Particle Image Velocimetry Data Research Articles

Spatio-temporal relationship between three-dimensional deformations of a collapsible tube and the downstream flowfield

The interactions between fluid flow and structural components of collapsible tubes are representative of those in several physiological systems. Although extensively studied, there exists a lack of characterization of the three-dimensionality in the structural deformations of the tube and its influence on the flow field. This experimental study investigates the spatio-temporal relationship between 3D tube geometry and the downstream flow field under conditions of fully open, closed, and slamming-type oscillating regimes. A methodology is implemented to simultaneously measure three-dimensional surface deformations in a collapsible tube and the corresponding downstream flow field. Stereophotogrammetry was used to measure tube deformations, and simultaneous flow field measurements included pressure and planar Particle Image Velocimetry (PIV) data downstream of the collapsible tube. The results indicate that the location of the largest collapse in the tube occurs close to the downstream end. In the oscillating regime, sections of the tube downstream of the largest mean collapse experience the largest oscillations in the entire tube that are completely coherent and in phase. At a certain streamwise distance upstream of the largest collapse, a switch in the direction of oscillations occurs with respect to those downstream. Physically, when the tube experiences constriction downstream of the location of the largest mean collapse, this causes the accumulation of fluid and build-up of pressure in the upstream regions and an expansion of these sections. Fluctuations in the downstream flow field are significantly influenced by tube fluctuations along the minor axes. The fluctuations in the downstream flowfield are influenced by the propagation of disturbances due to oscillations in tube geometry, through the advection of fluid through the tube. Further, the manifestation of the LU-type pressure fluctuations is found to be due to the variation in the propagation speed of the disturbances during the different stages within a period of oscillation of the tube.

Analysis of In-Cylinder Flow in a Small-Bore Spark-Ignition Engine Using Computational Fluid Dynamics Simulations and Zero-Dimensional-Based Modeling

Abstract The evolution of in-cylinder flow involves large- and small-scale structures during the intake and compression strokes, significantly influencing the fuel–air mixing and combustion processes. Extensive research has been conducted to investigate the flow evolution in medium- to large-sized engines using laser-based diagnostic methods, computational fluid dynamics (CFD) simulations, and zero-dimensional (0D) based modeling. In the present study, we provide a detailed analysis of the evolution of flow fields in a small-bore spark ignition (SI) engine with a displacement volume of 110 cm3. This analysis employs a unique methodology, where CFD simulation is performed and validated using measured particle image velocimetry (PIV) data. Subsequently, the validated CFD results are utilized to develop and validate a 0D-based model as it is computationally more efficient. The validated CFD simulation and 0D-based model are then used to evaluate the quantified strength of the flow by calculating the tumble ratio and turbulent kinetic energy (TKE). The streamlines and velocity vectors of the flow fields obtained from CFD simulations are utilized to explain the evolution of these parameters during intake and compression strokes. The study is further extended to analyze the effect of engine speed on the evolution of flow fields. With an increase in engine speed, relatively higher values of tumble ratio and TKE at the end of the compression stroke are observed, which is expected to improve the fuel–air mixing and combustion efficiency.

Investigation of fluid flow during flow boiling inside a horizontal rectangular channel with single-sided heating using particle image velocimetry

Subcooled flow boiling is a highly efficient cooling systems for thermal management systems. This study explores the intricate dynamics of subcooled flow boiling within a horizontal channel, investigating the impact of vapor generation on liquid-phase velocity using Particle Image Velocimetry (PIV) and advanced image processing techniques. Four mass flow rates ranging from 5–20 g/s with subcooled inlet conditions are investigated in a rectangular channel with single-sided heating. Three regions of interest along the heated channel are investigated for instantaneous PIV analysis. The PIV system captures detailed velocity profiles, illustrating the impact of varying mass flow rates and heat flux levels on flow behavior. Vapor masking techniques are introduced to enhance the precision of PIV data by mitigating interference from the vapor phase. Results demonstrate the influence of vapor bubbles on flow resistance, revealing non-uniform velocity distributions and turbulence near the liquid–vapor interface. The study emphasizes the critical role of inertia and buoyancy forces in shaping the velocity profiles. Moreover, the investigation sheds light on the effects of flow rates on the interfacial behaviors, hinting at a transition point between 10 and 15 g/s. In summary, this research contributes valuable insights into the nuanced dynamics of flow boiling, laying the foundation for future studies on turbulence, heat transfer, and phase-change phenomena in two-phase thermal management systems.

