Cylinder Wake Flow Research Articles

Cylinder wake flow in confined channel and its active control by sweeping jets

Cylinder wake flow in confined channel and its active control by sweeping jets

Stiefel manifold interpolation for non-intrusive model reduction of parameterized fluid flow problems

Many engineering problems are parameterized. In order to minimize the computational cost necessary to evaluate a new operating point, the interpolation of singular matrices representing the data seems natural. Unfortunately, interpolating such data by conventional methods usually leads to unphysical solutions, as the data live on manifolds and not vector spaces. An alternative is to perform the interpolation in the tangent space to the Grassmann manifold to obtain interpolated spatial modes. Temporal modes are afterwards determined via the Galerkin projection of the high-fidelity model onto the interpolated spatial basis. This method, which is known for some fifteen years, is intrusive. Recently, Oulghelou and Allery (JCP, 2021) have proposed a non-intrusive approach (equation-free), but requiring the resolution of two low-dimensional optimization problems after interpolation. In this paper, a non-intrusive alternative based on Interpolation on the Tangent Space of the Stiefel Manifold (ITSSM) is presented. This approach has the advantage of not requiring a calibration phase after interpolation. To assess the method, we compare our results with those obtained using global POD on the one hand, and two methods based on Grassmann interpolation on the other. These comparisons are performed for two classical configurations encountered in fluid dynamics. The first corresponds to the one-dimensional non-linear Burgers' equation. The second example is the two-dimensional cylinder wake flow. We show that the proposed strategy can accurately reconstruct the physical quantities associated with a new operating point. Moreover, the estimation is fast enough to allow real-time computation.

On the aeroacoustic simulation and far-field noise modeling of a cylinder turbulent wake at the Reynolds number of 3900

The turbulent wake flows of circular cylinders have been under extensive study due to their strong fluctuating forces and noise levels, yet the relationship between them has rarely been examined. In this study, a high-fidelity numerical simulation is performed for a cylinder wake flow at the Reynolds number of 3900, aimed at far-field noise modeling based on the investigation of noise source characteristics. Despite irregular multi-scale turbulent wake eddies, the primary vortex shedding that resembles the Karman vortex shedding is observed, and the modes extracted by the dynamic mode decomposition technique at the primary shedding frequency and its higher harmonics appear as nearly two-dimensional spanwise structures. A non-permeable Ffowcs Williams and Hawkings (FW-H) approach is used to formulate the far-field noise spectra as the surface integral of unsteady flow samples on the cylinder surface and the volume integral of unsteady stress in the turbulent wake. The former can be modeled as the dipole sound of unsteady lift and drag, and the latter can be modeled as the tonal noise of discrete two-dimensional (i.e., spanwise-averaged) vortex modes. The results show that the vortex sound is extremely weak. Thus, the far-field noise can be modeled almost entirely by the aerodynamic forces, and the quantitative relationship between them is established by the convective FW-H solutions.

Aeroacoustic simulation of bluff bodies with protrusions at moderate Reynolds number

This paper presents an evaluation of passive control methods that employ surface protrusions to mitigate the aerodynamic sound generated from a cylinder wake flow. Building on previous designs optimized for low Reynolds numbers (Re = 150) through adjoint-based aeroacoustic shape optimization, this study investigated the performance under a moderate Reynolds number (Re = 67 000) condition typical of mechanical engineering applications using aeroacoustic simulations based on the lattice Boltzmann method. Three configurations of surface protrusions were tested, all of which were found to significantly reduce the mean drag by at least 45% compared with that of an unmodified circular cylinder. Designs featuring rear protrusions outperformed the conventional splitter plate in terms of the sound reduction performance, with symmetrical protrusions on both the front and rear surfaces achieving a tonal sound reduction of 13 dB. However, a specific protrusion design increased the low-frequency sound owing to the intensified large-scale flow separation. These findings highlight the effectiveness of rear protrusions in suppressing wake oscillations and dipole sound generation in the subcritical Reynolds number range. Moreover, the study revealed the need to tailor the front protrusion shape to the Reynolds number for performance optimization.

