Particle Image Velocimetry Experiments Research Articles

Novel Venturi injector reactor design and application in ammonia nitrogen wastewater treatment

The Venturi injector reactor has a robust and durable design and is highly efficient and reliable. The flow field control in the Venturi reactor is an effective way to enhance gas−liquid mass transfer. In this study, a novel Venturi injector reactor with an expandable self-priming air inlet and a second water inlet in the diffusion section was designed by combining particle image velocimetry (PIV) experiments with a computational fluid dynamics simulation. This novel design increased the gas holdup, generated smaller and more uniform bubbles, and enhanced mass transfer. Furthermore, we applied the novel Venturi device to the biological degradation of ammonia nitrogen wastewater. The total gas holdup in the novel Venturi was higher than in the conventional Venturi injector reactor by 0.0056 on average. The minimum bubble diameter was approximately 0.71 mm. Moreover, the ammonia nitrogen removal efficiency of the novel Venturi equipment with the second inlet was 2 and 1.1 times higher than that of conventional aeration and conventional Venturi device, respectively. This study provides a theoretical basis and practical significance for improving the performance and efficient degradation of the ammonia nitrogen concentration in Venturi injector reactors.

Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior.

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamics (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.

Flow data forecasting for the junction flow using artificial neural network

The present study aims to predict the flow characteristics downstream of a cylinder, which is the result of junction flow using an Artificial Neural Network (ANN) algorithm. The training and test datasets were obtained through Particle Image Velocimetry (PIV) experiments. The experiments were conducted at Reynolds numbers Re = 1.5 x 103 and 4 x 103 based on the cylinder diameter (D) at dimensionless measurement heights (Z = h/D) of Z1 = 0.06, Z2 = 0.4, Z3 = 0.8, and Z4 = 1.6 respectively. While the X- and Y-coordinate and dimensionless measurement location (Z) variables are employed as inputs to the ANN model, the output variables are vorticity ⟨ω⟩, streamwise velocity ⟨u⟩, and transverse velocity ⟨v⟩, which are derived from the time-averaged flow data. Modeling flow characteristics with easily obtainable independent variables without flow and physical properties was considered. Three various training algorithms such as Levenberg Marquardt (LM), Resilient Backpropagation (RP), and Scaled Conjugate Gradient (SCG) were employed to assess and compare their prediction performance. The results indicate that the LM learning algorithm outperforms the RP and SCG algorithms, especially at low Reynolds (Re) numbers. The ANN model, trained with the LM algorithm, exhibits significant success, achieving R = 0.9816 correlation coefficient (R), MAE = 2.4250 m/s Mean Absolute Error (MAE), and RMSE = 3.3541 m/s Root Mean Square Error (RMSE) for streamwise velocity ⟨u⟩ data. Notably, the LM algorithm for the testing process demonstrates the best predictions at Re = 1.5x103, yielding R = 0.9779, MAE = 2.7417 m/s, and RMSE = 3.7493 m/s. The ANN-LM model's patterns closely align with experimental results, affirming its accuracy, which proves that the prediction of time-averaged velocity data solely based on spatial coordinates as input can be achieved successfully.

Experimental study of rotating direction for dual-axial swirlers on the flow field and combustion characteristics of aero-engine combustor

The flow field structure for aero-engine combustor is one of the most important factors for combustion performance. To investigate how the rotating direction of dual-axial swirlers affects the flow field and combustion characteristics, particle image velocimetry (PIV) experiments were conducted in the primary combustion and intermediate zones. Additionally, planar laser induced fluorescence (PLIF) was used to map the distribution of kerosene and OH in the primary combustion zone. The results show that the swirling jets in co-swirl exhibit greater jet momentum, resulting in a longer primary recirculation zone and a larger deflection angle of the primary jets. This ultimately leads to a higher recirculation rate compared to counter-swirl. The primary jets define the boundary of the flow field structure. The rotating direction primarily affects the flow field structure upstream of the primary jets, exerts a relatively weaker influence on the intermediate zone, which is mainly affected by the deflection direction of the primary jets, and essentially has no impact on the dilution jets. Co-swirl has a relatively stable flow field than counter-swirl, whereas the flow field in counter-swirl is more chaotic, featuring a greater number of vortex cores in the primary recirculation zone. The coupling between the primary and swirling jets affects the mass transfer direction in the intermediate zone, which is reflected in the fact that the mass transfer is from top-left to bottom-right in co-swirl, while it is in the opposite direction in counter-swirl. OH is predominantly found in the swirling shear layers, the primary and secondary combustion zones, whereas kerosene is mainly concentrated in the shear layers of swirling jets. The kerosene distribution in co-swirl shows a larger and more concentrated expansion angle compared to counter-swirl. In contrast, the OH distribution downstream of counter-swirl is broader, with more intense combustion due to enhanced kerosene decomposition and fuel-air mixing. The smaller primary recirculation zone in counter-swirl forces the primary combustion zone to extend downstream, leading to a more intense reaction in the secondary recirculation zone.