Fluid dynamics and mixing in a novel intermittently rotating bioreactor for CAR-T cell therapy: Spin-down from incomplete spin-up

This work explores the fluid flow and mixing within a new type of intermittently rotating bioreactor used for the automated manufacturing of CAR-T cell therapy products; CAR-T is a novel cell-based gene therapy which offers a single-dose cure for several forms of advanced blood cancer. The rotating cylinder bioreactor has a free surface and a liquid height to radius ratio of 0.5. Agitation is achieved via intermittent rotation of the entire vessel around its central vertical axis. No comprehensive engineering characterisation of the fluid dynamics in a bioprocessing context exists to date for this type of bioreactor. The present study examines the fluid dynamics and mixing during spin-down to rest following incomplete spin-up (i.e. the starting state of the fluid is not solid body rotation). Novel Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence (PLIF) data is presented, shedding light on the different transient flow regions inside the intermittently rotating bioreactor, and on the impact spin-up time has on the mixing efficiency of the subsequent spin-down stage. The results presented can be used to inform the design of a custom intermittent agitation pattern for any resembling cylinder bioreactors, the adoption of which could lead to higher yields and reduced costs for the cell expansion step of the manufacturing process.

Data Assimilation of Ideally Expanded Supersonic Jet Using RANS Simulation for High-Resolution PIV Data

Data assimilation using particle image velocimetry (PIV) and Reynolds-averaged Navier–Stokes (RANS) simulation was performed for an ideally expanded supersonic jet flying at a Mach number of 2.0. The present study aims to efficiently reconstruct all the physical quantities in the aeroacoustic fields that match well with a realistic, experimentally obtained flow field. The two-dimensional, two-component PIV measurement was applied to the jet axis plane, and the time-averaged velocity field was obtained using single-pixel ensemble correlation. Two-dimensional axisymmetric RANS simulation using the Menter shear stress transport (SST) model was also performed, and the parameters of the SST model were optimized via data assimilation using the ensemble Kalman filter. The standard deviation of the observation noise σ, which is a parameter of the ensemble Kalman filter, is estimated by the previously proposed method (Nakamura et al., Low-Grid-Resolution-RANS-Based Data Assimilation of Time-Averaged Separated Flow Obtained by LES. Int. J. Comp. Fluid. Dyn., 2022), and its effectiveness was investigated for the first time. This method effectively estimated the magnitude of σ at each generation without tuning the hyperparameters. The assimilated flow fields exhibited similar flow structures observed in PIV such as the potential core length or shear layer. Therefore, the present framework can be used to estimate time-averaged full flow fields that match well with experimentally observed flow fields, and has the potential to construct a database for the Navier-Stokes-based stability analysis that requires a full flow field.

Wake Formation Flow Physics and Boundary Layer Analysis on the Sides of the Isosceles Triangular Cylinder with Apex Pointing Downstream