Closed-loop plasma flow control of a turbulent cylinder wake flow using machine learning at Reynolds number of 28 000

Machine learning is increasingly used for active flow control. In this experimental study, alternating-current dielectric barrier discharge plasma actuators are deployed for the closed-loop intelligent control of the flow around a cylinder at a Reynolds number of 28 000 based on the velocity feedback from two hot-wire sensors placed in the wake. Variations in the cylinder drag are monitored by a load cell, and the temporal response of the wake flow field is visualized by a high-speed particle image velocimetry system working at 1 kHz. The high-speed control law is operated using a field programmable gate array optimized by genetic programing (GP). The results show that the peak drag reduction achieved by machine learning is of similar magnitude to that of conventional steady actuation (∼15%), while the power saving ratio is 35% higher than with conventional techniques because of the reduced power consumption. Analysis of the best GP control laws shows that the intensity of plasma actuation should be kept at a medium level to maximize the power-saving ratio. When compared with the baseline uncontrolled flow, the best controlled cases constrain the meandering motion of the cylinder wake, resulting in a narrow stabilized velocity deficit zone in the time-averaged sense. According to the results of proper orthogonal decomposition and dynamic mode decomposition, Karman vortex shedding is promoted under the best GP control.

Flow modulation mechanism in a cylinder with corrugated surfaces

Flow modulation mechanism in a cylinder with corrugated surfaces is investigated by wind tunnel experiments at Reynolds number Re=25 600. Experimental results include surface pressure measurement for the cylinder wall and particle image velocimetry (PIV) for the wake flow. A pair of corrugated surfaces are symmetrically installed on the cylinder wall. Corrugated surfaces are distributed at three different locations on the cylinder wall, i.e., the windward part (case 1), leeward part (case 2), and lateral part (case 3). Experimental results show that corrugated surfaces can modify surface pressure, aerodynamic forces, and vorticity evolution of the cylinder flow. Compared with the natural cylinder (baseline case), the mean drag and fluctuating lift forces of case 1 are reduced by 58% and 82%, which are optimal among all test cases. Flow modulation effects of case 2 on global cylinder wake flow are unobvious and that of case 3 are between cases 1 and 2. Corrugated surfaces can also modify modal properties of proper orthogonal decomposition (POD) of the cylinder wake. Moreover, characteristics of recirculation bubbles, velocity deficits, turbulence kinetic energy, and Reynolds stresses in the wake are all modulated. The main flow modulation mechanism is that shear-layer shapes and streamline distributions near the corrugated surfaces are changed based on zoom-in PIV results on the cylinder near-wall region.

Numerical investigation on the cavitating wake flow around a cylinder based on proper orthogonal decomposition

The non-cavitating and cavitating wake flow of a circular cylinder, which contains multiscale vortices, is numerically investigated by Large Eddy Simulation combined with the Schnerr–Sauer cavitation model in this paper. In order to investigate the spatiotemporal evolution of cavitation vortex structures, the Proper Orthogonal Decomposition (POD) method is employed to perform spatiotemporal decomposition on the cylinder wake flow field obtained by numerical simulation. The results reveal that the low-order Proper Orthogonal Decomposition modes correspond to large-scale flow structures with relatively high energy and predominantly single frequencies in both non-cavitating and cavitating conditions. The presence of cavitation bubbles in the flow field leads to a more pronounced deformation of the vortex structures in the low-order modes compared to the non-cavitating case. The dissipation of pressure energy in the cylinder non-cavitating wake occurs faster than the kinetic energy. While in the cavitating wake, the kinetic energy dissipates more rapidly than the pressure energy.

