journals.library.iit.edu (Illinois Tech)
Not a member yet
264 research outputs found
Sort by
smartPIV – an app for flow visualization by cross-correlation and optical flow using smartphones
Get PDFIn recent years smartphones considerably changed our communication and are used on a daily (or even every minute) basis especially by students without any difficulties. Fluid flows also belong to our daily experiences. However, the education of the basic principles of fluid mechanics is sometimes cumbersome due to its non-linear nature. This problem may be tackled in practical sessions applying flow visualization techniques in wind or water tunnels and directly learn from own observations. Nowadays, often optical methods like particle imaging velocimetry (PIV) or particle tracking velocimetry (PTV) are used for these purposes. A typical PIV/PTV setup consists of a (double)pulse laser, a scientific camera and a synchronization device. The costs for this equipment can easily add up to more than 100,000 euros and the installations and set up of the systems requires experiences and is complex. For these reasons Universities often only offer practical courses for a small amount of students and the students may not be allowed to use and set up the systems by their own as the equipment is also needed for scientific research. Due to the COVID-19 pandemic it is also often not allowed to share equipment or even to work in larger groups during practical sessions
Cross-plane stereo-PIV measurements in a refractive-index-matched environment of flow associated with barchan dunes immersed in a turbulent boundary layer
Get PDFBarchan dunes are crescent-shaped bedforms that form in aeolian (i.e., wind-driven) environments (including both Earth and other planets, such as Mars) as well as subaqueous environments. Under the forcing of the aloft turbulent boundary layer, they migrate downstream at a rate inversely proportional to their size, which results in complex interactions between neighboring dunes of disparate scales. In particular, it has been observed that dunes will interact at a distance, causing changes in morphology without contacting each other, which is thought to be driven by the way dunes modify the local flow field Bristow et al. (2018); Assis and Franklin (2020).
In this study, the coherent structures formed in the wakes of barchan dunes are investigated using measurements of the flow over fixed-bed (i.e., solid) barchan models, both in the wake of an isolated barchan and the interdune region between interacting barchans (Fig. 1(a)). Furthermore, the interactions between the flow structures shed by the dunes and the structures in the incoming boundary layer are analyzed
Application of SPOD analysis to PIV data obtained in the wake of a circular cylinder undergoing vortex induced vibrations
Get PDFVortex induced vibrations (VIV) of a circular cylinder have been investigated experimentally using a cyberphysical apparatus with m∗ = 8, ζ = 0.005, and Re = 4000. This study considers the application of proper orthogonal decomposition (POD) and spectral POD (SPOD) analysis to the wake dynamics of the low-mass-ratio VIV of a circular cylinder in the lower branch at U∗ = 7.5. SPOD has been previously shown to better separate frequency-centered modal dynamics, compared to POD. Coherent POD and SPOD modes were compared and the newly separated third SPOD mode pair was found to have a periodicity characteristic of vortex shedding and a peak in the temporal coefficient spectra at St = f D/U∞ = 0.2248. The literature has identified that the wake dynamics within the lower branch are synchronized to the cylinder motion; however the present study suggests that some hidden dynamics persist at the Strouhal frequency. Low order models based on the first eight POD and SPOD modes were compared, and it was found that the filtering operation in SPOD removes the uncorrelated stochastic energy component of the POD modes while producing a comparable representation of the coherent deterministic part of the wake dynamics. Using SPOD to separate the distinct frequency-centered dynamics into unique, interpretable mode pairs will simplify future efforts to develop sparse dynamical models of the flow
Flow Field around the Badminton Shuttlecock during Flipping Motion
Get PDFBadminton is one of the most popular sports in the world. The shuttlecock is used in badminton game has the unique shape. The shuttlecock is truncated cone-shaped and consists of a cork, gaps and a skirt portion. The shuttlecock has aerodynamic properties which differ from the ball used in other racquet sports. As an example of unique aerodynamic property, the shuttlecock shows high deceleration. It is known that the initial velocity immediately after smashing may reach up to 137m/s (493 km/h) at maximum. The velocities of the shuttlecock are reduced from the initial velocity of 67 m/s to the terminal velocity of approximately 7 m/s for approximately 0.6 s (Hubbard et al. 1997). In addition, turnover refers to the flipping experienced by a shuttlecock when undergoing heading change from nose pointing against the flight path at the moment of impact and a shuttlecock indicates the aerodynamically stable feature for the flip movement just after impact (Cohen et al. 2015). The turnover stability of a series of feather and synthetic shuttlecocks was measured to compare the performance of synthetic shuttlecocks to that of feather shuttlecocks (Calvin et al. 2013). The turnover stability of the shuttlecock is investigated through experiment and simulation, and the angular response of the shuttlecock in turnover was modelled and studied (Calvin et al. 2015). Furthermore, it was reported that the aerodynamic stability of the shuttlecock during flip movement was affected by gaps of the shuttlecock skirt in a previous study (Nakagawa et al. 2017). However, the mechanism of turnover stability of the shuttlecock has not been fully understood. The purpose of this study is to investigate the unsteady flow field around the shuttlecock during flip movements. In the present, we simulated the flipping motion by wind tunnel experiments and visualized the flow field around the shuttlecock by a PIV technique