Experimental study on the interaction between particles and coherent structures in dilute liquid–solid two-phase turbulent boundary layer

In order to reveal the modulation of particles on turbulent coherent structures and particle concentration variations induced by turbulence, particle image velocimetry experiments were performed in a dilute liquid–solid two-phase turbulent boundary layer. Large-scale coherent structures, including streaks and hairpin vortices, were extracted in the liquid–solid two-phase turbulent boundary layer by using multiple methods, such as proper orthogonal decomposition and finite-time Lyapunov exponent. The results showed that the addition of particles thickened the buffer layer of the average velocity profile, weakened the streamwise turbulence intensity at y+ > 10, strengthened the wall-normal turbulence in the near wall region (y+ < 52), and weakened the Reynolds stress in the logarithmic law region. A more thorough study of the turbulent structure revealed that the particles caused the mean spacing of the near-wall streak structure to increase, hairpin vortex migration to slow down, the uplift of hairpin vortex to be suppressed, and the overall size of the hairpin vortex packet to be compressed. This demonstrated that particles could modulate turbulence by inhibiting the development of hairpin vortices.

Experimental investigation of hydrodynamics and suspension characteristics in a boiling stirred tank reactor with helical coils

The boiling stirred tank reactor (BSTR) is widely used in the chemical industry, while the relevant basic data of hydrodynamics and suspension characteristics for reactor design are quite limited. In this work, the hydrodynamics and suspension characteristics in a BSTR with helical coils (BSTR@HC) were studied using three types of impellers. Particle image velocimetry and visualization experiments reveal different flow fields and nucleation processes of three impellers. Suspension experiments show that superficial vapor velocity, solid loading, and particle size play an important role for just-suspension impeller speed Njsv and power consumption Pjsv, while cloud height is mainly determined by superficial vapor velocity. Among the three impellers, radial impeller shows the best suspension performance in BSTR@HC. Correlations of Njsv and Pjsv were developed with relative mean deviations of 2.7% and 4.6%, respectively. Furthermore, a model for the estimation of energy efficiency was developed by evaluate specific power consumption εjsv.

Global POD Modes From PIV Asynchronous Patches

A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (overlapping) spatial locations. When performing robotic volumetric Particle Image Velocimetry (PIV) (Jux et al., 2018), the large-scale mean velocity fields are estimated merging several measurements performed at different (adjacent) locations covered by the robot sequence. The proposed methodology leverages the definition of POD modes as eigenvectors of the spatial correlation matrix to obtain also large-scale modes. Performing measurements over overlapping (50-75%) regions allows to approximate the correlation matrix in the two adjacent domains. When applied over a sequence of views, this method has the potential to deliver full-domain POD modes spanning the volume covered by the robot sequence, even if different regions are covered asynchronously. This methodology is particularly well-suited for applications that seek to investigate large-scale flow structures, whenever the dynamic spatial range (DSR) of the measurement system does not allow to capture the whole domain at once. The methodology is validated using a 2D experimental dataset of a turbulent boundary layer, where patches are artificially created from splitting the PIV measurements, later used as ground truth to assess the results. Furthermore, we apply the technique to a 3D robotic volumetric PIV experiment of the flow around a wall-mounted cube.