Numerical investigation of energy loss distribution in the cavitating wake flow around a cylinder using entropy production method

The wake flow of a circular cylinder is numerically investigated by Large Eddy Simulation (LES) combined with the Schnerr–Sauer cavitation model. By comparing entropy production in the presence or absence of cavitation, the energy loss distribution in the wake flow field of a cylinder is explored, shedding light on the interactions between multiscale vortex systems and cavitation. The comparative results reveal that, under non-cavitating conditions, the energy loss region in the near-wake area is more concentrated and relatively larger. Energy dissipation in the wake flow field occurs in regions characterized by very high velocity gradients, primarily near the upper and lower surfaces of the cylinder near the leading edge. The influence of cavitation bubbles on entropy production is predominantly observed in the trailing-edge region (W1) and the near-wake region (W2). The distribution trends of wall entropy production on the cylinder’s surface are generally consistent in both conditions, with wall entropy production primarily concentrated in regions exhibiting high velocity gradients.

Dynamics and dispersion of inertial particles in circular cylinder wake flows: A two-way coupled Eulerian–Lagrangian approach

In this paper, the motion of inertial particles in three-dimensional (3D) unsteady cylindrical wake flow is investigated by a two-way coupled Eulerian–Lagrangian approach. At different flow Reynolds numbers (Re), the corresponding striking dynamic property and dispersion mechanism of four particle classes have been studied, with inertia parameterized by means of Stokes number (Sk). It is found that inertial particles with lower Stokes number are expelled from vortex cores, and coherent voids encompass the local Kármán vortex cells. As Stokes number increases, a low velocity particle channel could be formed, which almost coincides with the results in the literature. Moreover, with the increase of Reynolds number, numerous irregular coherent voids are observed in the cylinder wake, and the high-speed particles follow the fluid flow closely when they are contained in the vortices. Although the centrifugal force of Kármán vortex cells significantly affects the dynamics of inertial particles, the fluid flow modulation is believed to be responsible for the distinctive particle dispersion patterns in the vortex streets. For particles with medium inertia, the two-way coupled modulation weakens the centrifugal effect of vortex structures on the particles. This trend declines with the increase of Reynolds number, and vanishes with light particles, while both two-way coupled modulation and the centrifugal effect of vortex structures are almost equally effective with heavy particles. The investigations contribute to a better understanding of the particle-laden flows in practical applications, which will benefit the optimized design of certain machinery and equipment for the industry.

Modulation of wake evolution, separation, and radiated noise by a cylinder with porous media cladding

In this paper, the effects of porous media parameters on circular cylinder wake flow and radiation noise are investigated using large eddy simulations and Ffowcs Williams–Hawkings acoustic analogy. We performed three-dimensional numerical simulations for flow around the cylinder coated with a porous layer of different pores per inch in a subcritical flow regime (ReD=4.7×104) to explore the control mechanism of porous media on wake and radiation noise. The results show that the application of porous media significantly alters the separation pattern behind the cylinder and stabilizes the shear layer detached from the cylinder. The existence of porous layers leads to the transformation of chaotic and irregular vortex structures into more orderly vortices. Moreover, this study also reveals that the cylinder coated with high pore density can provide the desired noise reduction. The analysis of vortex sound theory indicates that porous media reduces the interaction area and magnitude of the positive and negative Lamb vector divergences, which is beneficial for drag reduction and noise attenuation. In addition, the comparison of sound pressure contours shows that the application of porous media does not change the radiation mode of noise, but the porous media with high pore density helps to decrease the generation of noise and intensity of the sound source.

