Fluid Mechanics Techniques Research Articles

Particle Shadow Velocimetry And Its Potential Applications And Advantages Vis-à-Vis Particle Image Velocimetry

Particle image velocimetry (PIV) is an established standard velocimetry technique in experimental fluid mechanics that involves determining a fluid flow velocity field from the motion of tracer particles illuminated by a laser sheet. The necessity of laser illumination poses challenges in certain applications, and is a potential entry barrier due to its complexity and high cost. A laser-free alternative to PIV is particle shadow velocimetry (PSV), which uses images of the shadows cast by the particles on the camera sensor under back-illumination, instead of the Mie scattering signal produced by laser illumination. This study aims to compare various aspects of PSV such as depth of field, seeding density, type of illumination required, particle size, image filtering, cost effectiveness, and limitations with those of PIV. PSV and PIV measurements are conducted in the wake of a flow past a cylinder and in a boundary layer developing over a flat plate. It is found that PSV is capable of achieving equivalent accuracy to PIV and is a viable alternative to PIV in certain applications where light sheet illumination creates experimental challenges.

Recent advances and effectiveness of machine learning models for fluid dynamics in the built environment

ABSTRACT Indoor environmental quality is crucial for human health and comfort, necessitating precise and efficient computational methods to optimise indoor climate parameters. Recent advancements in machine learning (ML) and computational fluid dynamics (CFD) are promising. However, applying ML to complex building airflow presents challenges. This research aims to investigate the integration of ML with CFD in the context of built environment applications using a systematic review approach. It highlights a critical knowledge gap: the need to synthesise innovative approaches that address the limitations of indoor modelling using data-driven ML methods. The review examines contemporary literature, identifying current developments and suggesting potential future directions. It delves into the innovations in combining ML with CFD to predict thermal comfort and indoor air quality, uncovering key limitations such as the lack of high-quality experimental data for training and validation, the computational complexity of detailed CFD simulations, and the interpretability issues of ‘black-box’ ML models. The emergence of data-driven techniques in fluid mechanics offers promising prospects for modelling in the built environment. Future research should focus on incorporating physics-based rules in ML models, adapting turbulence closure models for indoor flows, and enhancing model validation using real-world datasets. The research emphasises the synergistic relationship between ML and CFD; it proposes pathways to overcome current limitations, aiming to enhance the precision and efficiency of indoor environment modelling through their integration.

An efficient partitioned framework to couple Arbitrary Lagrangian-Eulerian and meshless vector form intrinsic finite element methods for fluid-structure interaction problems with deformable structures

The Vector Form Intrinsic Finite Element (VFIFE) is an advanced meshless Lagrangian structural solver. This study introduces a partitioned framework to integrate Arbitrary Lagrangian-Eulerian and VFIFE methods into fluid-structure interaction (FSI) problems featured by large structural displacements and deformations. The VFIFE method enables the FSI simulations with deformable structures due to the nature of Lagrangian particles, which is challenging for traditional computational fluid mechanics techniques. In addition, the VFIFE can be effectively equipped with the partitioned coupling schemes because of the versatility of governing equations. In this algorithm system, several matrix problems in finite element methods and mapping and interpolation in the fluid-structure interface can be avoided. The significant potential of this algorithm in tackling complex FSI problems is demonstrated through various benchmarks, encompassing scenarios like Taylor-Green vortex, flow over a rigid cylinder (Re = 40 ∼ 200), forced vibration of a rigid cylinder, vortex-induced vibration (VIV) of an elastically mounted cylinder, and VIV of a rigid cylinder with an attached flexible cantilever beam. Finally, an experiment on flow past a curtain in a water tank is conducted to further evaluate the precision and stability of the current coupling strategy in three dimensions, demonstrating the feasibility of such methods in flexible structures.

