Bulletin of the American Physical Society
77th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 24–26, 2024; Salt Lake City, Utah
Session R23: Experimental Techniques: Pressure / Temperature |
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Chair: Jian Sheng, Texas A&M University - Corpus Christi Room: 251 A |
Monday, November 25, 2024 1:50PM - 2:03PM |
R23.00001: Pressure Field Reconstruction from Particle Image Velocimetry Data Using an Analytical Solution of the Pressure Poisson Equation with a Green’s Function Approach for an Axisymmetric Problem Oleg Goushcha, Peter Ganatos Pressure field reconstruction from a Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) data is a well-studied topic with improvements to existing methods and new approaches continuously appearing in the literature. The reconstruction process is primarily achieved using two classical processes: direct integration of the Navier-Stokes equations and solution of the Pressure Poisson Equation (PPE) with the associated boundary conditions. More recently, a new method using integration in Fourier space technique has also emerged. Each approach has its set of benefits, which is debated in the literature. The focus of current work is a solution to a classical PPE, which is usually discretized and solved using standard numerical methods. In this work, we propose an analytical solution to the PPE problem using Green's function approach eliminating the need to address numerical aspects of the problem except to perform numerical integration. Two formulations of an axisymmetric problem are proposed: one with mixed and one with all Neumann boundary conditions. The ideal flow case is used to evaluate the difference in the results obtained from two formulations before applying both equations to the PIV data set of a self-propelled vortex. |
Monday, November 25, 2024 2:03PM - 2:16PM |
R23.00002: Estimating the Full Pressure Field Immediately Downstream from a Propeller Using Stereoscopic PIV Nathan Welker, Daniel Maynes Experimental flow-field pressure data has long been acquired by employing pitot probe arrays. These arrays have poor time-response capabilities rendering them inadequate for time-resolved or high-speed flow applications. They are also intrusive and can cause perturbations that disrupt the flow field. However, experimental data from velocity-based measurements, such as particle image velocimetry (PIV), can be combined with the governing flow equations to derive high-resolution and time-resolved pressure fields in a non-intrusive manner. A multi-plane stereo-PIV system was used to determine the unsteady pressure field across a plane of the wake of an isolated propeller of diameter 9.5 in that was rotating at 4860 RPM. The measurement plane was located 0.01 propeller diameters downstream of the propeller and the PIV measurements were synchronized with the propeller rotation to allow the pressure field to be phase-resolved, without requiring high frame rate acquisition. The time-varying pressure fields were also phase-averaged to allow direct comparison to traditional averaged pitot probe pressure data, with good agreement. The unsteady pressure data were also compared to time-varying data from a RANS simulation of the same propeller geometry with very good agreement. |
Monday, November 25, 2024 2:16PM - 2:29PM |
R23.00003: Fast Multipole Method for Two-Dimensional Pressure Reconstruction from Noisy Gradient Information Ritik Mody, Qi Wang Accurately obtaining fields from noisy gradient information is crucial in many computational physics applications. Examples include deriving velocity potentials from sparse velocity measurements or reconstructing pressure fields from particle image velocimetry (PIV) data in high Reynolds number scenarios. The computational burden often arises from the need to average different integration paths and solve for unknown boundary conditions, which can be formulated as a boundary integral equation using the Green's function of the Laplace operator. In this paper, we apply the Fast Multipole Method (FMM) to reduce the computational complexity comparable to the number of measurements for two-dimensional problems. We present the formulation of the integral equation and describe the process of solving the boundary pressure using the Boundary Element Method (BEM) in conjunction with the FMM. We demonstrate the effectiveness of our approach using a test case from the Johns Hopkins turbulent database, reconstructing pressure fields from material acceleration data. Our results are compared with those obtained using existing methods, highlighting the advantages of our proposed approach in terms of accuracy and computational efficiency. |
Monday, November 25, 2024 2:29PM - 2:42PM |
R23.00004: Revisit Liu and Katz (2006) and Zigunov and Charonko (2024): On the Equivalency of Omni-directional Integration and Pressure Poisson Equation, and the Compatibility Condition Zhao Pan, Connor Pryce, Lanyu Li In this work, we demonstrate the equivalency of Omni-Directional Integration (ODI) and the Pressure Poisson Equation (PPE) for pressure field reconstruction from corrupted image velocimetry data. We show that ODI is equivalent to pursuing the minimal norm solution to a Poisson equation with Neumann boundary conditions and that a minimal norm solution automatically satisfies the compatibility condition required by the existence of the solution. We explain why some studies have reported poor robustness of the PPE and how this is often rooted in a lack of regularization to satisfy the compatibility consideration. Our new comprehensions on the equivalence of ODI and PPE not only provide insights into why ODI and the minimal norm solution of PPE are robust against random noise in the data, but, more importantly, reduce the immense computational cost of ODI to that of PPE. This work leads the way for further improvement to PPE/ODI-based pressure field reconstruction by leveraging the established fast and robust numerical methods for elliptic equations and the corresponding regularization methods. We conclude our analysis by providing a "minimalism" regularization for experimentalists for robust pressure field reconstruction, anything beyond which would require significantly more work. |
