. The paper presents a unique set of PIV and two-phase experimental data capturing the behavior of rising helium bubbles in molten FLiNaK and its surrounding flow field.

Systematic Validation Study of an Unsteady Cavitating Flow over a Hydrofoil Using Conditional Averaging: LES and PIV

We present results of Large-eddy simulations (LES) modeling of steady sheet and unsteady cloud cavitation on a two-dimensional hydrofoil which are validated against Particle image velocimetry (PIV) data. The study is performed for the angle of attack of 9° and high Reynolds numbers ReC of the order of 106 providing a strong adverse pressure gradient along the surface. We employ the Schnerr–Sauer and Kunz cavitation models together with the adaptive mesh refinement in critical flow regions where intensive phase transitions occur. Comparison of the LES and visualization results confirms that the flow dynamics is adequately reproduced in the calculations. To correctly match averaged velocity distributions, we propose a new methodology based on conditional averaging of instantaneous velocity fields measured by PIV which only provides information on the liquid phase. This approach leads to an excellent overall agreement between the conditionally averaged fields of the mean velocity and turbulence intensity obtained experimentally and numerically. The benefits of second-order discretization schemes are highlighted as opposed to the lower-order TVD scheme.

Reconstruction of particle image velocimetry data using flow-based features and validation index: a machine learning approach

Reconstruction of flow field from real sparse data by a physics-oriented approach is a current challenge for fluid scientists in the AI community. The problem includes feature recognition and implementation of AI algorithms that link data to a physical feature space in order to produce reconstructed data. The present article applies machine learning approach to study contribution of different flow-based features with practical fluid mechanics applications for reconstruction of the missing data of turbomachinery particle image velocimetry measurements. Support vector regression (SVR) and multi-layer perceptron (MLP) are selected as two robust regressors capable of modelling non-linear fluid flow phenomena. The proposed flow-based features are optimally scaled and filtered to extract the best configuration. In addition to conventional data-based validation of the regressors, a metric is proposed that reflects mass conservation law as an important requirement for a physical flow reproduction. For a velocity field including 25% of clustered missing data, the reconstruction accuracy achieved by SVR in terms of R 2-score is as high as 0.993 for the in-plane velocity vectors in comparison with that obtained by MLP which is up to 0.981. In terms of mass conservation metric, the SVR model by R 2-score up to 0.96 is considerably more accurate than the MLP estimator. For extremely sparse data with a gappiness of 75%, vector and contour plots from SVR and MLP were consistent with those of the original field. Comparison of the ML models outcome with the reconstruction made by six additional spatial interpolation methods indicates considerably higher performance of the SVR in terms of either the velocity-based or mass conservation based metrics.

Modal Decomposition and Linear Modeling of Swirl Fluctuations in the Mixing Section of a Model Combustor Based on Particle Image Velocimetry Data

Abstract In order to determine the flame transfer function of a combustion system, different mechanisms have been identified that need to be modeled. This study focuses on the generation and propagation of one of these mechanisms, namely, the swirl fluctuations downstream of a radial swirl combustor under isothermal conditions. Swirl fluctuations are generated experimentally by imposing acoustic perturbations. Time-resolved longitudinal and crosswise particle image velocimetry (PIV) measurements are conducted inside the mixing tube and combustion chamber to quantify the evolution of the swirl fluctuations. The measured flow response is decomposed using spectral proper orthogonal decomposition to unravel the contributions of different dynamical modes. In addition a resolvent analysis is conducted based on the linearized Navier–Stokes equations to reveal the intrinsically most amplified flow structures. Both, the data-driven and analytic approach, show that inertial waves are indeed present in the flow response and an inherent flow instability downstream of the swirler, which confirms recent theoretical works on inertial waves. However, the contribution of the identified inertial waves to the total swirl fluctuations turns out to be very small. This is suggested to be due to the very structured forcing at the swirler and the additional amplification of shear-driven modes. Overall, this work confirms the presence of inertial waves in highly turbulent swirl combustors and evaluates its relevance for industry-related configurations. It further outlines a methodology to analyze and predict their characteristics based on mean fields only, which is applicable for complex geometries of industrial relevance.