Boundary layer interaction with downstream flow structures was numerically studied to find the region of inactivity behind an 75° isosceles triangular cylinder with apex pointing downstream at intermediate Reynolds numbers (Re = 520, 640, 840 and 1040). The Standard k-ε model in OpenFOAM was used in the study. Numerical results were validated against Particle Image Velocimetry data. Results revealed the stable region of inactivity characterized by low turbulent kinetic energy and vorticity. The onset of secondary vortex and separation point, independent of Reynolds number, was identified. The onset of the secondary vortex was located at (x = 2 mm) from the base and (y = 1.5 mm) from the apex on either side of the cylinder. The ratio of modulus of absolute primary z-component of vorticity, |ω_z^1 |, to the modulus of secondary z-component of vorticity, |ω_z^2 |, was found to be approximately equal to 1.2. This ratio is invariant of the Reynolds number of the study. These findings have practical implications. The unique properties of the inactivity region forms an ideal location that can be used for injecting fluid, placing measurement probe, active flow control and drag reduction. The research problem is formulated in the introduction. Literature is reviewed next providing the background. Details about the range of parameters, governing equations, numerical study details and software used are given in the methodology section. The results section gives the numerical results, verified by mesh refinement test and validated against experimental results. The results are finally discussed in the next section.

Temporal and spatial flow field reconstruction from low-resolution PIV data and pressure probes using physics-informed neural networks

In this paper, we present an innovative approach using physics-informed neural networks to reconstruct high-frequency, full-field flows, including the pressure field, by integrating sparse, noisy, low-temporal-resolution particle image velocimetry (PIV) data with high-temporal-resolution pressure probe data. This method effectively leverages the spatial richness of PIV data and the temporal abundance of pressure probe data, offering a complementary spatial and temporal data fusion. The incorporation of physical laws via equation constraints enables the neural network to accurately learn and predict complex fluid dynamics, achieving comprehensive flow field reconstructions. Rigorous testing across various flow types—periodic, non-periodic, and complex—against critical parameters like sampling interval (SI), number of grid points per snapshot (NGPIS), and number of pressure probes (NPP) has demonstrated remarkable accuracy. The results show reconstruction errors for velocity components (u, v) and pressure (p) below 5% with sufficient data, and around 10% for v and p, and below 5% for u in data-limited scenarios. A case study with SI = 30, NGPIS = 2000, NPP = 5 underscores the enhanced robustness and accuracy of random sampling, especially under various noisy conditions. Thus, this approach shows significant potential for temporal and spatial reconstruction of flow fields.

Insights into the transition of separation bubble over a rough surface at varying angles of attack

The evolution of a separated boundary layer on the rough surface in the vicinity of a leading edge of a model airfoil is documented at varying angles of attack. Particle image velocimetry and hotwire data are analyzed to elucidate the flow feature, depicting the manifestation of the shear layer, its rollup, growth of perturbations, spectral response, and intermittency. For a hydrodynamically smooth surface, a laminar separation bubble often appears near the leading edge, where the shear layer becomes inviscidly unstable. Wall roughness amplifies the near-wall perturbations, resulting in earlier transition and reattachment. This leads to a reduction in bubble length and laminar shear layer length compared to the smooth surface at the corresponding angle of attack. Notably, despite the amplification of selective frequency, the inviscid instability is bypassed on the rough surface for varying angles of attack. Moreover, the linear stability analysis proves inadequate in predicting the most amplified frequency and the growth of disturbances. Furthermore, the universal intermittency curve formulated for the bypass transition is valid for the separation-induced transition, illustrating the significance of viscous effect.

Application of a Combinatorial Vortex Detection Algorithm on 2 Component 2 Dimensional Particle Image Velocimetry Data to Characterize the Wake of an Oscillating Wing

To investigate the vortical wake pattern generated by water flow past an oscillating symmetric airfoil, using experimental velocity fields from particle image velocimetry (PIV), a novel combinatorial vortex detection (CVD) algorithm is developed. The primary goal is to identify and characterize vortices within the wake. Experimental flows introduce complexities not present in numerical simulations, posing challenges for vortex detection. The proposed CVD approach offers a more robust alternative, excelling in both vortex detection and quantification of essential parameters, unlike widely-used methods such as Q-criterion, λ2-criterion, and Δ-criterion, which rely on subjective and arbitrary thresholds resulting in uncertainty. The CVD algorithm effectively characterizes the airfoil wake, identifying and analyzing vortices aligning with the Burgers model. This research enhances understanding of wake phenomena and showcases the algorithm’s potential as a valuable tool for vortex detection and characterization, particularly for experimental fluid dynamics. It provides a comprehensive, robust, and non-arbitrary approach, overcoming limitations of traditional methods and opening new avenues for studying complex flows.