First challenge on Lagrangian Particle Tracking and Data Assimilation: datasets description and planned evolution to an open online benchmark
Get PDFIn the last decade, Lagrangian Particle Tracking (LPT) has emerged as one of the leading measurement techniques for the quantitative determination of fluid flows in three-dimensional domains (see e.g. Schanz et al., 2016), due to its accuracy in reconstructing particles velocities and material accelerations. Due to the scattered nature of the obtained result, at the particles positions only, significant research efforts have also been placed in the development of dedicated Data Assimilation (DA) techniques, aiming at finally reconstructing full 3D velocity and pressure fields on regular Cartesian grids (see, e.g., Schneiders et al. 2016)
Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry
Get PDFConvolutional 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
Investigating Optimal Training and Uncertainty Quantification for CNN-based Optical Flow
Get PDFOptical Flow (OF) techniques provide “dense estimation” flow maps (i.e. pixel-level resolution) of timecorrelated images and thus are appealing to applications requiring high spatial resolutions. OF methods revolve around mathematical descriptions of the image as a collection of features, in which the pixel-level light intensity is the primary variable (Horn and Schunck, 1981). Feature tracking often involves the notion of scale invariance. Traditional OF approaches, merely based on mathematical formulations, have suffered from many challenges, especially when directly applied to images of fluid flows textured with tracer particles (hereafter PIV-like images). Due to the limited number of computationally manageable features and suboptimal regularization methods, successful implementation of past approaches has been limited to highly textured images and small displacement dynamic ranges
Uncertainty propagation and truncation errors in LPT kinematics
Get PDFLagrangian Particle Tracking (LPT) has become a near-standard approach for performing accurate 3D flow measurements, thanks notably to the technical breakthroughs brought by the Iterative Particle Reconstruction (IPR: Wieneke, 2013) and Shake-the-Box (STB: Schanz et.al, 2016) procedures. These decisive progresses have triggered a number of studies relative to the eduction of flow kinematics and dynamics based on particle trajectory analyses. Novara & Scarano (2013), and others, focused on polynomial approximations of the trajectories, which analytically provide the material derivatives used to estimate pressure gradients. In particular, approximations based on second order polynomials fits of a small number of particle positions are used in commercially available softwares and among research teams as a straightforward solution to obtain the first and second order derivatives with a limited effect of the measurement noise. Additionally the analyses conducted during the 2020 LPT challenge (Leclaire, 2020 ; Sciacchitano, 2020) have addressed the performance of methodologies used by different groups with respect to second order trajectory fits for both multi-pulse and four-pulse (Novara et. al, 2016) LPT cases. On more advanced theoretical grounds, Geseman et. al (2016) have proposed the trackfit approach using penalized B-splines with considerations on the time-varying acceleration rate (i.e. jolt or jerk) and spectral content of noisy particle track
Experimentally obtained velocity and pressure fields of an open channel flow around a cylinder using RIM-SPIV
Get PDFThe quantification of velocity and pressure fields over streambeds is important for predicting sediment mobility and water exchange between stream and sediment interstitial spaces (Schmeeckle and Nelson, 2003; Tonina and Buffington, 2009). The latter is typically referred as hyporheic flow, which consists of surface water that flows through the streambed sediment pores (Tonina and Buffington, 2009). These fluxes are mainly driven by pressure gradients at the water sediment interface. In this paper, we report an experimental investigation of the time-averaged velocity and pressure field, quantified in a set of laboratory experiments using stereo PIV (Particle Image Velocimetry) with a non-toxic index-matched fluid, for an open channel flow around a barely submerged vertical cylinder as a model for plant stalk over a plane bed of coarse granular sediment, mimicking a stream gravel bed. This is the first time that such a velocity and pressure field is characterized experimentally for a free surface flow with irregular floor contour
Noise canceling to compute second order statistic from SPIV
Get PDFStereoscopic PIV (SPIV) is performed near the wall in a turbulent boundary layer at high Reynolds numbers (Reτ = 2272, 3840) in the streamwise-wallnormal plane. A novel method for denoising the statistics is proposed by the use to two independent SPIV systems to capture the same field of view, with the objective being that the random noise associated with the velocity fluctuations are not correlated between two independent PIV systems. The derivatives in the third (spanwise) direction are obtained by axisymmetry assumptions [George and Hussein (1991)] and continuity equation. The statistics are in agreement with that of DNS at comparable Reynolds number, from y + = 25 for lower Reτ experiment and from y + = 40 for higher Reτ experiment