An attention-mechanism incorporated deep recurrent optical flow network for particle image velocimetry

Particle image velocimetry (PIV), as a key technique in experimental fluid mechanics, is able to estimate complex velocity field through consecutive input particle images. In this study, an attention-mechanism incorporated deep recurrent network called ARaft-FlowNet has been proposed, on the basis of a previously established Recurrent All-Pairs Field Transforms optical flow model. The attention module is added to improve the network's capability of recognizing tracer particles' motion. Moreover, a parameterized dataset, ParaPIV-Dataset, is generated to explore the influence of particle parameters on deep learning networks, including particle diameter, image particle density, Gaussian noise, and peak intensity. The accuracy and generalizability of the newly proposed model has been evaluated and analyzed comprehensively. The results indicate that ARaft-FlowNet achieves state-of-the-art performance. Compared to previous methods, ARaft-FlowNet shows an accuracy improvement of 62.9%, 10.9%, and 9.4% in cylindrical flow, surface quasi-geostrophic flow, and DNS-turbulence flow. Meanwhile, the proposed model shows the strongest generalization and best capability to deal with complex flow fields with small-scale vortices. Additionally, tests on experimental turbulent jet data reveal that ARaft-FlowNet is able to deal with real PIV images with brightness variations and noise.

Multiphase numerical simulation of exergy loss and thermo-hydraulic behavior with environmental cosiderations of a hybrid nanofluid in a shell-and-tube heat exchanger with twisted tape

In the foreseeable future, heat exchangers will continue to play an important role in environmental management and have numerous applications, their geometry has been the subject of interest for many researchers. The main goal of this research is improving the heat exchangers' thermal performance and investigating the exergy using SIMPLE algorithm, k-w turbulent model and the Eulerian- Eulerian method for multiphase flow. Hence, the operation of Al2O3CuO-water hybrid nanofluid in a 3D shell-and-tube heat exchanger is simulated to enhance the contact surface of hot and cold fluid streams using computation fluid mechanics techniques. Reynolds number is 10,000, 15,000, 20,000, and 25,000 and volume fraction of nanoparticle is 2–6%. The innovations of this research are the use of the use of hybrid nanofluid and turbulator. The hybrid nanoparticles are employed to enhance the thermal conductivity and the turbulator is utilized to increase flow turbulence. The results demonstrated that the hybrid nanofluid, the turbulator, and high amount of Reynolds number can have a remarkable impact on increasing the thermal performance. The obtained results revealed that a 6% increment in hybrid nanoparticles volume fraction and an enhancement in Reynolds number from minimum to maximum lead to a 126% improvement in thermal performance in the presence of the turbulator. Lastly, environmental damage, energy consumption, and emissions are reduced by nanofluids.

Role of surface-tension additive in coating framing effect topography

Materials are often coated with liquid coatings for functional or aesthetic purposes. However, liquid coating deposition may result in surface irregularities in the final film due to substrate topography or physico-chemical or thermo-capillary phenomena. The surface defect of interest in this work is the framing effect, a surface defect at the edges of coated substrates. This study enables an in-depth understanding of capillary phenomena near substrate edges during coating and evaporation. A particle tracking velocimetry (PTV) technique highlights coating flows resulting from hydrostatic pressure and thermo-capillary phenomena such as vapour recoil and surface-tension gradients. This fluid mechanics technique was coupled with topographical measurements to study and quantify the influence of coating flows on framing effect dynamics. Framing effect topography do not rely on coating surface tension but on the presence of surface tension additive at film free surface and of their behaviours toward evaporation conditions.

Optical Flow Methods For Tracer-Based Imaging Techniques In Fluid Mechanics

This paper is aimed at assessing the performance of Optical Flow methods to estimate flow velocities from images designed for 2D Particle Image Velocimetry (PIV). A benchmark is proposed, based on synthetic tracer images associated to rotation- and deformation-dominated flow configurations. The investigated Optical Flow methods are Lucas-Kanade, Horn-Schunck and Farnebäck, combined them with the Liu-Shen method. The true values of the flow field (ground truth) are compared with the results of PIV and Optical Flow methods. Relative and absolute errors are computed for different combinations of flow type, relative velocity, white noise level, particle spot size and particle concentration. The accuracy of Optical Flow methods varies with the flow type, particle sizes and pre-processing steps. Combination of methods improves accuracy. The most accurate method is (dense) Lucas-Kanade/Liu-Shen. The Farnebäck/Liu-Shen combination is highly accurate for 10 or 12 bit images. The range of applicability of Optical Flow methods seems higher than that of correlation-based PIV.

