Particle Image Velocimetry Processing Research Articles

PaIRS-UniNa: A Robust And Accurate Free Tool For Digital Particle Image Velocimetry And Optical Camera Calibration

This contribution presents PaIRS-UniNa (PArticle Image Reconstruction Software – University of Naples “Federico II”), a free software application designed to perform digital particle image velocimetry (PIV). PaIRS-UniNa relies on a powerful C library designed for speed, robustness and accuracy, and a user-friendly graphical interface (Figure 1) running in Python environment on all operating systems, including Windows, Linux and MacOS. At the current stage, it features modules to perform two-component (2C) PIV, stereoscopic PIV and optical camera calibration for multi-camera systems. The processing of PIV and stereoscopic PIV data is based on an iterative image deformation method, which can be customized by the user varying different parameters in such a way to find the best compromise between accuracy and computational speed. These parameters include the size, shape and number of interrogation windows, the number of process iterations and the type of interpolating functions for both image deformation and displacement interpolation (such as bilinear, bicubic, methods based on simplexes or the FFT shift theorem and B-splines of varying orders). Additionally, it is possible to select a range of weighting windows for cross-correlation estimation and velocity field filtering, including rectangular, Blackman, Gaussian, Harris, Hann windows, among others, and several vector validation strategies for outlier detection and replacement. Such a level of customization enables to effectively adjust the modulation transfer function of the PIV process to meet specific application requirements. For the stereoscopic PIV process two different approaches are possible: mapping, which involves applying PIV to de-warped images for 2D-2C displacement fields, and warping, which applies PIV to raw images and combine 3C reconstruction and image dewarping in a single subsequent step. In order to accurately estimate the laser sheet plane location and correct errors due to misalignment and inaccurate displacement vector positions, a disparity correction procedure is also available. The calibration module (CalVi) supports accurate calibration with the camera models mostly used in the PIV community: polynomials, rational functions and the pinhole camera model. Moreover, it supports the integration of the pinhole camera model with a refractive correction model for cylindrical geometries and camera calibration procedures working with unknown positions and orientations of the calibration target. In such a way, the target may be moved by hand without losing accuracy in comparison with a conventional calibration, in which the target is displaced with the aid of translational and rotational stages and knowledge of the target positions is strictly required. Already used in numerous experimental studies by the Experimental Thermo-Fluid Dynamics group of University of Naples “Federico II” and international collaborating groups, PaIRS-UniNa offers a rapid and effective free alternative to commercial PIV software for research and industrial use.

Multi-Scale Flow Estimation Within Stereoscopic PIV Processing Based On Deep Learning

Classical methods of particle-image velocimetry (PIV) based on state-of-the-art cross-correlation algorithms constitute a spatial filtering operation that leads to a loss of spatial resolution of the estimated velocity fields. This is associated with numerous limitations, e.g., when it comes to aligning high-fidelity numerical simulations, since the detection of small turbulent scales is often precluded. An optical flow neural network adapted for PIV processing, called RAFT-PIV, can overcome the drawback of reduced spatial resolution while maintaining the reliability and robustness of the classical approach. The present work aims to extend the application of the aforementioned deep learning based method by integrating it within a stereoscopic PIV processing workflow. Experimental data from an optically accessible single-cylinder combustion engine the flow field of which is characterized by complex, three-dimensional flow structures and a broad range turbulent scales are processed using RAFT-PIV. The 2D-3C vector fields obtained from stereoscopic reconstruction are compared against a classical cross-correlation based PIV evaluation, providing insight into instantaneous quantities and turbulent structures. A qualitative comparison of the instantaneous in-plane and out-of-plane velocity components reveals that the reconstructed flow obtained from the deep learning based estimation includes a wide range of resolved turbulent length scales. It outperforms the classical method. Furthermore, the findings of the present study demonstrate that the pixel accuracy has an effect on both physical quantities and turbulence characteristics. In particular, an increase in turbulent kinetic energy is observed across the engine cycles of the investigated engine flow states. Moreover, a higher fraction of three-dimensional isotropic turbulent structures is derived from RAFT-PIV compared to the results from the cross-correlation based technique.