Monday, November 25, 2024 2:42PM - 2:55PM |
R23.00005: Direct pressure field reconstruction using boundary-independent shortest-path integration Samuel Kok Suen Cheng, Jian Sheng Pressure measurement is of great importance in fluid mechanics and the direct reconstruction of the pressure field by integrating the pressure gradient fields has been realized. However, most of the current algorithms initiate the integration at the boundary where the pressure gradient measurement is often unreliable. In this study, we introduce the boundary-independent shortest-path (BISP) integration algorithm for planar pressure reconstruction whereby the integration starts at the inner domain of the pressure gradient field and grows outward toward the boundaries. The pressure at any given point is calculated by averaging over millions of shortest path line integrations originating from already developed points. The algorithm is first validated using a direct numerical simulation (DNS) forced isotropic turbulence dataset. Further, we demonstrate the ability to reconstruct pressure fields containing inner voids of arbitrary sizes and shapes. We also show the applicability of this algorithm in experimental datasets by reconstructing the pressure field around a bacteria streamer grown on an oil droplet. Overall, this algorithm can reconstruct pressure fields with high accuracy without propagation of boundary errors or any dependency on the boundary values. |
Monday, November 25, 2024 2:55PM - 3:08PM |
R23.00006: Wall model for surface pressure reconstruction from PIV data Julian Powers, Adrian Lozano-Duran We propose a wall model for estimating surface pressure from particle image velocimetry data. The model, which accepts pressure input from the Green’s function integral method, is derived from the thin-boundary layer equations and incorporates the effects of turbulence and wall curvature. |
Monday, November 25, 2024 3:08PM - 3:21PM |
R23.00007: Measurement of instantaneous wall stress distributions in turbulent flows with flexible micromirror sensors Maryam Jalali-Mousavi, Jian Sheng, Samuel Kok Suen Cheng, Abdessamad Talioua, Abdessamad Talioua, Kimberly Lopez The ability to resolve the shear and pressure forces concurrently carries a significant importance. In this study we have developed an array of microsensors that can resolve relevant scales of shear and pressure simultaneously. This technique attains information regarding complex dynamics near the wall, involving phenomena like the boundary layer, flow separation, and cavitation, providing insights into their dynamic interactions with the wall, and capturing deformations and stresses. The utilized method involves employing an array of wrinkle-free metallic thin film in polymer as sensors and each sensor is comprised of a micro-scale nanometer thin film sensor in a micro-well (µWell). Each µWell contains a metallic thin film encased in a soft polymer that acts as a flexible mirror within a 2D plane. The sensors are being validated and calibrated in a high-speed rotating disk shear flow facility, which proves attain diverse shear stress range. The sensors’ strain field is recorded via digital holographic microscopy interferometry, utilizing modeling and calibration, we will establish a direct correlation between deformations and wall and shear stresses. |
Monday, November 25, 2024 3:21PM - 3:34PM |
R23.00008: Development of 3D Printable Temperature-Sensitive Paint for Micro-channel Application Oscar F Pontiff, Daiki Kurihara, Hirotaka Sakaue, Yu-Wei Wu, Chih-Yung Huang Temperature-sensitive paint (TSP) has attracted attention in the thermo-fluid science fields as a global temperature measurement technique. It has been utilized to measure a 2-dimensional temperature field on a surface of interest. Those measured temperature fields are used to analyze temperature control systems or thermal properties of aerodynamic objects. The present research focuses on the study of micro-channel flow using a TSP. If the TSP can be applied to a desired cross-sectional region of interest, the heat-transfer between the channel and the surroundings at any desired region can be extracted. A 3-dimensional (3D) and printable TSP is introduced in this study. A 3D printer has been utilized to create a TSP layer at any desired region of an 3D object. The current status of this research will be presented. |
Monday, November 25, 2024 3:34PM - 3:47PM |
R23.00009: Kolmogorov Artificial Intelligence Velocimetry infers hidden temperature from turbulent experimental velocity data Juan Diego Toscano, Theo Käufer, Zhibo Wang, Christian Cierpka, Martin R Maxey, George Em Karniadakis We propose Kolmogorov Artificial Intelligence Velocimetry (KAIV) to infer hidden temperature fields from turbulent experimental velocity data. This scientific machine-learning approach allows temperature prediction using only velocity data, eliminating the need for direct temperature measurements. Our models are based on physics-informed Kolmogorov Arnold Networks (PIKANs) and are trained by optimizing a combined loss function that minimizes the residuals of the velocity data, boundary conditions, and the governing equations. To manage local imbalances in the optimization process, we propose a residual-based attention method with resampling (RBA-R) that enhances stability and efficiency by utilizing historical residual data for sampling and local multipliers to balance the point-wise errors. To ensure exact constraint enforcement, we use approximate distance functions for temperature boundary conditions and redesign the base model to predict divergence-free fields directly. We apply KAIV to experimental volumetric and simultaneous temperature and velocity data of Rayleigh-Bénard convection obtained from combined Particle Image Thermometry (PIT) and Lagrangian Particle Tracking (LPT), which allows us to compare KAIV predictions and measurements directly. Furthermore, we demonstrate its efficacy by accurately calculating convective heat transfer, analyzing the QR distribution, and viscous and thermal dissipation rates from the resulting velocity and temperature fields. |
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