Spatiotemporal parallel physics-informed neural networks: A framework to solve inverse problems in fluid mechanics

Physics-informed neural networks (PINNs) are widely used to solve forward and inverse problems in fluid mechanics. However, the current PINNs framework faces notable challenges when presented with problems that involve large spatiotemporal domains or high Reynolds numbers, leading to hyper-parameter tuning difficulties and excessively long training times. To overcome these issues and enhance PINNs' efficacy in solving inverse problems, this paper proposes a spatiotemporal parallel physics-informed neural networks (STPINNs) framework that can be deployed simultaneously to multi-central processing units. The STPINNs framework is specially designed for the inverse problems of fluid mechanics by utilizing an overlapping domain decomposition strategy and incorporating Reynolds-averaged Navier–Stokes equations, with eddy viscosity in the output layer of neural networks. The performance of the proposed STPINNs is evaluated on three turbulent cases: the wake flow of a two-dimensional cylinder, homogeneous isotropic decaying turbulence, and the average wake flow of a three-dimensional cylinder. All three turbulent flow cases are successfully reconstructed with sparse observations. The quantitative results along with strong and weak scaling analyses demonstrate that STPINNs can accurately and efficiently solve turbulent flows with comparatively high Reynolds numbers.

Improved deep learning method for accurate flow field reconstruction from sparse data

The analysis of flow field is essential for the fluid dynamics and the internal mechanism of hydraulic machinery. The use of deep learning technology has revolutionized the field of fluid dynamics by not only reducing computational power and time, but also improving the quality of experimental measurements and numerical simulations. However, certain numerical computing errors can lead to inaccurate simulation results with inadequate data. Fortunately, deep learning technology can accurately reconstruct distorted results, making flow mechanism research more convenient. In this study, a novel super-resolution convolutional neural network with a u-shaped architecture (SRUnet) model was developed by combining the benefits of super-resolution convolutional neural network (SRCNN) and convolutional neural network with the u-shaped architecture (U-net) to address the challenge of reconstructing turbulence flow field images with low resolution. The SRUnet model was validated with small datasets and was shown to deliver high-precision results when applied to reconstruct the velocity flow field of the two-dimensional (2D) cylinder wake flow. Furthermore, the SRUnet model's training and testing performance surpasses that of SRCNN and U-net models, producing more under-resolved details. When compared to the quality of the velocity flow field reconstruction of SRCNN and U-net models with the same hyper-parameter values, the SRUnet model produced 5.35% and 14.22% higher average values of the peak signal-to-noise ratio (PSNR) when low-resolution velocity field images were used as model inputs for the testing dataset reconstruction. Additionally, the average values of the structural similarity index metric (SSIM) were 1.09% and 3.33% higher than those of SRCNN and U-net models. When super-low resolution flow field images were used as model inputs for the testing dataset reconstruction, the average values of the PSNR metric were 8.31% and 2.76% higher than those of SRCNN and U-net models, respectively. The average values of the SSIM metric were also 5.81% and 2.25% higher than those of SRCNN and U-net models, respectively.

Passive control of wake flow behind a square cylinder using a flat plate

In this study, control over the wake flow of a single square cylinder exercised by a flat plate attached to the rear side of the cylinder is analyzed numerically via the lattice Boltzmann method. The Reynolds number (Re) is fixed at 150, and the length of the plate is varied from 0.1 to 8.5. Three distinct flow modes are observed in this study: unsteady, transient, and steady flow in the cases of plate lengths (L) in the ranges 0.1 ≤L≤ 6.5, 6.75 ≤L≤ 7.5, and 7.75 ≤L≤ 8.5, respectively. The streamlines exhibit different flow structures, termed hairpin-like, ellipse-like, and elongated bubble-like, at different values of L. Complete wake control is achieved at plate lengths beyond 7.75. This study reveals that the drag and lift coefficients exhibit unsteadiness at short plate lengths in early time steps, but as the plate length increases, unsteadiness slows down and eventually disappears, confirming the steady flow pattern. The mean drag coefficient (CDm), Strouhal number (St), and root-mean-square value of drag and lift coefficients (CDrms; CLrms) are reduced by maximums of 23.5%, 100%, 84.6%, and 99.5%, respectively, as a result of the presence of the plate.