Generalization of deep recurrent optical flow estimation for particle-image velocimetry data

Particle-image velocimetry (PIV) is one of the key techniques in modern experimental fluid mechanics to determine the velocity components of flow fields in a wide range of complex engineering problems. Current PIV processing tools are mainly handcrafted models based on cross-correlations computed across interrogation windows. Although widely used, these existing tools have a number of well-known shortcomings, including limited spatial output resolution and peak-locking biases. Recently, new approaches for PIV processing leveraging a novel neural network architecture for optical flow estimation called recurrent all-pairs field transforms (RAFT) have been developed. These have matched or exceeded the performance of classical, handcrafted models. While the RAFT-PIV method is a promising approach, it is important for the broader fluids community to more completely understand its empirical behavior and performance. To this end, in this study, we thoroughly investigate the performance of RAFT-PIV under varying image and lighting conditions. We consider applications spanning synthetic and experimental data, with a breadth and depth going far beyond currently available empirical results. The results for the wide variation of experiments shed new light on the capabilities of deep learning for PIV processing.

Method for utilizing PIV to investigate high curvature and acceleration boundary layer flows around the compressor blade leading edge

Particle Image Velocimetry (PIV) is a well-developed and contactless technique in experimental fluid mechanics, but the strong velocity gradient and streamline curvature near the wall substantially limits its accuracy improvement. This paper presents a data processing procedure combining conventional PIV and newly developed Mirror Interchange (MI) based Interface-PIV for the measurement of the boundary layer parameter development in the blade leading edge region. The synthetic particle images are used to analyze the measurement errors in the entire procedure. Overall, three types of errors, namely the errors caused by the Window Deformation Iterative Multigrid (WIDIM) algorithm, the discrete data interpolation and integration, and the wall offset uncertainty, comprise the main measurement error. Specifically, the errors due to the discrete data interpolation and integration and the WIDIM algorithm comprise the mean bias, which can be corrected through the error analysis method proposed in the present work. Meanwhile, the errors due to the WIDIM algorithm and the wall offset uncertainty contribute to the measurement uncertainty. Computational fluid dynamics-based synthetic particle flows were generated to verify the newly developed PIV data processing procedure and the corresponding error analysis method. Results showed that the data processing method could improve the accuracy of PIV measurements for boundary layer flows with high curvature and acceleration and even with significant flow separation bubbles. Finally, the data processing method is also applied in a PIV experiment to investigate the boundary layer flows around a compressor blade leading edge, and several credible boundary flow parameters were obtained.

Applying deep reinforcement learning to active flow control in weakly turbulent conditions

Machine learning has recently become a promising technique in fluid mechanics, especially for active flow control (AFC) applications. A recent work [Rabault et al., J. Fluid Mech. 865, 281–302 (2019)] has demonstrated the feasibility and effectiveness of deep reinforcement learning (DRL) in performing AFC over a circular cylinder at Re = 100, i.e., in the laminar flow regime. As a follow-up study, we investigate the same AFC problem at an intermediate Reynolds number, i.e., Re = 1000, where the weak turbulence in the flow poses great challenges to the control. The results show that the DRL agent can still find effective control strategies, but requires much more episodes in the learning. A remarkable drag reduction of around 30% is achieved, which is accompanied by elongation of the recirculation bubble and reduction of turbulent fluctuations in the cylinder wake. Furthermore, we also perform a sensitivity analysis on the learnt control strategies to explore the optimal layout of sensor network. To our best knowledge, this study is the first successful application of DRL to AFC in weakly turbulent conditions. It therefore sets a new milestone in progressing toward AFC in strong turbulent flows.

CFD analysis of compressible flows in a convergent-divergent nozzle

A Nozzle is a mechanical device that uses pressure energy and fluid enthalpy to increase the outflow velocity and control fluid flow direction. To obtain the nozzle duct's shock pattern, the flow inside the nozzle must be supersonic with a Mach number greater than one. Experimentally, the shock pattern is obtained for a nozzle with a Mach number 2 and nozzle pressure ratio (NPR) equivalent to 7 and below. For Mach M = 2, the needed NPR is equal to 7.82 for correct expansion.. When the NPR is greater than 7.82, flow from the nozzles is under-expanded. For NPR less than 7.72 the flow from the nozzle is over-expanded. In this paper, the computational fluid mechanics (CFD) technique was used to simulate the nozzle flow based on the experimental investigation. A two-dimensional transient compressible flow of air through a supersonic nozzle was simulated using ANSYS fluent software. A time-dependent flow using the density-based implicit solver was useds to analyze the simulation results. The results illustrate that the CFD technique simulates the fluid flows and the formation of shock in a duct and gives useful information about fluid dynamics analysis.

