Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Volume 67, Number 19
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session Q16: Experimental Techniques: PIV/PTV Methods and Algorithms |
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Chair: Tommaso Astarita, Univ of Napoli Federico II Room: 143 |
Monday, November 21, 2022 1:25PM - 1:38PM |
Q16.00001: Inferring volumetric data from PTV measurements of incompressible flows Sreevatsa Anantharamu, Krishnan Mahesh, YANG ZHANG, Michael Fenelon, Louis N Cattafesta A novel method is proposed to spatially reconstruct a divergence-free velocity from Particle Tracking Velocimetry (PTV) measurements of incompressible flow. Current methods for a general flow first interpolate the scattered PTV data onto a fixed Eulerian grid using a predetermined number of particles around each grid point. This limits the achievable accuracy as the particles move along their Lagrangian trajectories and the density of the particles around each fixed grid point changes in time. Our method avoids a fixed Eulerian grid and directly uses the Lagrangian PTV data to reconstruct velocities with maximum accuracy. At each instant, a very high-degree rational polynomial is fitted to the scattered PTV velocities and then projected to be divergence-free on a dynamic triangulation that encompasses the particles. Arnoldi-based methods are presented for numerically reliable computation of the high-degree fit and projection. The proposed method is shown to outperform the widely used radial basis functions in both accuracy and speed for certain functions. The method is applied on data ranging from simulated flows to experimental PTV measurements. |
Monday, November 21, 2022 1:38PM - 1:51PM |
Q16.00002: A Voronoi-based smooth particle hydrodynamics approach for estimating instantaneous pressure from PTV measurements Sathvik Bhat, Jesse Capecelatro, Harish Ganesh Estimating local pressure from experimental optical measurement techniques like particle tracking velocimetry (PTV) remains challenging. Traditional methods project the Lagrangian data onto an Eulerian grid, using memory intensive techniques that introduce averaging errors. In this work, a novel approach is proposed to compute the instantaneous pressure from PTV data using a Voronoi-based smooth particle hydrodynamics (SPH) method. Voronoi tessellation is performed on the particle field to obtain local volumes, and efficiently identify nearest neighbours. Velocity gradients at each particle are computed via a smoothing kernel, and the pressure is obtained by enforcing the full Navier-Stokes equations. One of the major benefits of the approach is that it does not require special treatment at measurement boundaries. The proposed approach is validated using a model problem of a Taylor-Green vortex, and application of the methodology to high-speed time-resolved PTV measurements in the wake of a bluff-body (Re ~ 110000) is discussed. Furthermore, a detailed comparison on the computational errors and the associated costs with different smoothing kernels is presented. |
Monday, November 21, 2022 1:51PM - 2:04PM |
Q16.00003: Optimizing dt for mp-stb in particle tracking velocimetry Michael Fenelon, Louis N Cattafesta, YANG ZHANG, Krishnan Mahesh, Nick Morse Multi-pulse tracking velocimetry is gaining increasing popularity in the fluid mechanics community. The basic working principle is to trace the particle displacements in multiple images separated by specified time intervals, and then the Lagrangian particle path and associated quantities can be determined. A recent breakthrough in particle tracking velocimetry was the introduction of the Shake-the-Box algorithm, which can accurately determine particle trajectories in a tomographic experimental setup. More specifically, multi-pulse Shake-the-Box has allowed for overcoming hardware limitations in which cameras cannot sample fast enough to time resolve the flow dynamics. However, the timing strategy of the multi-pulse technique must be specified by the user. Therefore, this study aims to investigate and assess the timing strategy used in multi-pulse Shake-the-Box for canonical flows, such as a synthetic jet, as well as to quantify the uncertainty associated with each strategy. |
Monday, November 21, 2022 2:04PM - 2:17PM |
Q16.00004: Algorithmic improvements for 3D-3C volumetric PTV processing Kevin Mallery, Dan Troolin, Amine Koched The study of fluid mechanics benefits greatly from the acquisition of volumetric velocity fields with high spatial and temporal resolution. Volumetric 3-component Velocimetry (V3VTM) (Pereira et al., EXIF 2000) is one such technique in which multiple high-speed, high-resolution, and low-noise cameras are used to capture the Lagrangian motion of tracer particles distributed throughout a measurement volume. This presentation describes an improved volumetric PTV algorithm building on Dense Particle Identification and Reconstruction (Boomsma and Troolin, Lisbon 2018). Additional feedback loops are utilized to drastically reduce the occurrence of ghost particles. A probabilistic algorithm for solving an assignment problem is presented. This algorithm is utilized for solving both camera and temporal particle correspondences. Time information is used in order to map the unique paths of a large quantity of individual particles resulting in both velocity and acceleration information obtained within a reasonable processing time. A detailed analysis of the algorithms as well as videos of extracted experimental flow fields will be presented and discussed. |
Monday, November 21, 2022 2:17PM - 2:30PM |
Q16.00005: Image-based Measurements of Diffusion Coefficients from Diffraction-Limited Nanoparticles Luis C Sanjuan, Sayantan Bhattacharya, Jiacheng Zhang, Javad Eshraghi Image-based techniques can be used to estimate the diffusion coefficient of particles in a fluid. Techniques such as dynamic light scattering, fluorescence recovery after photobleaching, and fluorescence correlation microscopy have limited resolution and are highly sensitive to experimental conditions. Particle image velocimetry (PIV) methods provide better accuracy and repeatability. A modified PIV approach called image-based probability estimation of displacement (iPED) was developed and tested on particle sizes ranging from 200nm to 1000nm. iPED allows for higher accuracy and robustness in the estimation of diffusion coefficients of nanoparticle solutions compared to established PIV methods without assumptions on particle behavior and shape. However, the effectiveness, parametrization, and limitations of iPED have yet to be established for a wide range of diffraction-limited nanoparticles. In this work, we present an adaptation and application study of iPED for 20nm to 100nm particles. This study improves upon the image pre-processing and cross-correlation processing stages of iPED. The effectiveness of the method is determined by performing a convergence analysis of iPED and comparing the best estimate of the diffusion coefficient with the theoretical value obtained from the Stokes-Einstein equation. Results demonstrate that images in this range of nanoparticles exhibit an extremely low signal-to-noise ratio, leading to a low primary peak ratio in the ensemble cross-correlation plane. This requires additional efforts in image pre-processing and the use of filtering techniques such as phase correlation in order to improve the quality of the signal, thus, reducing the error in the diffusion coefficient measurements. |