Dairesel Silindir Çevresindeki Pasif Akış Kontrolünün Sayısal İncelenmesi

The effects of a perforated cylinder on the passive flow control around a circular cylinder mounted on a wall were investigated. The perforated cylinder was placed outside of the single circular cylinder concentrically. The large-eddy simulation was used to resolve the flow field. The study was aimed at both reducing the drag coefficient of the single cylinder and controlling the fluctuating forces acting on the single cylinder caused by vortex shedding in the downstream wake. The results showed that the structure of the downstream wake flow of the single cylinder changed significantly after placing the perforated cylinder. For example, von Karman vortices disappeared, and the maximum magnitude of turbulent kinetic energy, TKE, in the downstream wake was reduced. The time-averaged drag coefficient of the single cylinder was decreased by 69%. In addition, the maximum value of the lift coefficient of the single cylinder was reduced by eight times when the perforated cylinder was placed outside the single cylinder.

A comparative study of data-driven modal decomposition analysis of unforced and forced cylinder wakes

The present study on the recognition of coherent structures in flow fields was conducted using three typical data-driven modal decomposition methods: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and Fourier mode decomposition (FMD). Two real circular cylinder wake flows (forced and unforced), obtained from two-dimensional particle image velocimetry (2D PIV) measurements, were analyzed to extract the coherent structures. It was found that the POD method could be used to extract the large-scale structures from the fluctuating velocity in a wake flow, the DMD method showed potential for dynamical mode frequency identification and linear reconstruction of the flow field, and the FMD method provided a significant computational efficiency advantage when the dominant frequency of the flow field was known. The limitations of the three methods were also identified: The POD method was incomplete in the spatial–temporal decomposition and each mode mixed multiple frequencies leading to unclear physics, the DMD method is based on the linear assumption and thus the highly nonlinear part of the flow field was unsuitable, and the FMD method is based on global power spectrum analysis while being overwhelmed by an unknown high-frequency flow field.

Comparative analysis of machine learning methods for active flow control

Machine learning frameworks such as genetic programming and reinforcement learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, benchmarking some of their most representative algorithms against global optimization techniques such as Bayesian optimization and Lipschitz global optimization. First, we review the general framework of the model-free control problem, bringing together all methods as black-box optimization problems. Then, we test the control algorithms on three test cases. These are (1) the stabilization of a nonlinear dynamical system featuring frequency cross-talk, (2) the wave cancellation from a Burgers’ flow and (3) the drag reduction in a cylinder wake flow. We present a comprehensive comparison to illustrate their differences in exploration versus exploitation and their balance between ‘model capacity’ in the control law definition versus ‘required complexity’. Indeed, we discovered that previous RL control attempts of controlling the cylinder wake were performing linear control and that the wide observation space was limiting their performances. We believe that such a comparison paves the way towards the hybridization of the various methods, and we offer some perspective on their future development in the literature of flow control problems.

Near wake flow and forced convection heat transfer of sinusoidal wavy cylinder based on flow decomposition