An Assessment Of Event-Based Imaging Velocimetry For Dimensionality Reduction In Turbulent Flows

"This study investigates the feasibility of using neuromorphic event-based vision (EBV) camera for low-order modelling, a key enabler for real-time flow control. A synchronized experiment with simultaneous Event Based Image Velocimetry (EBIV) and Particle Image Velocimetry (PIV) is performed on a submerged water jet flow at Re = 2600. Flow statistics, spectral content, and reduced-order modeling capabilities using Proper Orthogonal Decomposition (POD) are assessed. Velocity fields are computed via standard PIV processing and compared after interpolation onto a common grid. The analysis reveals good agreement in jet flow statistics and spectra between EBIV and conventional PIV, with differences observed in the power spectral density (PSD) for high frequencies (St>1.5) due to higher noise levels in EBIV data. Nonetheless, most of the spectral content is correctly captured by the EBIV. EBIV successfully identifies dominant flow structures and spectral distribution of energy. The correct identification of temporal modes confirms EBIV's potential for flow control applications based on reduced order models. Despite minor discrepancies downstream of the jet in the Low Order Reconstruction (LOR) analysis, attributed to EBIV's challenge in accurately reconstructing broad-spectrum turbulent regions, the technology proves promising for real-time imaging-based flow control."

Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements

Accurate prediction and measurement of wall-shear stress dynamics in fluid flows is crucial in domains as diverse as transportation, public utility infrastructure, energy technology and human health. However, we still lack adequate experimental methods that simultaneously capture the temporal and the spatial behaviour of the wall-shear stress. In this contribution, we present a holistic approach that derives these dynamics from particle-image velocimetry (PIV) measurements using a deep optical flow estimator with physical knowledge. While the experimental measurements resemble state-of-the-art PIV set-ups, the established particle image processing is replaced by a deep neural network specifically tailored to extract velocity and wall-shear stress information. Since this WSSflow framework operates at the original image resolution, it provides the respective flow field information at a much higher spatial resolution compared with state-of-the-art PIV processing. The results show that this per-pixel approach is essential for an accurate wall-shear stress estimation. The validity and physical correctness of the derived flow quantities are demonstrated with synthetic and real-world experimental data of a turbulent channel flow, a wavy turbulent channel flow and an elastic blood vessel flow. Where baseline data are available for comparison, the instantaneous and time-averaged wall-shear stress predictions accurately follow the ground truth data.

Simultaneous flow measurement and deformation tracking for passive flow control experiments involving fluid–structure interactions

Fluid–structure interactions (FSI) on highly flexible structures involve large deformations and require specific techniques for a thorough investigation of the flow field and structural deformation. To this purpose, a physics-informed method is introduced that allows for simultaneous determination of the flow fields and the structural deformation by using Particle Image Velocimetry (PIV) raw images. The method combines apriori knowledge of the mechanical characteristics of the flexible structure with classical image processing techniques for segmentation. PIV recordings of an actively pitched, highly deformable hydrodynamic profile experiment in a closed water tunnel serve as an example case. To achieve accurate results, the contour obtained from image segmentation is further defined under the assumption that its flexure can be described with the Euler–Bernoulli beam theory model. This makes it possible to determine the neutral fiber of the structure and the final reconstruction becomes possible from knowledge of the original geometry. The resulting procedure allows for a recognition of the structure itself and is suitable for cross-section deformation measurements and for masking of the structure in the raw images to improve the PIV processing. A test case comprising synthetic data similar to the application with a modeled profile geometry of known shape is used to investigate the accuracy of the method and its validity for deformation measurements. Under conditions of cyclic dynamic stall, a mean absolute error of 0.84° could be reached, with a deterioration up to 2° mean absolute error under static stall. The method has a major advantage compared to other technically more sophisticated and complex methods, such as the combination of Laser interferometers combined with Laser-Doppler Anemometry: the method allows for the usage of a single data source for both, fluid and solid in a unified measurement method. Therefore a direct comparison of instantaneous flow field and deformation is possible. In consequence it is in particular useful for highly dynamic multi-physical processes involving extreme deformations, such as passive flow control and soft actuated or flexible under water robotics.