Flow analysis through a randomly packed pebble-bed geometry using computational fluid dynamics

This study presents a comprehensive analysis of the flow behavior in packed pebble-bed reactors using computational fluid dynamics (CFD) simulations. The pebble-bed geometry corresponds to an experimental facility located at the Texas A&M Thermal-Hydraulics Research Laboratory. The unsteady Reynolds-averaged Navier–Stokes (URANS) k−ω shear stress transport (SST) and the large eddy simulation (LES) approaches were selected to model the turbulence at different Reynolds numbers. The numerical models were first validated by comparing the pressure drop results obtained from the simulations against established correlations, finding the simulation predictions in accurate agreement. Secondly, the velocity first-order statistics from the URANS k−ω SST and LES calculations were also contrasted with the available experimental particle image velocimetry data to validate the numerical models. Results were found in reasonable agreement as the mean absolute error achieved values smaller than 10% of the inlet velocity for most of the analyzed velocity profiles. A comprehensive turbulence characterization was performed, including second-order statistics, Reynolds stress anisotropy, and turbulent kinetic energy production. The proper orthogonal decomposition of the fluctuating velocity was examined in the current flow domain. The turbulence characterization revealed the complex nature of turbulence in packed pebble-bed geometries, which is further complicated by the presence of an enclosing wall. Overall, the findings of this study provide a solid foundation for the development of more accurate CFD-based methodologies for predicting the behavior of flow through packed pebble-bed reactors.

Impacts of the Bottom Vortex on the Surrounding Flow Characteristics of a Semi-Submerged Rectangular Cylinder Under Four Aspect Ratios

Abstract Vortex has attracted attention because it is the main factor affecting the flow characteristics around offshore structures. This paper researches the flow field variations around a semi-submerged rectangular cylinder (SRC) under four aspect ratios, thereby revealing the bottom vortex effects on the flow field at Reynolds number = 36,300. According to the particle image velocimetry (PIV) data, the renormalization group (RNG) k–ε model is selected as an applicable turbulence model for studying SRC flow characteristics. From the results, the bottom vortex generation and streamwise velocity are inseparable. As the draft increases, the increased range of the bottom vortex would directly interfere with the flow characteristic around the SRC. Since the wake vortex forms a reattachment state when the SRC pierces the free surface, its flow field could be disturbed by the bottom vortex. Furthermore, the bottom vortex also affects the extreme point position of the boundary layer characteristic at the SRC bottom.

Low-order planar pressure reconstruction of stalled airfoils using particle image velocimetry data

Low-order planar pressure reconstruction of stalled airfoils using particle image velocimetry data

Deriving cool flame propagation speeds by means of an ozone-seeded, stagnation plate burner configuration

This study aims to investigate the feasibility of experimental determination of DME/O2/O3 cool flame propagation speeds using Particle Image Velocimetry (PIV) in a stagnation plate burner operated at atmospheric pressure. A specific PIV data analysis procedure was developed in order to improve the accuracy of the measurements in this particular configuration. Five flame conditions, with equivalence ratio varying from 0.3 to 0.5 and ozone mole fraction varying from 1.5 to 2% were investigated to compare experimental results with kinetic modeling. Three ozone-submechanisms, respectively from Jian et al. (Jian et al., 2022), Halter et al. (Halter et al., 2011) and Zhao et al. (Zhao et al., 2016), were coupled with our previously developed DME mechanism (Panaget et al., 2021) and used to compare experimental and simulated axial velocity profiles. Results show that a thoughtful choice of the ozone-submechanism is of particular importance in predicting an accurate cool flame velocity in these conditions. A numerically assisted non-linear extrapolation method is proposed for the determination of the unstrained cool flame speed Su,0. Additionally, simulations for which the plate temperature reaches the maximal flame temperature (adiabatic conditions) were performed, demonstrating a negligible effect of the plate temperature on the determined Su,0. A kinetic analysis is also presented to highlight the most sensitive chemical reactions influencing the reference cool flame speed Su,ref, showing the preponderant role of the fuel low temperature chemistry.

Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction

The super-temporal-resolution (STR) reconstruction of turbulent flows is an important data augmentation application for increasing the data reach in measurement techniques and understanding turbulence dynamics. This paper proposes a data assimilation (DA) strategy based on weak-constraint four-dimensional variation to conduct an STR reconstruction in a turbulent jet beyond the Nyquist limit from given low-sampling-rate observations. Highly resolved large-eddy simulation (LES) data are used to produce synthetic measurements, which are used as observations and for validation. A segregated assimilation procedure is realised to assimilate the initial condition, inflow boundary condition and model error separately. Different types of observational data are tested. The first type is down-sampled LES data containing many small-scale turbulence structures with or without synthetic noise. The DA results show that the temporal variation of the small-scale structures is well recovered even with noise in the observations. The spectra are resolved to a frequency approximately one order of magnitude higher than what can be captured within the Nyquist limit. The second type of observation is low-sampling-rate tomographic particle image velocimetry (tomo-PIV) data with or without the injection of small-scale structures. The modulation between the large-scale structures contained in the tomo-PIV fields and the small scales injected from the observations is improved. The resultant small scales in the STR reconstruction have the characteristics of authentic turbulence to a considerable extent. Additionally, DA yields much smaller errors in the prediction of particle positions when compared with the Wiener filter, demonstrating the great potential for Lagrangian particle tracking in measurement techniques.

Features of laminar separation bubble subjected to varying adverse pressure gradients

This article describes the spatial development of a laminar separation bubble (LSB), its transition, and eventual breakdown under the influence of adverse pressure gradients (APGs) similar to those experienced by low-pressure turbine blades. The investigation combines a comprehensive experimental approach with a well-resolved large eddy simulation (LES). The streamwise pressure gradients were varied by manipulating the upper wall within the test section. The Reynolds number (Re), based on the plate length and inlet velocity, was 0.2 × 106 with a freestream turbulence intensity of 1.02%. The particle image velocimetry (PIV) and hotwire data were used to illustrate the vortex dynamics, growth of perturbations, and intermittency. The onset and end of transition progressively shift upstream, resulting in a reduction of the laminar shear layer length and bubble length with increasing APG. Interestingly, the flow features exhibit self-similarity in velocity profiles and the growth rate of velocity fluctuations when normalized against the bubble length. The formation of two-dimensional Kelvin–Helmholtz (K–H) rolls is apparent in the beginning, resulting in the selective amplification of frequency and exponential growth of fluctuations. Linear stability theory explains the most amplified frequency and phase speed of convective vortices, apart from the growth of disturbances. Analysis of LES data reveals intricate inviscid–viscous interactions that trigger shear layer breakdown. In brief, evolving perturbations within the braid region of vortices in the latter half interact with the advecting K–H rolls, culminating in the breakdown and the onset of turbulent flow downstream.