Based on flow decomposition we investigate the near wake flow and forced convection heat transfer around sinusoidal wavy cylinders, with an attempt to deepen our understanding of inherent correlations between near wake flow and heat transfer/transport. Large eddy simulations (LES) are conducted at a Prandtl number Pr = 0.7 and a subcritical Reynolds number Re = 3.0 × 103 for the unsteady flow of the sinusoidal wavy cylinder of different wavelengths λ (= 1.89, 3.79 and 6.06Dm) and a fixed wave amplitude a (= 0.152Dm), where Dm is the mean diameter of the cylinder. The near wake flow of the wavy cylinder, together with heat transport, is decomposed via the traditional triple decomposition and a newly-proposed quadruple decomposition, taking into account dominant periodic vortex shedding and/or spanwise heterogeneity of the flow. While heat transfer from the surface of the wavy cylinder to the surrounding flow is distinctly different from that of the smooth cylinder, results from the flow decomposition reveal intrinsic characteristics of the near wake flow and heat transport for the wavy cylinder with different wavelengths. It is observed that heat transport in the near wake of the wavy cylinder is predominantly associated with the coherent spanwise vortices of dominant shedding frequency as well as the spanwise time-invariant components of the mean flow. It is also found that the major contributor to Reynolds stresses (heat flux) is the temporal random stresses (temporal random heat flux) in the near wake of the wavy cylinder. Furthermore, results from the quadruple decomposition show that the spanwise-averaged heat flux in the near wake of the smooth cylinder and the wavy cylinder with λ = 1.89Dm encompasses both spatiotemporal random and temporal dispersive heat fluxes outside the recirculation bubble and only the former heat flux inside the recirculation bubble. However, the spanwise-averaged heat flux in the near wake of the wavy cylinder with λ = 3.79 or 6.06Dm is profoundly and exclusively contributed by the spatial dispersive heat flux outside the recirculation bubble.

Effects of free-stream turbulence on the near wake flow and aerodynamic forces of a square cylinder

Despite a large volume of preceding studies on the effects of free-stream turbulence (FST) on the aerodynamic forces of a square cylinder, limited knowledge is available regarding the evolutionary characteristics of flow with the change of turbulence integral length scale and turbulence intensity. This paper presents a systematic experimental study of the near wake flow and aerodynamic forces of a two-dimensional (2D) square cylinder in grid-generated turbulent flows and smooth flow. Particular attention is devoted to exploring the respective influences of turbulence integral length scale and turbulence intensity. The effect of FST with a higher-level turbulence intensity than previous investigations is also investigated. The near wake flow and aerodynamic forces are studied by particle image velocimetry (PIV) and synchronous pressure measurements. The proper orthogonal decomposition (POD) analysis is also performed to further understand the effects of FST on the coherent structures of fluctuating pressures around the square cylinder. The results show that the FST with a larger turbulence integral length scale and higher turbulence intensity can cause an increase in the vortex formation length, weaker vortex shedding process and interactions between the oppositely signed vortex rows. These changes lead to significant reductions in Reynolds normal and shear stresses, turbulence kinetic energy and fluctuating lift forces. A completely different flow pattern featured with flow reattachment and reseparation is interestingly captured on the square cylinder in a highly turbulent flow with a turbulence intensity of 20%. This flow pattern results in an obvious mean pressure recovery on the side surfaces and much larger pressure fluctuations near the leading edges and fluctuating torsional moments compared to those in smooth flow and turbulent flows with lower turbulence intensities.

Strouhal number universality in high-speed cylinder wake flows

Experiments with circular cylinders in high-speed flow reveal new universal behavior associated with coherent oscillations in the near-wake region. The Strouhal number shows invariance with respect to both the flow Reynolds number and Mach number. Further, flow disturbances in the top and bottom halves of the wake have an anti-symmetric relationship.

Event-based imaging velocimetry applied to a cylinder wake flow in air

Event-based vision is a new upcoming field within the field of computer vision. Unlike conventional frame-based imaging, event-based vision (or dynamic vision sensing) asynchronously records binary signals of contrast changes at the pixel level with microsecond resolution. Event-based imaging of small particles illuminated by a laser or other continuous light source generates streaks of events in a space-time domain. From this data the particles’ position in both space and time can be inferred. The present work explores the possibilities of harnessing the potentials of event- based vision for fluid flow measurement with focus on two-dimensional, two-component velocity field estimation at particle image densities corresponding to that of conventional PIV. Three different motion detection schemes are implemented. Measurements of a cylinder wake flow demonstrate that EBIV provides time resolved velocity data that is suitable for the retrieval of flow statistics as well as spectral and modal analysis. The present work is an extension of the recently published Exp. Fluids article by the authors on event-based imaging velocimetry and provides further details on current limitations related to event-based imaging.