A CFD study of particle–bubble collision efficiency in froth flotation

To improve understanding of the interaction between bubbles and particles during the flotation process, the interaction between the various factors that affect the collision efficiency has been analyzed in this paper. Four kinds of mineral particles (quartz, chalcopyrite, copper sulfide and galena) were investigated. Particle–bubble collisions between bubbles with size ranging from 0.6 mm to 2.0 mm and particles with diameter from 31 μm to 150 μm were examined. Representative analytical mathematical models (Generalised Sutherland Equation (GSE) model and Schulze model) are studied in depth, and errors in the equations for the GSE model reported in a review paper are corrected. Simultaneously, a computational fluid mechanics (CFD) technique was used to establish a fluid mechanics model which can directly reflect the relative motion of bubbles and particles during flotation. The results of the CFD model are compared with the existing mathematical models to analyze the advantages and disadvantages of the existing models as a description of collisions in the flotation process. The GSE model accounts for the centrifugal inertial effect (neglected in the Schulze model), but comparison with the CFD results suggests that it greatly over-estimates this effect. On the other hand, the Schulze model appears to overestimate the collision efficiency at the larger particle size range, giving values greater than unity in some cases. It would be expected that the CFD model would be more exact than either semi-theoretical model, given that it involves fewer assumptions. Therefore, it is recommended that the Schulze model (limited to a value less than unity) should be used as a sub-model for bubble–particle collision rate in macro-scale CFD models of flotation cells rather than the GSE model.

Combined particle image velocimetry/digital image correlation for load estimation

Particle image velocimetry (PIV) and digital image correlation (DIC) are widely used experimental techniques in fluid mechanics and structural dynamics, respectively. PIV is capable of resolving detailed velocity fields around structures, from which the hydrodynamic loading can be reconstructed. However, PIV is ill-suited to capturing the structural response, which is critical for a complete understanding of the bidirectional coupling between the fluid flow and the structural dynamics. On the other hand, DIC can accurately quantify local deformation of the structure, but does not afford the precise identification of the hydrodynamic loading due to the ill-posed nature of the inverse load estimation process. Here, we explore the feasibility of a combined PIV/DIC technique for the investigation of fluid-structure interactions. Specifically, we study fluid-structure interactions associated with a flexible cantilever plate immersed in a steady unidirectional flow. We demonstrate that the combination of pressure estimation from PIV and deformation measurement through DIC enables the precise identification of the hydrodynamic loading and structural response. The proposed methodology may help in improving our understanding of a number of fluid-structure interaction problems, such as biomimetic propulsion, aeroelasticity of airfoils, and hydrodynamic impact on marine structures.

Application of the ideas and techniques of classical fluid mechanics to some problems in physical oceanography.

This review makes a case for describing many of the flows observed in our oceans, simply based on the Euler equation, with (piecewise) constant density and with suitable boundary conditions. The analyses start from the Euler and mass conservation equations, expressed in a rotating, spherical coordinate system (but the f-plane and β-plane approximations are also mentioned); five examples are discussed. For three of them, a suitable non-dimensionalization is introduced, and a single small parameter is identified in each case. These three examples lead straightforwardly and directly to new results for: waves on the Pacific Equatorial Undercurrent (EUC) with a thermocline (in the f-plane); a nonlinear, three-dimensional model for EUC-type flows (in the β-plane); and a detailed model for large gyres. The other two examples are exact solutions of the complete system: a flow which corresponds to the underlying structure of the Pacific EUC; and a flow based on the necessary requirement to use a non-conservative body force, which produces the type of flow observed in the Antarctic Circumpolar Current. (All these examples have been discussed in detail in the references cited.) This review concludes with a few comments on how these solutions can be extended and expanded.This article is part of the theme issue 'Nonlinear water waves'.

Error propagation in the procedure of pressure reconstruction based on PIV data

Pressure reconstruction based on particle image velocimetry (PIV) has become a popular technique in experimental fluid mechanics. Errors in the raw velocity field significantly affect the accuracy of pressure gradient field and further reduce the quality of reconstructed pressure. Thus, error propagation deserves a serious concern. The format and magnitude of errors are investigated using a probability density function (PDF) based method. Theoretical derivation and numerical validation are operated in both Eulerian and Lagrangian descriptions. The influence of spatial and temporal resolutions on error propagation is discussed. A criterion of parameter selection for error suppression in the Lagrangian method is proposed. The results show that error propagation in the Lagrangian method has definite format and magnitude which makes this method more suitable for error control through specific treatment. Time interval during pressure reconstruction should be carefully determined since a large uncertainty appears when the time interval is employed improperly.