The air entrainment and hydrodynamic shear of the liquid slosh in syringes

Understanding the interface motion and hydrodynamic shear induced by the liquid sloshing during the insertion stage of an autoinjector can help improve drug product administration. We perform experiments to investigate the interfacial motion and hydrodynamic shear due to the acceleration and deceleration of syringes. The goal is to explore the role of fluid properties, air gap size, and syringe acceleration on the interface dynamics caused by autoinjector activation. We used a simplified autoinjector platform to record the syringe and liquid motion without any view obstruction. Water and silicone oil with the same viscosity are used as the model fluids. Particle Image Velocimetry (PIV) is employed to measure the velocity field. Simultaneous shadowgraph visualization captures the air entrainment. Our in-house PIV and image processing algorithms are used to quantify the hydrodynamic stress and interfacial area to investigate the effects of various autoinjector design parameters and fluid types on liquid sloshing. The results indicate that reducing the air gap volume and syringe acceleration/deceleration mitigate the interface area and effective shear. Moreover, the interfacial area and induced hydrodynamic stress decrease with the Fr=U/aD, where U is the interface velocity, a is the maximum syringe acceleration, and D is the syringe diameter.

The Application Of Apertured Filter Method To The Simultaneous Measurement Of Particle-Turbulence Interaction In Dilute Suspension Layer Under Oscillatory Sheet Flows

One of the challenges confronted in particle image velocimetry (PIV) measurements applied in multi-phase flows is the discrimination of signals from different phases simultaneously registered in each single monochromatic camera. In the current sheet flow study, the sediment particles with a mean diameter of 240 μm are used and the diameter of tracer particles ranges from 25~50 μm. Under conventional imaging, the disparity in particle sizes would inevitably cause highly imbalanced reflected signals from the two phases and the image quality of one of the phases typically must be sacrificed. This manuscript introduces a low-cost and exceedingly convenient solution, named as the apertured filter method, which is characterized by the combined use of fluorescent tracer particles and an optical band pass filter with a round hole drilled in the center, with the hole diameter comparable to the smallest lens aperture size. It is demonstrated that the apertured filter method can provide balanced image intensities among the sediment and tracer particles and preserve the identifiability and separability of both phases. To compute the carrier phase velocity, median filter subtraction (Kiger & Pan, 2000) is applied to the acquired images, followed by direct implementation of stereoscopic PIV (SPIV) process. The cross-talk PIV errors due to the presence of sediment particles are assessed and found to be within 0.3 pixels up to a sediment concentration of C=1%. Some guidance on the fabrication of the apertured filter are also addressed to assist potential users. Enabled by the apertured filter method and the multi-camera single-plane (MCSP) method developed by Liu & Kiger (2022), a whole field, phase-locked time-resolved, particle-resolved and concurrent measurement of both the fluid and the sediment phase within the dilute regime (C≤1%) in a sinusoidal oscillatory sheet flow (period, T=5s, and peak free stream velocity, U_(o,p)=1 m/s) is achieved. Such detailed measurements in sheet flow have never been reported before to the author's best knowledge. The analysis of the acquired data focused upon three phase angles when the external flow is reversing the direction. The phase averaged sediment concentration, mean velocities for each phase and slip velocities between the two phases are evaluated.