Light Reflections Detection And Correction For Robotic Volumetric PIV

Laser light reflection mitigation in Particle Image Velocimetry (PIV) is crucial for accurate flow field measurements. While numerous methods exist for planar PIV, fewer have been developed for volumetric PIV systems, and in particular for coaxial setups like robotic volumetric PIV. Light reflections in volumetric PIV experiments result in high-intensity regions that corrupt particle detection and analysis. This study presents a novel approach for treating light reflections in robotic volumetric PIV experiments. The proposed method uses image filtering and masking techniques in the wavenumber space to separate particle images from reflection regions. The process involves decomposing the image signal into low- and high-wavenumber components using the 2D discrete Fourier transform (DFT) to then use a high-pass filter to attenuate the intensity of the reflection regions. Finally, a step of automated adaptive masking is applied to remove residual reflection areas that the filtering is not able to eliminate. The proposed approach is tested on experimental data obtained from experiments performed using robotic volumetric PIV on two different geometries: a side-view mirror and a rotating two-blade propeller. Comparison between raw and pre-processed images, as well as particle tracking results, is presented. The results from this data comparison show successful removal of reflection-induced artifacts in instantaneous images by using the spatial Fourier filter automated masking approach. The developed image pre-processing strategy effectively removes unsteady light reflection regions in robotic volumetric PIV images, preventing the appearance of spurious particle tracks and improving the accuracy of flow field measurements. The spatial gaps introduced by the masking procedure can be easily filled in via measurements from multiple directions, which are promptly achieved via the robotic volumetric PIV approach.

Effect of impeller rotational phase on the FDA blood pump velocity fields.

The Food and Drug Administration (FDA) blood pump is an open-source benchmark cardiovascular device introduced for validating computational and experimental performance analysis tools. The time-resolved velocity field for the whole impeller has not been established, as is undertaken in this particle image velocimetry (PIV) study. The level of instantaneous velocity fluctuations is important, to assess the flow-induced rotor vibrations which may contribute to the total blood damage. To document these factors, time-resolved two-dimensional PIV experiments were performed that were precisely phase-locked with the impeller rotation angle. The velocity fields in the impeller and in the volute conformed with the previous single blade passage experiments of literature. Depending on the impeller orientation, present experiments showed that volute outlet nozzle flow can fluctuate up to 34% during impeller rotation, with a maximum standard experimental uncertainty of 2.2%. Likewise, the flow fields in each impeller passage also altered in average 33.5%. Considerably different vortex patterns were observed for different blade passages, with the largest vortical structures reaching an average core radii of 7 mm. The constant volute area employed in the FDA pump design contributes to the observed velocity imbalance, as illustrated in our velocity measurements. By introducing the impeller orientation parameter for the nozzle flow, this study considers the possible uncertainties influencing pump flow. Expanding the available literature data, analysis of inter-blade relative velocity fields is provided here for the first-time to the best of our knowledge. Consequently, our research fills a critical knowledge gap in the understanding of the flow dynamics of an important benchmark cardiovascular device. This study prompts the need for improved hydrodynamic designs and optimized devices to be used as benchmark test devices, to build more confidence and safety in future ventricular assist device performance assessment studies.

Assimilating mean velocity fields of a shockwave–boundary layer interaction from background-oriented schlieren measurements using physics-informed neural networks

We propose a novel method to reconstruct mean velocity fields of turbulent shockwave–boundary layer interactions (SBLIs) from background-oriented schlieren (BOS) measurement data using physics-informed neural networks (PINNs). By embedding the compressible Reynolds-Averaged Navier–Stokes equations into the PINN loss function, we recover a full set of physical variables from only the density gradient as training data. This technique has the potential to generate velocity fields similar to particle image velocimetry (PIV) results from usually simpler planar BOS measurements, at the cost of some computational resources. We analyze our method's capabilities on two oblique SBLI cases: a high-fidelity Mach 2.28 direct numerical simulation dataset for validation and a Mach 2.0 wind tunnel experiment. We demonstrate the positive impact of different wall boundary constraints such as the wall shear stress and pressure distribution for enhancing the PINN's convergence toward physically accurate solutions. The predicted fields are compared with experimental PIV and other point measurements, while we discuss the accuracy, limitations, and broader implications of our approach for SBLI research.