An irrotation correction on pressure gradient and orthogonal-path integration for PIV-based pressure reconstruction

Particle image velocimetry (PIV)-based pressure reconstruction has become a popular technique in experimental fluid mechanics. Noise or errors in raw velocity field would significantly affect the quality of pressure reconstruction in PIV measurement. To reduce experimental errors in pressure gradient and improve the precision of reconstructed pressure field, a minimal 2-norm criteria-based new technique called irrotation correction (IC) with orthogonal decomposition is developed. The pressure reconstruction is therefore composed of three steps: calculation of pressure gradient from time-resolved velocity fields of PIV, an irrotation correction on the pressure gradient field, and finally a simple orthogonal-path integration (OPI) for pressure. Systematic assessments of IC algorithm are performed on synthetic solid-body rotation flow, direct numerical simulations of a channel flow and an isotropic turbulent flow. The results show that IC is a robust algorithm which can significantly improve the accuracy of pressure reconstruction primarily in the low wave number domain. After irrotation correction, noisy pressure gradient field ideally becomes an irrotational field on which the pressure integration is independent of integrating paths. Therefore, an OPI algorithm is proposed to perform the pressure integration in an efficient way with very few integration paths. This makes the new technique to be a doable method on three-dimensional pressure reconstruction with acceptable computational cost.

CFD 2D Description of Local Flow of Polymer Workpiece through a modified U-Shaped Die During Equal Channel Multiple Angular Extrusion

The present article is focused on a 2D computational fluid mechanics study of local viscous flow dynamics and the formation character of rotary modes of deformation during Equal Channel Multiple Angular Extrusion (ECMAE) of a polymer workpiece fluid model through a U-shaped die with parallel slants in channel intersection zones. The present local flow problem was experimentally analyzed using physical simulation methods and theoretically studied with numerical fluid mechanics techniques. The computational approach has been grounded on the numerical finite difference solution of the boundary value problem for the Navier-Stokes equations in the curl transfer form for the local viscous flow of incompressible Newtonian fluid through a U-shaped rectangular die with parallel slants. The derived research results allow us to draw a conclusion that the implementation of a geometric design of parallel slants within a 2-turn U-shaped die results in localization of the maximum tangential stresses within the workpiece volume to the vicinity of these parallel slants during ECMAE.

Numerical simulation of secondary vortex chamber effect on the cooling capacity enhancement of vortex tube

A vortex tube with additional chamber is investigated by computational fluid mechanics techniques to realize the effects of additional chamber in Ranque–Hilsch vortex tube and to understand optimal length for placing the second chamber in order to have maximum cooling effect. Results show that by increasing the distance between two chambers, both minimum cold and maximum hot temperatures increase and maximum cooling effect occurs at Z/L = 0.047 (dimensionless distance).

Pressure probe with five embedded flush-mounted sensors: unsteady pressure and velocity measurements in hydraulic turbine model

The unsteady five-hole Pitot tube, with embedded flush-mounted sensors, provides one of the most advanced experimental techniques in fluid mechanics stemming from Pitot tube. This instrument enables the measurement of both unsteady pressures and instantaneous three-dimensional velocity vectors. The present paper focuses on the methodology and use of this type of probe in hydraulic turbines. Different fluctuating phenomena can be monitored, providing reliable estimates regarding amplitude and frequency. The probe is particularly advantageous for applications when the flow exhibits large angular fluctuation. In such cases, even the mean values must be validated using unsteady measurements. The calibration method and the validation of the unsteady measurements are presented through three different hydraulic turbine models.

Patient-specific computer modeling of blood flow in cerebral arteries with aneurysm and stent

We present the special arterial fluid mechanics techniques we have developed for patient-specific computer modeling of blood flow in cerebral arteries with aneurysm and stent. These techniques are used in conjunction with the core computational technique, which is the space---time version of the variational multiscale (VMS) method and is called "DST/SST-VMST." The special techniques include using NURBS for the spatial representation of the surface over which the stent mesh is built, mesh generation techniques for both the finite- and zero-thickness representations of the stent, techniques for generating refined layers of mesh near the arterial and stent surfaces, and models for representing double stent. We compute the unsteady flow patterns in the aneurysm and investigate how those patterns are influenced by the presence of single and double stents. We also compare the flow patterns obtained with the finite- and zero-thickness representations of the stent.