Experimental investigation of the transonic buffet cycle on a supercritical airfoil

Transonic buffet behaviour of the supercritical airfoil OAT15A was investigated experimentally at flow conditions Ma=0.7 and alpha =3.5^circ , using schlieren and particle image velocimetry (PIV). The general behaviour of the buffet cycle was characterised with short-exposure schlieren visualisation and phase-averaged PIV measurements. A spectral analysis showed that the shock oscillation occurs with a dominant contribution at 160 Hz (St = 0.07, in good agreement with the literature) and between 25 and 55 % of the chord of the airfoil. Proper Orthogonal Decomposition (POD) was applied to the PIV data to extract the main modes connected with buffet. It is found that the first three most energetic modes capture around 65 % of the total fluctuating kinetic energy. The first and the third modes have a main frequency peak at 160 Hz and are well representing the separated area and the shock oscillation. The second mode was, instead, associated with an asymmetrical behaviour of the separated area and of the shear layer and displays a main peak at 320 Hz, being double the main buffet cycle frequency. Finally, it was shown that by using the 11 most energetic POD modes, an accurate reduced-order model (ROM) is obtained, which when subtracted from the instantaneous velocity fields allows the visualisation of the small-scale structures present in the flow, such as the upstream travelling waves (UTWs) and the vortex shedding in the separated area near the trailing edge. The analysis allowed to estimate the velocity of the UTWs, obtaining values in good agreement with the literature. In contrast, the analysis of the vortex dynamics in the trailing edge area revealed that vortices shed at the shock foot, which convect downstream in an area detached from the airfoil surface, cannot be considered responsible for the creation of UTWs in view of the mismatch in frequency of the two phenomena.Graphic abstract

Effect of basset history drag on the accurate determination of streaming flow with PIV measurement

In acoustofluidics, the conflation of acoustic streaming and acoustic pressure effects on suspended particles, especially for particle image velocimetry (PIV) with micron-sized particles, leads to inaccurate streaming measurements. Typical multiphase streaming models fail to account for the Basset history drag, which emerges due to a mismatch between the momenta of the particle and the local flow. The history drag is proportional to a fractional-order derivative of the mismatch. Since this force integrates over time, its absence leads to significant modeling errors while also augmenting errors from other sources (e.g., particle-fluid density mismatch and acoustic radiation forcing). We investigate the effects of particle size selection on accurate streaming flow determination from PIV microacoustofluidic system measurements. We achieve this through experimental observation ofparticle behaviors under varied ultrasonic forcing frequencies (30–500MHz) and particle sizes. We pair the empirical study with an analytical fractional-order differential model of a spherical particle under the influence of radiation forces. Rigorous comparative analyses between the model and observations are undertaken to more completely characterize the magnitude of various effects leading to spurious PIV data. We expect the results of this investigation to clearly delineate optimal PIV particle size selection for the continued study of microacoustofluidic systems.

On the use of particle image velocimetry (PIV) data for the validation of Reynolds averaged Navier-Stokes (RANS) simulations during the intake process of a spark ignition direct injection (SIDI) engine