Analysis Of PIV Images Of Transonic Buffet Flow By Recurrent Deep Learning Based Optical Flow Prediction

In the past few years, several algorithms have been proposed that leverage deep learning techniques within the data analysis workflow of particle-image velocimetry (PIV) experiments. This emerging body of work has shown that deep learning has the potential to match or outperform state-of-the-art classical algorithms in terms of efficiency, accuracy, and spatial resolution. Due to the significant relevance of PIV experiments in the broader fluid mechanics community, progress in PIV processing approaches based on state-of-the-art machine learning tools has a major impact across a range of problems in applied physics and engineering where velocity components of flow fields need to be determined. In contrast to existing methods, these approaches are general, near-automated and yield per-pixel flow estimates. Primary work on deep learning for PIV is promising, but important questions concerning the application to challenging engineering problems remain open and more scientific analyses are necessary to substantiate the general confidence amongst practitioners in such neural network based tools. This motivates us to employ a novel deep learning based PIV approach called RAFT-PIV to a challenging application of a recent research subject. That is, we use our neural optical flow approach to evaluate Background-Oriented Schlieren (BOS) and PIV images obtained by measurements of the transonic flow around a supercritical DRA-2303 profile and demonstrate that it can be used as a direct one-to-one replacement for standard cross-correlation based counterparts.

Generalization of deep recurrent optical flow estimation for particle-image velocimetry data

Particle-image velocimetry (PIV) is one of the key techniques in modern experimental fluid mechanics to determine the velocity components of flow fields in a wide range of complex engineering problems. Current PIV processing tools are mainly handcrafted models based on cross-correlations computed across interrogation windows. Although widely used, these existing tools have a number of well-known shortcomings, including limited spatial output resolution and peak-locking biases. Recently, new approaches for PIV processing leveraging a novel neural network architecture for optical flow estimation called recurrent all-pairs field transforms (RAFT) have been developed. These have matched or exceeded the performance of classical, handcrafted models. While the RAFT-PIV method is a promising approach, it is important for the broader fluids community to more completely understand its empirical behavior and performance. To this end, in this study, we thoroughly investigate the performance of RAFT-PIV under varying image and lighting conditions. We consider applications spanning synthetic and experimental data, with a breadth and depth going far beyond currently available empirical results. The results for the wide variation of experiments shed new light on the capabilities of deep learning for PIV processing.

Measuring binary fluidization of nonspherical and spherical particles using machine learning aided image processing

AbstractThe binary fluidization of Geldart D type nonspherical wood particles and spherical low density polyethylene (LDPE) particles was investigated in a laboratory‐scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using particle image velocimetry (PIV). The wood particles orientation and velocity are measured using particle tracking velocimetry (PTV). A machine learning pixel‐wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.

Pulsed LED line light for large-scale PIV—development and use in wave load measurements

In this paper the development of a high-power pulsed LED line light and its use to apply particle image velocimetry (PIV) during wave impact measurements are described. An electrical circuit that generates high-current pulses is designed and built, which is used to overdrive a number of commercially available LEDs. The limit for this overdrive-capacity is determined as function of pulse duration for various commercial available LEDs. Two systems of cylindrical convex lenses are designed to act as a collimator and reduce divergence of the LED bundle and the resulting light sheet properties (maximum light intensity and sheet thickness) are investigated. An array of LEDs of 60 cm length (referred to as the LED line light) is designed and manufactured. For the two lens systems, the LED line light provides proper light sheet conditions to illuminate measurement regions in the order of either 0.3 × 0.3 m2, or 1 × 1 m2, at a sufficiently constant light sheet thickness of 5 mm. The application of the LED line light is demonstrated by quantifying the instantaneous flow field of a wave impacting on a blunt object in a wave flume. PIV measurements are conducted at an acquisition rate of 25 frame pairs per second, quantifying maximum flow velocities in the order of 1.0 m s−1 at a LED pulse width of 200 μs. The system, consisting of the LED line light, a CMOS camera and open source PIV processing software provides the possibility to perform 2D planar PIV measurements for a fraction of the costs of a commercially available laser based PIV system.

Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry

Convolutional neural networks have been successfully used in a variety of tasks and recently have been adapted to improve processing steps in Particle-Image Velocimetry (PIV). Recurrent All-Pairs Fields Transforms (RAFT) as an optical flow estimation backbone achieve a new state-of-the-art accuracy on public synthetic PIV datasets, generalize well to unknown real-world experimental data, and allow a significantly higher spatial resolution compared to state-of-the-art PIV algorithms based on cross-correlation methods. However, the huge diversity in dynamic flows and varying particle image conditions require PIV processing schemes to have high generalization capabilities to unseen flow and lighting conditions. If these conditions vary strongly compared to the synthetic training data, the performance of fully supervised learning based PIV tools might degrade. To tackle these issues, our training procedure is augmented by an unsupervised learning paradigm which remedy the need of a general synthetic dataset and theoretically boosts the inference capability of a deep learning model in a way being more relevant to challenging real-world experimental data. Therefore, we propose URAFT-PIV, an unsupervised deep neural network architecture for optical flow estimation in PIV applications and show that our combination of state-of-the-art deep learning pipelines and unsupervised learning achieves a new state-of-the-art accuracy for unsupervised PIV networks while performing similar to supervisedly trained LiteFlowNet based competitors. Furthermore, we show that URAFT-PIV also performs well under more challenging flow field and image conditions such as low particle density and changing light conditions and demonstrate its generalization capability based on an outof-the-box application to real-world experimental data. Our tests also suggest that current state-of-the-art loss functions might be a limiting factor for the performance of unsupervised optical flow estimation.

Development and validation of a PIV processing routine for two-phase index-matched flows

Implementing particle image velocimetry (PIV) in particle-laden flows is a challenging task due to the optical effects of the added particles. The addition of particles reduces optical access in the measurement region, degrades image quality and increases the experimental uncertainty. One method for reducing this effect is by using refractive index matching (RIM) which matches the index of refraction of the fluid and added particles rendering the particles optically transparent. Exact matching of the refractive index is difficult to achieve in practice. Even slight mismatches in the refractive indices lead to degradation of the image quality which will impact the higher-order statistics. Comparisons of statistics between loading levels are difficult under these conditions. In this work, the effect of processing methodology on RIM PIV images is investigated. RIM is achieved through the use of superabsorbent hydrogel materials in water. Two processing methods are investigated. The baseline method is a basic PIV correlation process without any pre- or post-processing. The refined process utilizes image pre-processing, data post-processing, and experimental error compensation. The goal for the refined method was to optimize and implement a more sophisticated PIV processing routine using existing commercial software on a particle-laden flow to reduce progressive error artifacts. Images from a fully turbulent channel flow with particle loading levels of 0–15% are utilized. Past literature indicates that turbulence levels will be attenuated for the particles used. Turbulence levels calculated using the baseline method show a linear increase with increasing loading, which is a non-physical result. The refined processing method shows a slight attenuation of turbulence which is consistent with expectations. The study highlights the need for careful processing and interpretation of results in cases where changes in image quality can be expected between different experimental conditions.

Experimental investigation of shock-induced separation and flow control in a transonic compressor cascade

The influence of transition control on shock-induced flow separation was investigated in a highly loaded transonic compressor cascade at an inlet Mach number of 1.21 and a chord-based Reynolds number of $$1.4 \times 10^6$$ . Transition was influenced by raising the free-stream turbulence from 0.5 to 2.5%. Two further cases employed either air-jet vortex generators (AJVG) or a surface roughness patch as transition control devices. Velocity fields in the vicinity of the unsteady transonic separation were captured by particle image velocimetry (PIV). Blade flexure induced by the unsteady aerodynamic loading was tracked for each image and compensated individually prior to PIV processing. The captured flow fields indicate shape variations of the separation region, while the shock foot moves within a range of up to 20% of chord. The frequency of separation for each investigated case was assessed on the number of vectors with negative velocity in each PIV sample. To further quantify the size of the separation region, the statistically independent PIV samples were conditionally averaged for various passage shock positions at a resolution of 1% chord length. Insight to the dynamics and frequency of the passage shock motion was further provided by high-speed shadowgraphy. Large bubble separation occurs if the turbulence of the incoming flow is low. The size of separation region decreases when AJVGs are applied but still exhibits bubble separation as the passage shock moves downstream. The size of the separation region is significantly reduced either if a roughness patch is applied or if the turbulence level of the incoming flow is high. The flow conditions showing bubble separation in the mean flow also exhibit distinct spectral peaks indicating periodic shock oscillations.

