Bulletin of the American Physical Society
74th Annual Meeting of the APS Division of Fluid Dynamics
Volume 66, Number 17
Sunday–Tuesday, November 21–23, 2021; Phoenix Convention Center, Phoenix, Arizona
Session P20: Experimental Techniques: Quantitative Flow Visualization and Data Analysis III |
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Chair: Samuel Grauer, Pennsylvania State University Room: North 221 AB |
Monday, November 22, 2021 4:05PM - 4:18PM |
P20.00001: Measurement of the unsteady drag of shock-accelerated micro-droplets with uncertain diameter Kyle Hughes, Adam A Martinez, John J Charonko To address a lack of targeted experimental data in unsteady flow conditions, we have been conducting a campaign of experiments, integrated with ongoing validation and modeling efforts, to measure the drag of shock-accelerated micro-droplets. The experiments are being conducted in the Horizontal Shock Tube facility to examine shock-accelerated liquid micro-particles in a dilute suspension at low Mach numbers. An eight-pulse particle tracking diagnostic measures individual particle positions, and a shadowgraph system measures shock location, with pressure transducers providing shock speed at the test section. These diagnostics give us detailed measurements of particle positions versus time for Mach 1.2, 1.3 and 1.4 experiments. Droplet sizes of 2, 6, and 8 μm were investigated, expanding on the 4 μm droplets presented previously. However, the particle diameter is not measured directly but is obtained from the input size distribution. To recover the empirical drag curve, many trajectories are averaged. Various approaches for performing the averaged will be discussed. Finally, the new droplet data shows drag approximately 20% above the standard drag curve, in agreement with previously presented 4 μm droplets. |
Monday, November 22, 2021 4:18PM - 4:31PM |
P20.00002: Denoising particle trajectories containing measurement uncertainties from magnetic particle tracking using neural networks Mohit Nahar Prashanth, Pan Du, Jian-Xun Wang, Huixuan Wu Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like turbidity flow or fluidized-bed flow. Existing analytical trajectory reconstruction algorithms usually require magnetic field gradients, which are challenging to measure accurately. A novel analytical method is developed for an arbitrary sensor arrangement to resolve this. Additionally, to reduce measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, DNN denoisers are more accurate in position reconstruction. However, they often over-smooth the velocity signal, and so a hybrid method is developed that combines the wavelet and DNN models, for a more accurate velocity reconstruction. DNN algorithm is trained using processed sensor and image data collected from a series of experiments. Processed image and sensor data serve as the label and input to DNN respectively. 300000 points in space are to be processed and denoised. We expect DNN to show vast reduction in noise and fluctuation levels of the position, orientation, and velocity signals. |
Monday, November 22, 2021 4:31PM - 4:44PM |
P20.00003: Lagrangian Turbulence at Unprecedented Reynolds Numbers Christian Kuechler, Antonio Ibanez Landeta, Jan Molacek, Eberhard Bodenschatz The Lagrangian reference frame, in which turbulence is viewed by tracking fluid elements over time, is the natural framework for studying transport and mixing phenomena (Sawford (2001)) and previously unexplored properties of turbulence (Toschi & Bodenschatz (2009)). Particularly important Lagrangian dynamics such as the formation of clouds occur at large Reynolds numbers. To our knowledge, the Variable Density Turbulence Tunnel (Bodenschatz et al. (2014)) is the only apparatus capable of generating turbulence at Taylor-scale Reynolds numbers up to 6000, while permitting Lagrangian measurements. The turbulence generation is highly adjustable through a uniquely flexible active grid (Griffin et al. (2019)) and by tuning the pressure of the working fluid SF6 up to 15 bar. Here we present the first Lagrangian particle tracking measurements in this high-pressure wind tunnel. We present the particle properties and the particle injection device. We further describe the laser illumination and the vibration-damped high-speed imaging setup. We present statistics of particle accelerations at Reynolds numbers greater than 3000, marking the highest Reynolds numbers at which such statistics have ever been recorded, as well as radial distribution functions between Stokes Numbers 0.02 and 0.2. |
Monday, November 22, 2021 4:44PM - 4:57PM |