Computational fluid dynamics (CFD) simulations of the in-cylinder flow field are widely used in the design of internal combustion engines (ICEs) and must be validated against experimental measurements to enable a robust predictive capability. Such validation is complicated by the presence of both large-scale cycle-to-cycle variations and small-scale turbulent fluctuations in experimental measurements of in-cylinder flow fields. Reynolds averaged Navier-Stokes (RANS) simulations provide overall flow structures with acceptable accuracy and affordable computational cost for widespread industrial applications. Due to the nature of averaging physical parameters in RANS, its validation against experimental results obtained by particle image velocimetry (PIV) requires consideration of how best to average or filter the measured turbulent flows. In this paper, PIV measurements on the cross-tumble plane were recorded every five crank angle degrees for [Formula: see text] cycles during the intake process of a motored, optically accessible spark ignition direct injection (SIDI) engine. Several methods including ensemble averaging, speed-based averaging and low-order proper orthogonal decomposition (POD) reconstruction were applied to remove the fluctuations from experimental PIV vector fields and thus enable comparison to RANS simulations. Quantitative comparison metrics were used to evaluate the performances of each method in representing the intake jet. Recommendations are made on how to provide a fair validation between measured data and simulation results in highly fluctuating flow fields such as the engine intake jet.

Temporally resolved reconstruction of sweeping jet flow field based on sub-Nyquist-rate PIV data

Sweeping jets can produce kHz frequency actuation for effective flow control. However, high-frequency flow dynamics poses a challenge for the temporal resolution of particle image velocimetry (PIV) measurements. In this article, two methods for reconstructing the temporally resolved flow fields of sweeping jets from sub-Nyquist-rate PIV data, namely least squares (LS) regression and compressive sensing (CS) reconstructions, are proposed and compared. In both methods, proper orthogonal decomposition (POD) is first conducted to reduce the dimensionality of the data by acquiring the spatial modes and the corresponding sub-Nyquist-rate coefficients. For LS regression reconstruction, high-frequency data obtained from pointwise measurement (hotwires) are used to estimate the temporally resolved POD coefficients. For the CS method, the temporally resolved coefficients are calculated by solving a basic pursuit problem based on the sub-Nyquist coefficients. Together with spatial modes, the temporal sweeping jet flow fields can be reconstructed. The performance of LS regression in reproducing the ground truth is highly sensitive to the number of pointwise probes and has a high error rate. In comparison, the CS method can provide more accurate reconstruction with an error rate below 3% without the need for pointwise measurements.

Analysis of flocculation in a jet clarifier. Part 1 – Global and local hydrodynamic analysis

Jet clarifier combines jet hydrodynamics, flocculation and settling in a unit operation. Generally, Camp and Stein G t parameter is recommended to evaluate clarifier efficiency, where G stands for a global velocity gradient and t a characteristic time scale (contact time). In this work, a quasi-two-dimensional jet clarifier is developed to make easier the hydrodynamic analysis of the flocculation zone of a jet clarifier. Measurements of instantaneous velocity field are performed by means of particle image velocimetry (PIV). PIV data are processed to visualise the strong circulation induced by the jet in the flocculation zone. Characteristic time scales related to macromixing are then extracted. Based on PIV data processing, local and instantaneous shear rate are estimated. The analysis of space averaged velocity gradient G is presented. The range of G is 2–15 s−1 whereas the residence time decreases from 4 to 1 h. Based on the hydrodynamic analysis, the parameter Gt is shown to be constant around 30,000 for different jet flow rates. Efficiency of such jet clarifier can thus be foreseen.

A new field correction method for PIV measurements based on mutual information: Case study on a stirred tank flow

Particle image velocimetry (PIV) is an experimental approach widely used to investigate the flow inside stirred tanks. PIV noise sources deteriorate the velocity fields and compromise the measurements, mainly in the estimation of the turbulent parameters. PIV correction approaches based on the primary peak ratio (PPR), namely the ratio between the primary correlation peak to the second highest peak, are used when identifying outliers in the velocity field becomes difficult. However, in some cases, PPR is not sensitive to noise sources and may fail to identify outliers. Therefore, this work proposes a new method of field correction based on mutual information, which is estimated by dividing the height of the cross-correlation peak by the magnitude of the autocorrelation. Analysis were performed using PIV data measured in a 50 L stirred tank. Results show that the proposed methodology can be a valuable tool for the correction of noisy fields.