Temporal consistency loss for physics-informed neural networks

Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations (PDEs) in a forward and inverse manner using neural networks. However, balancing individual loss terms can be challenging, mainly when training these networks for stiff PDEs and scenarios requiring enforcement of numerous constraints. Even though statistical methods can be applied to assign relative weights to the regression loss for data, assigning relative weights to equation-based loss terms remains a formidable task. This paper proposes a method for assigning relative weights to the mean squared loss terms in the objective function used to train PINNs. Due to the presence of temporal gradients in the governing equation, the physics-informed loss can be recast using numerical integration through backward Euler discretization. The physics-uninformed and physics-informed networks should yield identical predictions when assessed at corresponding spatiotemporal positions. We refer to this consistency as “temporal consistency.” This approach introduces a unique method for training physics-informed neural networks (PINNs), redefining the loss function to allow for assigning relative weights with statistical properties of the observed data. In this work, we consider the two- and three-dimensional Navier–Stokes equations and determine the kinematic viscosity using the spatiotemporal data on the velocity and pressure fields. We consider numerical datasets to test our method. We test the sensitivity of our method to the timestep size, the number of timesteps, noise in the data, and spatial resolution. Finally, we use the velocity field obtained using particle image velocimetry experiments to generate a reference pressure field and test our framework using the velocity and pressure fields.

Information Entropy Analysis of a PIV Image Based on Wavelet Decomposition and Reconstruction.

In particle image velocimetry (PIV) experiments, background noise inevitably exists in the particle images when a particle image is being captured or transmitted, which blurs the particle image, reduces the information entropy of the image, and finally makes the obtained flow field inaccurate. Taking a low-quality original particle image as the research object in this research, a frequency domain processing method based on wavelet decomposition and reconstruction was applied to perform particle image pre-processing. Information entropy analysis was used to evaluate the effect of image processing. The results showed that useful high-frequency particle information representing particle image details in the original particle image was effectively extracted and enhanced, and the image background noise was significantly weakened. Then, information entropy analysis of the image revealed that compared with the unprocessed original particle image, the reconstructed particle image contained more effective details of the particles with higher information entropy. Based on reconstructed particle images, a more accurate flow field can be obtained within a lower error range.

Laser Doppler velocimetry and particle image velocimetry measurements in a wind tunnel model of the jack rabbit II special sonic anemometer study

A 1:50 scale model of the Jack Rabbit II (JR-II) Mock Urban Environment (MUE) was constructed in the wind tunnel of the University of Arkansas. Laser Doppler Velocimetry (LDV) measurements show agreement between the approach wind characteristics in the tunnel model and field test. LDV velocity measurements within the MUE wind tunnel model also agree with the anemometry results obtained in JRII Special Sonic Anemometer Study (JRII-S) conducted in 2016.After demonstrating that the wind tunnel models the JRII-S field test with LDV measurements, Particle Image Velocimetry (PIV) experiments were conducted to provide velocity measurements that could not be obtained in the field tests. Velocity measurements in a horizontal plane parallel to the ground were made to compare to computational fluid dynamics (CFD) simulations of the JRII-S field tests found in the literature. PIV measurements taken in the wind tunnel model pointed out concerns with those simulations.

Machine learning approach to predict flow fields induced by internal solitary waves acting on mid-water structures based on particle image velocimetry experiments

Mid-water structures (MWSs) have important applications in the ocean to minimize the effects of environmental factors in the surface ocean and to avoid the effects of high pressure on the seabed. However, their operational positions are vulnerable to internal solitary waves (ISWs), which poses a significant challenge to the safe functioning of MWSs. Consequently, there is a pressing need to explore the flow field characteristics surrounding MWSs under the action of ISWs to establish a reliable foundation for structural design. In this study, experimental investigations were conducted to analyze the flow fields induced by ISWs acting on MWSs, employing the particle image velocimetry (PIV) method. The intricate details of the flow fields around the structure were thoroughly analyzed, and the impact of the structure's submerged depth on the ISW-induced flow fields was discussed. A machine learning approach is utilized to develop an Artificial Neural Network (ANN) model aimed at predicting the flow fields of ISWs. The experimental results are compared and analyzed alongside the predicted outcomes, focusing on the anomaly region, the tail field region, and the occluded region. The results show that the presence of the structure complicates the flow characteristics in the tail flow regions, and the flow patterns are constantly changing. The characteristics of the ISW flow fields around the structure vary with the different submerged depths of the MWS. Positioning the MWS above the wave trough results in an ascending trend followed by a descending trend in the strength of the surrounding flow field with increasing submerged depth. The ANN predictions for different types of regions of the flow fields show satisfactory accuracy. The model closely matches the PIV results in predicting the extremes and positions of horizontal velocities in the tail flow fields at different stages of ISW trough propagation. The ANN model can be recommended for predicting the flow fields of ISWs acting on MWSs. This study offers comprehensive insights into the characteristics of the flow fields induced by ISWs acting on MWSs, emphasizing the valuable contribution of machine learning methods to the field of fluid dynamics.