P20.00004: Three-dimensional particle tracking velocimetry and size estimation using stereo schlieren systems Sean P Palmer, Michael J Hargather Stereo single-lens focused shadowgraphy is utilized as an optical diagnostics technique for visualizing two-dimensional projections of high-velocity and explosively-launched material fragments in non-parallel light in stereo high-speed cameras. Centroids of the fragment projections in each frame of a high-speed video sequence are extracted via digital image processing techniques including image segmentation. Fragments tumbling on multiple axes of three-dimensional space result in noisy position measurements. Kalman filter-based tracking is implemented in each stereo camera view to generate an optimal estimate of the object's velocities and trajectory in the presence of noise by recursively updating position predictions with centroid position measurements. The trajectories of the fragments are reconstructed in three-dimensional space via triangulation of the 2D trajectory in each stereo camera at each frame. Size estimation is determined via the spherical assumption of the projected size of fragments and the pixel area conversion to an equivalent diameter for each fragment. |
Monday, November 22, 2021 4:57PM - 5:10PM |
P20.00005: Temperature measurements in the plume of an inductively coupled plasma torch using CARS Dan Fries, John S Murray, Rajkumar Bhakta, Sean P Kearney, Noel T Clemens, Philip L Varghese We perform Coherent Anti-Stokes Raman Scattering (CARS) measurements at different axial locations in the plume of an inductively coupled plasma torch with air as the working gas. We use a nano-second broadband multiplex CARS system to record spectra of nitrogen ro-vibrational transitions in the plasma plume. The spectra are least-squares fitted to theoretical results from the Sandia CARSFT code and resulting temperatures are compared to measurements using emission spectroscopy. The high temperatures in the air plasma make such measurements more challenging than, e.g., measurements in a hydrocarbon flame: the gas densities are lower, the population peak is shifted away from the ground state to higher vibrational levels, and the population differences are reduced, significantly lowering the achievable CARS signal and generating strong background emissions. This work represents a first step in the determination of spatially resolved thermodynamic properties and species composition in the plasma plume. |
Monday, November 22, 2021 5:10PM - 5:23PM |
P20.00006: Experimental 3D fields of velocity, density, and pressure in variable-density turbulent flows Dominique Fratantonio, Erin G Connor, Antonio B Martinez, Tiffany R Desjardins, Adam A Martinez, John J Charonko Understanding the physics of variable-density (VD) turbulence and validation of the relevant turbulent models desperately require experimental data providing the 3D structures of velocity, density, and pressure at the same time. Unfortunately, the simultaneous application of tomographic techniques for both velocity and density is currently too expensive and challenging. In this context, we recently developed a new reconstruction algorithm for generating time-resolved 3D velocity and density fields from simultaneous stereoscopic particle image velocimetry and laser-induced fluorescence. This new method has been demonstrated to be more accurate in turbulent shear flows than the more frequently used method based on the Taylor's hypothesis of frozen turbulence. The improvements in the 3D velocity and density reconstructions also enable the possibility of recovering more accurate 3D pressure fields. Using this method, we present time-series of experimental 3D velocity, density, and pressure fields of a VD round jet. This dataset can then provide crucial information on the pressure-density-velocity correlation statistics, shedding light on the effects of density gradients on the pressure transport of turbulent kinetic energy and on the evolution of turbulent anisotropy in space. |
Monday, November 22, 2021 5:23PM - 5:36PM |
P20.00007: Physics-Informed Flow Field Tomography with UQ using a B-PINN Joseph P. Molnar, Samuel J Grauer Planar and volumetric flow field measurements play an important role in the observation of novel phenomena and validation of numerical models. Tomography is increasingly used to record these measurements due to the limited optical access required for 2D tomography and the potential to conduct instantaneous or time-resolved 3D imaging. Reconstruction is inherently ill-posed so information in addition to the projection data is required to obtain realistic estimates of a flow field. This is usually done with a smoothness prior or maximum entropy constraint. However, existing algorithms give rise to non-physical errors that cannot be fully-corrected in post-processing (PP). We introduce a novel form of flow field tomography using a physics-informed neural net (PINN) to directly reconstruct a flow from a set of coupled projections, as opposed to PINN-based PP of conventional reconstructions. Our approach can be employed in conjunction with numerous modalities in 2D or 3D. Moreover, we show how a Bayesian PINN facilitates uncertainty quantification and enables robust reconstructions of noisy data. We demonstrate our method using synthetic projections of a 2D flow. Our reconstructions are superior to those produced by state-of-the-art algorithms even when a PINN is used for PP. |