Inter-Laboratory Characterization of the Velocity Field in the FDA Blood Pump Model Using Particle Image Velocimetry (PIV).

A credible computational fluid dynamics (CFD) model can play a meaningful role in evaluating the safety and performance of medical devices. A key step towards establishing model credibility is to first validate CFD models with benchmark experimental datasets to minimize model-form errors before applying the credibility assessment process to more complex medical devices. However, validation studies to establish benchmark datasets can be cost prohibitive and difficult to perform. The goal of this initiative sponsored by the U.S. Food and Drug Administration is to generate validation data for a simplified centrifugal pump that mimics blood flow characteristics commonly observed in ventricular assist devices. The centrifugal blood pump model was made from clear acrylic and included an impeller, with four equally spaced, straight blades, supported by mechanical bearings. Particle Image Velocimetry (PIV) measurements were performed at several locations throughout the pump by three independent laboratories. A standard protocol was developed for the experiments to ensure that the flow conditions were comparable and to minimize systematic errors during PIV image acquisition and processing. Velocity fields were extracted at the pump entrance, blade passage area, back gap region, and at the outlet diffuser regions. A Newtonian blood analog fluid composed of sodium iodide, glycerin, and water was used as the working fluid. Velocity measurements were made for six different pump flow conditions, with the blood-equivalent flow rate ranging between 2.5 and 7 L/min for pump speeds of 2500 and 3500rpm. Mean intra- and inter-laboratory variabilities in velocity were ~ 10% at the majority of the measurement locations inside the pump. However, the inter-laboratory variability increased to more than ~ 30% in the exit diffuser region. The variability between the three laboratories for the peak velocity magnitude in the diffuser region ranged from 5 to 25%. The bulk velocity field near the impeller changed proportionally with the rotational speed but was relatively unaffected by the pump flow rate. In contrast, flow in the exit diffuser region was sensitive to both the flow rate and the rotational speed. Specifically, at 3500rpm, the exit jet tilted toward the inner wall of the diffuser at a flow rate of 2.5 L/min, but the jet tilted towards the outer wall when the flow rate was 7 L/min. Inter-laboratory experimental mean velocity data (and the corresponding variance) were obtained for the FDA pump model and are available for download at https://nciphub.org/wiki/FDA_CFD . Experimental datasets from the inter-laboratory characterization of benchmark flow models, including the blood pump model presented herein and our previous nozzle model, can be used for validating future CFD studies and to collaboratively develop guidelines on best practices for verification, validation, uncertainty quantification, and credibility assessment of CFD simulations in the evaluation of medical devices (e.g. ASME V&V 40 standards working group).

Comparative assessment of pressure field reconstructions from particle image velocimetry measurements and Lagrangian particle tracking

A test case for pressure field reconstruction from particle image velocimetry (PIV) and Lagrangian particle tracking (LPT) has been developed by constructing a simulated experiment from a zonal detached eddy simulation for an axisymmetric base flow at Mach 0.7. The test case comprises sequences of four subsequent particle images (representing multi-pulse data) as well as continuous time-resolved data which can realistically only be obtained for low-speed flows. Particle images were processed using tomographic PIV processing as well as the LPT algorithm ‘Shake-The-Box’ (STB). Multiple pressure field reconstruction techniques have subsequently been applied to the PIV results (Eulerian approach, iterative least-square pseudo-tracking, Taylor’s hypothesis approach, and instantaneous Vortex-in-Cell) and LPT results (FlowFit, Vortex-in-Cell-plus, Voronoi-based pressure evaluation, and iterative least-square pseudo-tracking). All methods were able to reconstruct the main features of the instantaneous pressure fields, including methods that reconstruct pressure from a single PIV velocity snapshot. Highly accurate reconstructed pressure fields could be obtained using LPT approaches in combination with more advanced techniques. In general, the use of longer series of time-resolved input data, when available, allows more accurate pressure field reconstruction. Noise in the input data typically reduces the accuracy of the reconstructed pressure fields, but none of the techniques proved to be critically sensitive to the amount of noise added in the present test case.

AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

Abstract. Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

Time-Resolved Particle Image Velocimetry Measurements with Wall Shear Stress and Uncertainty Quantification for the FDA Nozzle Model.

We present advanced particle image velocimetry (PIV) processing, post-processing, and uncertainty estimation techniques to support the validation of computational fluid dynamics analyses of medical devices. This work is an extension of a previous FDA-sponsored multi-laboratory study, which used a medical device mimicking geometry referred to as the FDA benchmark nozzle model. Experimental measurements were performed using time-resolved PIV at five overlapping regions of the model for Reynolds numbers in the nozzle throat of 500, 2000, 5000, and 8000. Images included a twofold increase in spatial resolution in comparison to the previous study. Data was processed using ensemble correlation, dynamic range enhancement, and phase correlations to increase signal-to-noise ratios and measurement accuracy, and to resolve flow regions with large velocity ranges and gradients, which is typical of many blood-contacting medical devices. Parameters relevant to device safety, including shear stress at the wall and in bulk flow, were computed using radial basis functions. In addition, in-field spatially resolved pressure distributions, Reynolds stresses, and energy dissipation rates were computed from PIV measurements. Velocity measurement uncertainty was estimated directly from the PIV correlation plane, and uncertainty analysis for wall shear stress at each measurement location was performed using a Monte Carlo model. Local velocity uncertainty varied greatly and depended largely on local conditions such as particle seeding, velocity gradients, and particle displacements. Uncertainty in low velocity regions in the sudden expansion section of the nozzle was greatly reduced by over an order of magnitude when dynamic range enhancement was applied. Wall shear stress uncertainty was dominated by uncertainty contributions from velocity estimations, which were shown to account for 90-99% of the total uncertainty. This study provides advancements in the PIV processing methodologies over the previous work through increased PIV image resolution, use of robust image processing algorithms for near-wall velocity measurements and wall shear stress calculations, and uncertainty analyses for both velocity and wall shear stress measurements. The velocity and shear stress analysis, with spatially distributed uncertainty estimates, highlights the challenges of flow quantification in medical devices and provides potential methods to overcome such challenges.

Autocorrelation-based estimate of particle image density for diffraction limited particle images

In particle image velocimetry (PIV), the number of particle images per interrogation region, or particle image density, impacts the strength of the correlation and, as a result, the number of valid vectors and the measurement uncertainty. For some uncertainty methods, an a priori estimate of the uncertainty of PIV requires knowledge of the particle image density. An autocorrelation-based method for estimating the local, instantaneous, particle image density is presented. The method assumes that the particle images are diffraction limited and thus Gaussian in shape. Synthetic images are used to develop an empirical relationship between the autocorrelation peak magnitude and the particle image density, particle image diameter, particle image intensity, and interrogation region size. This relationship is tested using experimental images. The experimental results are compared to particle image densities obtained through implementing a local maximum method and are found to be more robust. The effect of varying particle image intensities was also investigated and is found to affect the measurement of the particle image density. Knowledge of the particle image density in PIV facilitates uncertainty estimation, and can alert the user that particle image density is too low or too high, even if these conditions are intermittent. This information can be used as a new vector validation criterion for PIV processing. In addition, use of this method is not limited to PIV, but it can be used to determine the density of any image with diffraction limited particle images.