Showing papers on "Reynolds number published in 2019"

Prediction of aerodynamic flow fields using convolutional neural networks

Abstract: An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen flow conditions and geometries given the pixelated shape of the object. In particular, we consider Reynolds Averaged Navier–Stokes (RANS) flow solutions over airfoil shapes as training data. The CNN can automatically detect essential features with minimal human supervision and is shown to effectively estimate the velocity and pressure field orders of magnitude faster than the RANS solver, making it possible to study the impact of the airfoil shape and operating conditions on the aerodynamic forces and the flow field in near-real time. The use of specific convolution operations, parameter sharing, and gradient sharpening are shown to enhance the predictive capabilities of the CNN. We explore the network architecture and its effectiveness in predicting the flow field for different airfoil shapes, angles of attack, and Reynolds numbers.

Machine learning methods for turbulence modeling in subsonic flows around airfoils

Abstract: In recent years, the data-driven turbulence model has attracted widespread concern in fluid mechanics. The existing approaches modify or supplement the original turbulence model by machine learning based on the experimental/numerical data, in order to augment the capability of the present turbulence models. Different from the previous researches, this paper directly reconstructs a mapping function between the turbulent eddy viscosity and the mean flow variables by neural networks and completely replaces the original partial differential equation model. On the other hand, compared with the machine learning models for the low Reynolds (Re) number flows based on direct numerical simulation data, high Reynolds number flows around airfoils present the apparent scaling effects and strong anisotropy, which induce large challenges in accuracy and generalization capability for the machine learning algorithm. We mainly concentrate on the high Reynolds number turbulent flows around the airfoils and take the results calculated by the computational fluid dynamics solver with the Spallart-Allmaras (SA) model as training data to construct a high-dimensional data-driven network model based on machine learning. The radial basis function neural network and the auxiliary optimization methods are adopted, and the individual models are built separately for the flow fields of the near-wall region, wake region, and far-field region. The training data in this paper is extracted from only three subsonic flow fields of NACA0012 airfoil. The data-driven turbulence model can be applied to various airfoils and flow states, and the predicted eddy viscosity, lift/drag coefficients, and skin friction distributions are all in good agreement with the results of the original SA model. This research demonstrates the promising prospect of machine learning methods in future studies about turbulence modeling.

High Reynolds number Taylor-Couette turbulence

Abstract: Taylor-Couette flow -- the flow between two coaxial co- or counter-rotating cylinders -- is one of the paradigmatic systems in physics of fluids. The (dimensionless) control parameters are the Reynolds numbers of the inner and outer cylinder, the ratio of the cylinder radii, and the aspect ratio. The response of the system is the torque required to retain constant angular velocities, which can be connected to the angular velocity transport through the gap. While the low Reynolds number regime has been very well explored in the '80s and '90s of the last century, in the fully turbulent regime major research activity only developed in the last decade. In this paper we review this recent progress in our understanding of fully developed Taylor-Couette turbulence, from the experimental, numerical, and theoretical point of view. We will focus on the parameter dependence of the global torque and on the local flow organisation, including velocity profiles and boundary layers. Next, we will discuss transitions between different (turbulent) flow states. We will also elaborate on the relevance of this system for astrophysical disks (Keplerian flows). The review ends with a list of challenges for future research on turbulent Taylor-Couette flow. The published version in ARFM: Annu. Rev. Fluid Mech. 48:53-80 (2016).

Magneto-hydrodynamic flow and heat transfer of a hybrid nanofluid in a rotating system among two surfaces in the presence of thermal radiation and Joule heating

Abstract: In this paper, the researchers explore heat transfer and magneto-hydrodynamic flow of hybrid nanofluid in a rotating system among two surfaces. The upper and lower plates of the system are assumed penetrable and stretchable, respectively. The thermal radiation and Joule heating impacts are considered. A similarity technic is applied to alter governing energy and momentum equations into non-linear ordinary differential ones that contain the convenient boundary conditions and used the Duan-Rach Approach (DRA) to solve them. Influences of assorted parameters including rotation parameter, suction/blowing parameter, radiation parameter, Reynolds number, hybrid nanofluid volume fraction, and magnetic parameter on temperature and velocity profiles are examined. Also, a correlation for the Nusselt number has been developed in terms of the acting parameters of the present study. The outcomes indicate that Nusselt number acts as an ascending function of injection and radiation parameters, as well as volume fraction of nanofluid.