High level GPU-accelerated 2D PIV framework in Python

The Particle Image Velocimetry (PIV) method is widely used for optical measurment of flow velocity fields. This paper demonstrates the possibilities of using high-level libraries for GPU-accelerated PIV data analysis in Python. The Torch PIV library for the analysis of 2D PIV experiments based on the deep learning framework PyTorch with CUDA support was developed. The library implements a multi pass cross-correlation FFT PIV algorithm with an interrogation window shift. The chosen implementation does not require compilation from the user, has a compact codebase, is able to run both on the CPU and the GPU depending on the user choice, and also it is as flexible as the Python module. In this work, the performance of the CPU version of the developed method was compared with existing open source implementations. It is shown that the main functions of the developed module can be executed on the GPU at the speed of CUDA implementations. The developed library is tested on synthetic images and experimental data. Program SummaryProgram title: TorchPIV.CPC Library link to program files: https://doi.org/10.17632/yw43vjc36h.1.Developer's repository link: https://github.com/NikNazarov/TorchPIV.Licensing provisions: MIT license.Programming language: Python.Nature of problem: PIV experiments often require analyzing a large number of images in order to determine the statistical characteristics of the flow. GPUs are actively used in this field to speed up the data analysis process. Open-source software solutions to this problem usually require build and are difficult to integrate into the analysis pipelines.Solution method: The main feature of the developed module is its flexibility and simple distribution. The module is cross-platform, and installation does not require compilation from the user. The developed library can be imported as an ordinary Python module, at the same time it allows to get a significant performance gain when analyzing PIV experiments by using NVIDIA GPUs. Implementation in pure Python allows the module to serve as a backend for more complex experimental data processing systems. The core library of this method is one of the most reliable and widespread in the field of machine learning.

Segmental biomechanics of the normal and glaucomatous human aqueous outflow pathway

The conventional aqueous outflow pathway, encompassing the trabecular meshwork (TM), juxtacanalicular connective tissue (JCT), and inner wall endothelium of Schlemm's canal (SC), governs intraocular pressure (IOP) regulation. This study targets the biomechanics of low-flow (LF) and high-flow (HF) regions within the aqueous humor outflow pathway in normal and glaucomatous human donor eyes, using a combined experimental and computational approach. LF and HF TM/JCT/SC complex tissues from normal and glaucomatous eyes underwent uniaxial tensile testing. Dynamic motion of the TM/JCT/SC complex was recorded using customized green-light optical coherence tomography during SC pressurization in cannulated anterior segment wedges. A hyperviscoelastic model quantified TM/JCT/SC complex properties. A fluid-structure interaction model simulated tissue-aqueous humor interaction. FluoSpheres were introduced into the pathway via negative pressure in the SC, with their motion tracked using two-photon excitation microscopy. Tensile test results revealed that the elastic moduli of the LF and HF regions in glaucomatous eyes are 3.5- and 1.5-fold stiffer than the normal eyes, respectively. The FE results also showed significantly larger shear moduli in the TM, JCT, and SC of the glaucomatous eyes compared to the normal subjects. The LF regions in normal eyes demonstrated larger elastic moduli compared to the HF regions in glaucomatous eyes. The resultant strain in the outflow tissues and velocity of the aqueous humor in the FSI models were in good agreement with the digital volume correlation and 3D particle image velocimetry data, respectively. This study uncovers stiffer biomechanical responses in glaucomatous eyes, with LF regions stiffer than HF regions in both normal and glaucomatous eyes. Statement of significanceThis study delves into the biomechanics of the conventional aqueous outflow pathway, a crucial regulator of intraocular pressure and ocular health. By analyzing mechanical differences in low-flow and high-flow regions of normal and glaucomatous eyes, this research unveils the stiffer response in glaucomatous eyes. The distinction between regions' properties offers insights into aqueous humor outflow regulation, while the integration of experimental and computational methods enhances credibility. These findings have potential implications for disease management and present a vital step toward innovative ophthalmic interventions. This study advances our understanding of glaucoma's biomechanical basis and its broader impact on ocular health.