Influence mechanism of hydrocyclone main diameter on separation performance

The main diameter of hydrocyclone (HC) has significant effects on separation performance. However, the differences of flow field characteristics within different HCs have not been understood well yet, which makes it impossible to grasp the influence mechanism of the main diameter on the separation performance. Through the study of the migration trajectories of particles, it is found that the greater the diameter and the finer the particles, the more particles escape from the overflow outlet. The relationships between the longest residence time and the HC main diameter for particles with 10 and 15 μm were, respectively, clarified. The high-speed video and particle image velocimetry experiments were performed and found although the main diameter of a large-HC (LHC) is twice that of a mini-HC (MHC), its air-core diameter is much larger than twice that of MHC, which is not beneficial for separations. The axial velocity around the central axis area in LHC is higher than that of the MHC, which helps separate low-density discrete phases from the overflow outlet at a faster speed but not for the separations of high-density discrete phases from the underflow outlet. The angle of the locus of zero vertical velocity in MHC is larger than that of LHC, which is anticipated to enhance the separation efficiency for the high-density discrete phase. This study first reveals the influence mechanism of the HC main diameter on its separation performance from the perspective of the flow field characteristics, which would hopefully provide significant references for the applications of HCs.

Insights into the fluid dynamics of the Bach impeller

The Bach impeller is a novel impeller that was purposefully designed to reduce power consumption as well as shear stress levels in stirred bioreactors. Contrary to conventional impellers where stirring is achieved by the pushing action of the blades, the Bach impeller relies on a central spiral element which induces an up-pumping central vortex and discharges the flow in the tangential direction through vanes. This feature is new and relevant to the field of biochemical engineering where cell culture and microbial fermentation often make use of shear-sensitive, living organisms to produce biopharmaceuticals and medicines. This article offers a thorough characterisation of the fluid mechanics of this impeller by means of 2D phased- and time-resolved particle image velocimetry (PIV) experiments. Key flow dynamics parameters including velocity magnitudes, kinetic energy, and shear stresses were estimated for different combinations of impeller size, clearance, and speed. The standard impeller size, D/T=0.52, performed most promisingly in combination with an off-bottom clearance of C=0.6 T, as the elevated impeller height allowed to pull high momentum fluid to the upper part of the tank and therefore improved overall transport phenomena in the entire reactor volume. For this impeller diameter-clearance combination peak ensemble-averaged shear stresses at the vessel base were lower due to the impeller greater distance from the bottom. A distinctive feature of the Bach impeller was its "smooth operation" mode, where velocity fluctuations due to the vane passages are nearly negligible (∼5 % of ensemble-averaged kinetic energy). These aspects make of the Bach impeller an appealing option in the context of stem cell bioprocess applications.

Experimental dataset investigation of deep recurrent optical flow learning for particle image velocimetry: flow past a circular cylinder

Current optical flow-based neural networks for particle image velocimetry (PIV) are largely trained on synthetic datasets emulating real-world scenarios. While synthetic datasets provide greater control and variation than what can be achieved using experimental datasets for supervised learning, it requires a deeper understanding of how or what factors dictate the learning behaviors of deep neural networks for PIV. In this study, we investigate the performance of the recurrent all-pairs field transforms-PIV (RAFTs-PIV) network, the current state-of-the-art deep learning architecture for PIV, by testing it on unseen experimentally generated datasets. The results from RAFT-PIV are compared with a conventional cross-correlation-based method, Adaptive PIV. The experimental PIV datasets were generated for a typical scenario of flow past a circular cylinder in a rectangular channel. These test datasets encompassed variations in particle diameters, particle seeding densities, and flow speeds, all falling within the parameter range used for training RAFT-PIV. We also explore how different image pre-processing techniques can impact and potentially enhance the performance of RAFT-PIV on real-world datasets. Thorough testing with real-world experimental PIV datasets reveals the resilience of the optical flow-based method’s variations to PIV hyperparameters, in contrast to the conventional PIV technique. The ensemble-averaged root mean squared errors between the RAFT-PIV and Adaptive PIV estimations generally range between 0.5–2 (px) and show a slight reduction as particle densities increase or Reynolds numbers decrease. Furthermore, findings indicate that employing image pre-processing techniques to enhance input particle image quality does not improve RAFT-PIV predictions; instead, it incurs higher computational costs and impacts estimations of small-scale structures.