Monday, November 22, 2021 5:36PM - 5:49PM |
P20.00008: Probing the effect of water-soluble fluorescent surfactant on complex multi-phase annular flows using structured planar laser-induced fluorescence Andrius Patapas, Victor Voulgaropoulos, Valeria Garbin, Ronny Pini, Karl Anderson, Omar K Matar, Christos N Markides We employ a novel optical diagnostic technique, structured planar laser-induced fluorescence (S-PLIF), developed in-house, in conjunction with a bespoke capacitance probe, to study the flow characteristics of downwards air-water annular flows. The film thickness, temporal characteristics of interfacial waves, and gas entrainment in thin annular films are examined. Simultaneous application of S-PLIF and the capacitance probe provides an opportunity to study the subtle effects of surfactants on the interfacial dynamics. The role of Marangoni stresses brought about by gradients in the surfactant interfacial concentration is highlighted, and the mechanisms underlying the observed phenomena elucidated. |
Monday, November 22, 2021 5:49PM - 6:02PM |
P20.00009: Quantifying bed architecture and pore flow within simulated granular sediment beds by laser induced fluorescence Brandon Hilliard, Ralph S Budwig, Daniele Tonina, Vibhav Durgesh Planar Laser-Induced Fluorescence (PLIF) is a robust experimental technique that involves injecting fluorescent dye into a flow field to extract certain characteristics of the flow field being investigated (Crimaldi, 2008). These characteristics include scalar quantities such as species concentrations (Rubol et al., 2018), physical quantities like grain bed architecture (Hilliard et al., 2021), jet clouds (Wu et al., 2018), and flames (Liu et al., 2018), and vector field quantities such as velocity and pressure gradients (Su & Dahm, 1996). Few studies have attempted to extract velocity fields from the actual movement of the fluorescent dye (Tokumaru, P.T., Dimotakis, 1993), and none have attempted it in a porous media. Current work in the field of porous media flow has been focused on hyporheic flow, or flow that travels between the surface and subsurface domains of rivers and streams. This type of flow is essential for benthic communities to survive, as it is the main transport mechanism for nutrients. We propose a novel experimental method where PLIF is utilized in combination with Refractive Index Matching (RIM) to extract a flow field through a homogeneous porous media. |
Monday, November 22, 2021 6:02PM - 6:15PM |
P20.00010: High-speed two color scanning VLIF Diego Tapia Silva, Tracy Mandel, Cole Cooper, Dustin P Kleckner, Shilpa Khatri A novel two-color scanning volumetric laser induced fluorescence imaging technique (VLIF) for fluid dynamics is presented. The scanning VLIF imaging system allows for a flexible trade off between speed and resolution with throughput of over 17 gigavoxels per second. To demonstrate the capabilities of our technique, we characterize the flow past a sphere for a range of Reynolds numbers. The two-color technique allows us to simultaneously track a passive dye and fluorescently labelled tracer particles. The latter allows us to reconstruct the velocity field using a scanning particle tracking velocimetry technique. |
Monday, November 22, 2021 6:15PM - 6:28PM |
P20.00011: Visual AnemomeTree: using deep learning to predict wind speeds from videoclips of swaying trees in nature. Roni Goldshmid, John O Dabiri Detailed mapping of the wind is necessary for a variety of engineering applications such as weather forecasting, air pollution dispersion models, and wind turbine siting. Wind measurements in the field are commonly taken using expensive remote sensing instrumentation or less expensive point anemometers that can become prohibitively costly when scaling to full field. Alternatively, the use of preexisting objects in the flow could potentially better the balance between cost and scalability. This work uses deep learning to extract wind speeds from videos of swaying trees collected using a drone. To accomplish this, we generated a dataset that consists of preprocessed images that depict statistical measures of the flow, meaning that each datapoint has temporal information encoded in it. The generalizability of the model to a different tree species will be discussed. These curated statistical inputs provide physical insights of the flow-structure interactions which can later assist in further generalization of the model and improve the physical understanding of flow-structure interactions. |
Monday, November 22, 2021 6:28PM - 6:41PM |
P20.00012: Full-field reconstruction of water slamming pressures Rene Kaufmann, Vegard Aune Wave loading can lead to severe damage in offshore structures exposed to extreme weather conditions. Predicting the underlying loading conditions is crucial for designing such structures to provide the necessary resilience and protective capabilities for long term operation. |
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