Monday, November 21, 2022 2:30PM - 2:43PM |
Q16.00006: A Gaussian Smoothing-based velocity track denoising approach for 3D-PTV measurements Rudra Sethu Viji, Melissa C Brindise, Jiacheng Zhang, Sayantan Bhattacharya, Pavlos Vlachos Volumetric particle tracking velocimetry (3D-PTV) is a well-established optical measurement technique that analyses complex flow structures by tracking the motion of several neutrally buoyant particles seeded in the fluid. Novel particle tracking methods such as Shake-the-Box (STB) have expanded the utility and accuracy of PTV. However, the obtained tracks have measurement noise, owing to several error sources present in the measurement chain. This adversely affects the accuracy of the reconstructed velocity field and other derived quantities. This work introduces a novel Gaussian kernel-based track smoothing approach for denoising particle tracks. The uniqueness of this technique lies in its ability to provide smoothened velocity tracks without any differentiation scheme on the position tracks. The Gaussian smoothing (GS) method was tested on synthetic cases of 3D Hama flow and Stokes vortex. GS reduced the root-mean-square-errors in the velocity and turbulent statistics by around 90% compared to standard PTV and 50% compared to FlowFit. Additionally, the effect of kernel width and length is investigated for optimal noise reduction. GS will be tested on experimental datasets and compared against the state-of-the-art methods (TrackFit, FlowFit) and other commonly used filters. |
Monday, November 21, 2022 2:43PM - 2:56PM |
Q16.00007: 4D DIH-PTV via Stochastic Particle Advection Velocimetry (SPAV) Ke Zhou, Samuel J Grauer, Jiarong Hong Particle tracking velocimetry (PTV) is widely used to measure 4D velocity and pressure fields in fluid dynamics research. Particle localization uncertainty is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH). To address this, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss term that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that estimates particle positions over time as a function of the velocity field. The model can account for non-ideal effects like Stokes drag in a shock. A statistical data loss that compares tracked and advected particle positions, accounting for arbitrary localization uncertainties, is derived and approximated. We demonstrate our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss and a Navier–Stokes physics loss, for both synthetic and experimental DIH-PTV data. The statistical approach significantly improves PTV reconstruction compared to a conventional data loss. Our method can be readily adapted to other data assimilation techniques like state observer or adjoint state methods. |
Monday, November 21, 2022 2:56PM - 3:09PM |
Q16.00008: Computational optimization of omni-directional pressure integration schemes John J Charonko, Dominique Fratantonio Variations in the pressure field play an important role in many fluid dynamics problems. For turbulent flows in particular the pressure fluctuations and their correlations with the instantaneous velocity field are a major contributor to the evolution of the turbulent transport. However, measurement of fluctuating pressure is very difficult without resorting to invasive pressure probes, and the spatially resolved information needed for analysis of pressure gradient terms in the turbulent transport equations is generally not available through direct diagnostics. Instead, researchers have pursued calculation of pressure fields from experimentally measured velocity fields but multiple papers have shown that such calculations are very sensitive to propagation of experimental error. Various strategies have emerged to control this effect, with one of the most robust being the various omni-directional pressure integration schemes which are usually shown to minimize the resultant error on the pressure fields as compared to matrix inversion approaches such as Poisson equation solutions. Unfortunately, this technique, while tractable in 2D, can be significantly more expensive when adapted to 3D, which has been shown to be important for evaluating the true pressure fields. In this work we will propose and explore several options for optimizing omni-directional pressure integration methods for both 2- and 3D calculations by reducing or eliminating the need to explicitly calculate each individual line integral, and compare their effect on the accuracy and efficiency of the methods as compared to the original approach and Poisson solvers in various simulated and experimental flows. |
Monday, November 21, 2022 3:09PM - 3:22PM |
Q16.00009: PAIRS: a free software application for robust and accurate digital particle image velocimetry Tommaso Astarita, Gerardo Paolillo This contribution presents PAIRS (PArticle Image Reconstruction Software), a free software application designed to perform digital particle image velocimetry (PIV). PIV has become a standard tool in the fluid dynamics community for the analysis of turbulent flows. PAIRS, developed since 2000, includes several modules that allow to process also stereoscopic and tomographic PIV measurements. The current free release aims mainly at introducing the user to the module that handles the processing of double-frame or time-resolved 2D planar PIV images. PAIRS employs an iterative multi-grid approach based on image deformation. The user is allowed to select different types of process parameters in such a way to find the best compromise between accuracy and computational speed. Beyond this flexibility, preset strategies are also available for image analysis and designed for specific tasks like fast visualization of results in preliminary measurements or high accuracy in the final phase of investigation. PAIRS has been validated both numerically and experimentally and its accuracy and robustness have been proved during the 2nd and 3rd International PIV Challenges. PAIRS is based on a C library and relies on a user interface in Python to facilitate its usage and promote the diffusion. |
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