Predictive large-eddy-simulation wall modeling via physics-informed neural networks

Abstract: A study shows that when trained using channel flow data at just one Reynolds number and informed with known flow physics, the neural network works robustly as a wall model in large-eddy simulation (LES) of channel flow at any Reynolds number. It also outperforms the equilibrium wall model in LES of a 3D boundary layer flow.

Fast flow field prediction over airfoils using deep learning approach

Abstract: In this paper, a data driven approach is presented for the prediction of incompressible laminar steady flow field over airfoils based on the combination of deep Convolutional Neural Network (CNN) and deep Multilayer Perceptron (MLP). The flow field over an airfoil depends on the airfoil geometry, Reynolds number, and angle of attack. In conventional approaches, Navier-Stokes (NS) equations are solved on a computational mesh with corresponding boundary conditions to obtain the flow solutions, which is a time consuming task. In the present approach, the flow field over an airfoil is approximated as a function of airfoil geometry, Reynolds number, and angle of attack using deep neural networks without solving the NS equations. The present approach consists of two steps. First, CNN is employed to extract the geometrical parameters from airfoil shapes. Then, the extracted geometrical parameters along with Reynolds number and angle of attack are fed as input to the MLP network to obtain an approximate model to predict the flow field. The required database for the network training is generated using the OpenFOAM solver by solving NS equations. Once the training is done, the flow field around an airfoil can be obtained in seconds. From the prediction results, it is evident that the approach is efficient and accurate.

Turbulent mixing: A perspective

Abstract: Mixing of initially distinct substances plays an important role in our daily lives as well as in ecological and technological worlds. From the continuum point of view, which we adopt here, mixing is complete when the substances come together across smallest flow scales determined in part by molecular mechanisms, but important stages of the process occur via the advection of substances by an underlying flow. We know how smooth flows enable mixing but less well the manner in which a turbulent flow influences it; but the latter is the more common occurrence on Earth and in the universe. We focus here on turbulent mixing, with more attention paid to the postmixing state than to the transient process of initiation. In particular, we examine turbulent mixing when the substance is a scalar (i.e., characterized only by the scalar property of its concentration), and the mixing process does not influence the flow itself (i.e., the scalar is "passive"). This is the simplest paradigm of turbulent mixing. Within this paradigm, we discuss how a turbulently mixed state depends on the flow Reynolds number and the Schmidt number of the scalar (the ratio of fluid viscosity to the scalar diffusivity), point out some fundamental aspects of turbulent mixing that render it difficult to be addressed quantitatively, and summarize a set of ideas that help us appreciate its physics in diverse circumstances. We consider the so-called universal and anomalous features and summarize a few model studies that help us understand them both.

A smoothed particle hydrodynamics approach for numerical simulation of nano-fluid flows

Abstract: Nano-fluidic flow and heat transfer around a horizontal cylinder at Reynolds numbers up to 250 are investigated by using weakly compressible smoothed particle hydrodynamics (WCSPH). To be able to simulate enhanced nanoparticle heat transfer, this manuscript describes for the first time a development that allows conductive and convective heat transfer to be modelled accurately for the Eckert problem using WCSPH. The simulations have been conducted for Pr = 0.01–40 with nanoparticle volumetric concentrations ranging from 0 to 4%. The velocity fields and the Nusselt profiles from the present simulations are in a good agreement with the experimental measurements. The results show that WCSPH is appropriate method for such numerical modelling. Additionally, the results of heat transfer characteristics of nano-fluid flow over a cylinder marked improvements comparing with the base fluids. This improvement is more evident in flows with higher Reynolds numbers and higher particle volume concentration.

Entropy generation analysis of different nanofluid flows in the space between two concentric horizontal pipes in the presence of magnetic field: Single-phase and two-phase approaches

Abstract: In this paper, entropy generation analysis of different nanofluid flows in the space between two concentric horizontal pipes in the presence of magnetic field by using of single-phase and two-phase approaches was carried out. Single-phase model and two-phase model (mixture) are utilized to model the flow and heat transfer for Newtonian nanofluids in the space between two concentric horizontal tubes subjected to the magnetic field. The Reynolds and Hartman numbers ranges are 500 ≤ R e ≤ 1500 and 0 ≤ H a ≤ 20, respectively. In this study, heat transfer of various nanofluids (Al2O3, TiO2, ZnO and SiO2) and their entropy generation have been investigated. The effect of diameter of particles (water-Al2O3 nanofluid) on heat transfer and entropy generation has also been studied. Average Nusselt number in terms of Hartman number and Reynolds number for different nanofluids for single-phase and two-phase models in various volume fractions, entropy generation due to friction, magnet and heat transfer in terms of radial direction for different Hartman numbers, Reynolds number and different nanofluids with different diameter of particles were obtained. We found that in all states, the Nusselt number is higher in two-phase model than in single-phase model. The maximum pressure difference for single- and two-phase models occurs at maximum volume fractions and Hartman number. Also, as the diameter of the nanoparticle increases, the result will be an increase in the temperature of the walls, leading to an increase in entropy generation. Also, as the Hartman number increases, the amount of entropy generation increases.