High-resolution PIV measurement over wind-generated waves

The interaction between wind and waves plays a significant role in the exchange of heat, aerosols and gases, thereby influencing our understanding of climate dynamics and air–sea interaction. Particle image velocimetry (PIV) has emerged as a valuable tool for investigating the intricate effects of small-scale waves on airflow characteristics in laboratory settings. However, previous PIV experiments have exhibited notable variability in spatial resolution, potentially affecting the accuracy of turbulence statistics, particularly in relation to small-scale waves such as capillary ripples. To systematically explore the impact of PIV spatial resolution on airflow characteristics over multi-scale wind-generated waves, we conducted high-resolution planar PIV experiments near the wave surface. We adjusted the spatial resolution of the results by modifying the spatial filter. Additionally, recognising the limitations of the high-resolution PIV system in terms of wall-normal and streamwise extent, we conducted larger field-of-view experiments to capture consecutive waveforms and achieve spatial averaging across the boundary layer. Consistent with existing literature, our findings illustrate the formation of a horizontal shear layer leading to airflow separation on the lee side of the wave, accompanied by a pronounced vorticity field and circulation region. Notably, analysis of the high-magnification dataset reveals localised airflow separation caused by small-scale capillary waves, phenomena not resolved by the large field-of-view set-up, underscoring the importance of adequate spatial resolution. Further analysis indicates that a spatial resolution larger than the size of the capillary waves leads to significant attenuation of the spanwise vorticity imposed by the small-scale waves. In this study, we also introduce a novel method relying to identify wave surfaces solely on PIV images, demonstrating its effectiveness in detecting capillary-scale waves.

Regulating turbulent separation by surface microstructures on a blunt plate

Microstructured surfaces can induce secondary flow and regulate flow structures of the turbulent separation flow. However, the mechanism governing the relationship between the microstructure size and the characteristic flow size remains unclear. In this study, the separated flow over a blunt flat plate with surface microstructures is studied using time-resolved particle image velocimetry experiments and implicit large-eddy simulations for the plate-thickness-based Reynolds number from 5.08×103 to 1.31×104. The ratio of the height of microstructures to the plate thickness (h/d) ranges from 0.01 to 0.1. Combining experimental and numerical results, the relationships between the separation bubble size and the microstructure size under different Reynolds numbers exhibit similarity when normalized by the separation bubble size of the smooth plate. The dimensionless separation bubble size decreases when the microstructure height increases and large microstructures (h/d = 0.1) exhibit good performance on reducing the flow separation. Near the leading edge, the distortion of two-dimensional vortices and the generation of three-dimensional hairpin vortices are promoted by the first several rows of large microstructures. Additionally, in the main separation region, secondary positive spanwise vortices emerge from large microstructures. Subsequently, the secondary vortices lift up and evolve into streamwise vortices. The characteristic scale of secondary vortices is represented by a significant peak in the spectra of spanwise wavenumbers, which is of the same magnitude as the height of large microstructures. Furthermore, increasing the microstructure height weakens the streamwise correlation of the flow, and the characteristic scale of the correlation is comparable to the height of large microstructures.

Manipulation of a turbulent boundary layer using sinusoidal riblets

We investigate experimentally the effects of micro-grooves on the development of a zero pressure gradient turbulent boundary layer at two different values of the friction Reynolds number. We consider both the well-known streamwise aligned riblets as well as wavy riblets, characterized by a sinusoidal pattern in the mean flow direction. Previous investigations by the authors showed that sinusoidal riblets yield larger values of drag reduction with respect to the streamwise aligned ones. We perform new particle image velocimetry experiments on wall-parallel planes to get insights into the effect of the sinusoidal shape on the near-wall organisation of the boundary layer and the structures responsible for the friction drag reduction and the turbulence generation. Conditional averages, aimed at identifying the topology of the low-speed streaks in the turbulent boundary layer, reveal that the flow is highly susceptible to wall manipulation. This is particularly evident in the cases that are associated with greater values of drag reduction. The results suggest a fragmentation and/or weakening of the streaks in the sinusoidal cases, that is triggered by the larger values of the wall-normal vorticity found at the streaks’ edges. The results are also confirmed by applying the variable interval spatial averaging events eduction technique. The turbulent kinetic energy budget also shows that the sinusoidal geometry significantly attenuates the turbulence production, hence supporting the idea of the manipulation of the turbulence regeneration cycle.