Reconstruction of flow field with missing experimental data of a circular cylinder via machine learning algorithm

In this study, artificial neural networks (ANNs) have been implemented to recover missing data from the particle image velocimetry (PIV), providing quantitative measurements of velocity fields. Due to laser reflection or lower intensity of particles in the interrogation area, the reconstruction of erroneous velocity vectors is required. Therefore, the distribution of time-averaged and normalized flow characteristics around a circular cylinder has been demonstrated as streamwise and cross-stream velocities at Re = 8000. These velocity components have been given for different regions at x/D = 0.5, x/D = 1.25, x/D = 2, and y/D = 0. These stations have been chosen to estimate missing data for near-wake, mid-wake, far-wake, and symmetry regions. The missing data ratios (A*) for 0.5 ≤ x/D ≤ 2 are A* = 3.5%, 7%, and 10%. In addition, these values are A* = 4%, 8%, and 12% for y/D = 0, while A* = 7.5% for the shaded region. The increment of area positively affects the estimation results for near-wake and mid-wake regions. Moreover, the errors tend to decrease by moving away from the body. At y/D = 0, increasing the area negatively influences the prediction of the results. The mean velocity profiles of predicted and experimental data have also been compared. The missing data have been predicted with a maximum percentage error of 3.63% for horizontal stations. As a result, the ANN model has been recommended to reconstruct PIV data.

An analysis of the influence of a generic building on tornadic flow fields using high-frequency PIV and point velocity measurements

The current practice of implementing tornadic wind actions on structures in building codes is not well established compared to atmospheric boundary layer winds. Given there is a lack of velocity measurements in actual tornados close to the surface, there is a body of research that is currently looking into physical simulations of tornado-like vortices (TLVs). Our study contributes to this field of research by looking into the aerodynamic effects a generic building exerts on TLVs that are stationary and positioned above the building. Our study also investigated the differences among two distinctively different velocity measurement techniques in TLV flows—Cobra probe and particle image velocimetry (PIV). We further examine how different TLV intensities affect the results by considering TLVs that correspond to (Enhanced Fujita) EF1- and EF2-rated tornadoes in the atmosphere. We show that the velocity fluctuations in Cobra probe records are higher than in PIV measurements despite both velocity records being sampled at 200 Hz. The discrepancy is due to the inherent spatial filtering used in PIV data processing. A larger discrepancy between the two measurement techniques was observed in the EF1 TLV. The presence of the building affected EF1 more than EF2 TLV due to the larger core diameter of the latter vortex. The diameter of the circumference of the maximum tangential velocity in EF1 and EF2 TLVs undisturbed by the building was 1.6 and 2.1 times larger than the diagonal length of the building. By comparing our results against prior research on the same TLVs, but without a building in the flow, we concluded that the building had the strongest effect in amplifying vertical velocity in the EF2 TLV. The significance of this result to wind loading and structural responses requires further wind engineering analyses. The circular symmetry of EF1 was more affected than in EF2 TLV due to its smaller size relative to the building.

Flow control over a circular cylinder using vortex generators: Particle image velocimetry analysis and machine-learning-based prediction of flow characteristics

Controlling the flow around circular cylinders is crucial to mitigate vortex-induced vibrations and prevent structural damage in a range of applications, such as marine and offshore engineering, tall buildings, long-span bridges, transport ships, and heat exchangers. In this study, we aimed to control the turbulent flow structure around a circular cylinder by placing vortex generators (VGs). We examined the flow structure using particle image velocimetry (PIV). This enabled quantitative data acquisition, intuitive flow visualization, and drag coefficient determination from PIV data. We developed artificial neural network (ANN) models that successfully predict both mean and instantaneous flow characteristics for different scenarios. Our findings show that using VGs elongated the wake and increased vortex formation lengths while reducing velocity fluctuations and the drag coefficient. A minimum drag coefficient of 0.718 was achieved with VGs oriented at α = 60° & β = 60°, reducing the drag by 35.3% compared to the bare cylinder. The drag coefficient exhibited a substantial inverse correlation with both wake and vortex formation lengths. This study is significant for controlling flow structures, providing detailed insights into the near-wake region, and highlighting the potential applications of machine learning in fluid dynamics.