Reynolds-averaged Navier-Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned

Abstract: Reynolds-averaged Navier–Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy-viscosity models are intrinsically unable to handle flows with non-equilibrium turbulence (e.g. flows with massive separation). Reynolds stress models, on the other hand, are plagued by their lack of robustness. Recent studies in plane channel flows found that even substituting Reynolds stresses with errors below 0.5 % from direct numerical simulation databases into RANS equations leads to velocities with large errors (up to 35 %). While such an observation may have only marginal relevance to traditional Reynolds stress models, it is disturbing for the recently emerging data-driven models that treat the Reynolds stress as an explicit source term in the RANS equations, as it suggests that the RANS equations with such models can be ill-conditioned. So far, a rigorous analysis of the condition of such models is still lacking. As such, in this work we propose a metric based on local condition number function for a priori evaluation of the conditioning of the RANS equations. We further show that the ill-conditioning cannot be explained by the global matrix condition number of the discretized RANS equations. Comprehensive numerical tests are performed on turbulent channel flows at various Reynolds numbers and additionally on two complex flows, i.e. flow over periodic hills, and flow in a square duct. Results suggest that the proposed metric can adequately explain observations in previous studies, i.e. deteriorated model conditioning with increasing Reynolds number and better conditioning of the implicit treatment of the Reynolds stress compared to the explicit treatment. This metric can play critical roles in the future development of data-driven turbulence models by enforcing the conditioning as a requirement on these models.

Investigating the effect of nanoparticles diameter on turbulent flow and heat transfer properties of non-Newtonian carboxymethyl cellulose/CuO fluid in a microtube

Abstract: Although many studies have been conducted on the nanofluid flow in microtubes, this paper, for the first time, aims to investigate the effects of nanoparticle diameter and concentration on the velocity and temperature fields of turbulent non-Newtonian Carboxymethylcellulose (CMC)/copper oxide (CuO) nanofluid in a three-dimensional microtube. Modeling has been done using low- and high-Reynolds turbulent models. CMC/CuO was modeled using power law non-Newtonian model. The authors obtained interesting results, which can be helpful for engineers and researchers that work on cooling of electronic devices such as LED, VLSI circuits and MEMS, as well as similar devices.,Present numerical simulation was performed with finite volume method. For obtaining higher accuracy in the numerical solving procedure, second-order upwind discretization and SIMPLEC algorithm were used. For all Reynolds numbers and volume fractions, a maximum residual of 10−6 is considered for saving computer memory usage and the time for the numerical solving procedure.,In constant Reynolds number and by decreasing the diameter of nanoparticles, the convection heat transfer coefficient increases. In Reynolds numbers of 2,500, 4,500 and 6,000, using nanoparticles with the diameter of 25 nm compared with 50 nm causes 0.34 per cent enhancement of convection heat transfer coefficient and Nusselt number. Also, in Reynolds number of 2,500, by increasing the concentration of nanoparticles with the diameter of 25 nm from 0.5 to 1 per cent, the average Nusselt number increases by almost 0.1 per cent. Similarly, In Reynolds numbers of 4,500 and 6,000, the average Nusselt number increases by 1.8 per cent.,The numerical simulation was carried out for three nanoparticle diameters of 25, 50 and 100 nm with three Reynolds numbers of 2,500, 4,500 and 6,000. Constant heat flux is on the channel, and the inlet fluid becomes heated and exists from it.,The authors obtained interesting results, which can be helpful for engineers and researchers that work on cooling of electronic devices such as LED, VLSI circuits and MEMS, as well as similar devices.,This manuscript is an original work, has not been published and is not under consideration for publication elsewhere. About the competing interests, the authors declare that they have no competing interests.

The effect of using water/CuO nanofluid and L-shaped porous ribs on the performance evaluation criterion of microchannels

Abstract: The main purpose of this study is numerically investigating the flow and heat transfer of nanofluid flow inside a microchannel with L-shaped porous ribs as well as studying the effect of porous media properties on the performance evaluation criterion (PEC) of the fluid. In the present paper, in addition to the pure water fluid, the effect of using water/CuO nanofluid on the PEC of microchannel was investigated. The flow was simulated in four Reynolds numbers and two different volume fractions of nanoparticles in laminar flow regime. The investigated parameters are the thermal conductivity and the porosity rate of porous medium. The results indicate that with the existence of porous ribs, the nanofluid does not have a significant effect on heat transfer increase. By using porous ribs in flow with Reynolds number of 1200, the heat transfer rate increases up to 42% and in flow with Reynolds number of 100, this rate increases by 25%.

Spectral analysis of the budget equation in turbulent channel flows at high Reynolds number

Abstract: The transport equations for the variances of the velocity components are investigated using data from direct numerical simulations of incompressible channel flows at friction Reynolds number ( ) up to . Each term in the transport equation has been spectrally decomposed to expose the contribution of turbulence at different length scales to the processes governing the flow of energy in the wall-normal direction, in scale and among components. The outer-layer turbulence is dominated by very large-scale streamwise elongated modes, which are consistent with the very large-scale motions (VLSM) that have been observed by many others. The presence of these VLSMs drives many of the characteristics of the turbulent energy flows. Away from the wall, production occurs primarily in these large-scale streamwise-elongated modes in the streamwise velocity, but dissipation occurs nearly isotropically in both velocity components and scale. For this to happen, the energy is transferred from the streamwise-elongated modes to modes with a range of orientations through nonlinear interactions, and then transferred to other velocity components. This allows energy to be transferred more-or-less isotropically from these large scales to the small scales at which dissipation occurs. The VLSMs also transfer energy to the wall region, resulting in a modulation of the autonomous near-wall dynamics and the observed Reynolds number dependence of the near-wall velocity variances. The near-wall energy flows are more complex, but are consistent with the well-known autonomous near-wall dynamics that gives rise to streaks and streamwise vortices. Through the overlap region between outer- and inner-layer turbulence, there is a self-similar structure to the energy flows. The VLSM production occurs at spanwise scales that grow with . There is transport of energy away from the wall over a range of scales that grows with . Moreover, there is transfer of energy to small dissipative scales which grows like , as expected from Kolmogorov scaling. Finally, the small-scale near-wall processes characterised by wavelengths less than 1000 wall units are largely Reynolds number independent, while the larger-scale outer-layer processes are strongly Reynolds number dependent. The interaction between them appears to be relatively simple.

Dynamic slip wall model for large-eddy simulation.

Abstract: Wall modelling in large-eddy simulation (LES) is necessary to overcome the prohibitive near-wall resolution requirements in high-Reynolds-number turbulent flows. Most existing wall models rely on assumptions about the state of the boundary layer and require a priori prescription of tunable coefficients. They also impose the predicted wall stress by replacing the no-slip boundary condition at the wall with a Neumann boundary condition in the wall-parallel directions while maintaining the no-transpiration condition in the wall-normal direction. In the present study, we first motivate and analyse the Robin (slip) boundary condition with transpiration (non-zero wall-normal velocity) in the context of wall-modelled LES. The effect of the slip boundary condition on the one-point statistics of the flow is investigated in LES of turbulent channel flow and a flat-plate turbulent boundary layer. It is shown that the slip condition provides a framework to compensate for the deficit or excess of mean momentum at the wall. Moreover, the resulting non-zero stress at the wall alleviates the well-known problem of the wall-stress under-estimation by current subgrid-scale (SGS) models (Jimenez & Moser, AIAA J., vol. 38 (4), 2000, pp. 605-612). Second, we discuss the requirements for the slip condition to be used in conjunction with wall models and derive the equation that connects the slip boundary condition with the stress at the wall. Finally, a dynamic procedure for the slip coefficients is formulated, providing a dynamic slip wall model free of a priori specified coefficients. The performance of the proposed dynamic wall model is tested in a series of LES of turbulent channel flow at varying Reynolds numbers, non-equilibrium three-dimensional transient channel flow and a zero-pressure-gradient flat-plate turbulent boundary layer. The results show that the dynamic wall model is able to accurately predict one-point turbulence statistics for various flow configurations, Reynolds numbers and grid resolutions.

Heat and fluid flow analysis of metal foam embedded in a double-layered sinusoidal heat sink under local thermal non-equilibrium condition using nanofluid

Abstract: The present study aims to enhance the hydrothermal performance of a porous sinusoidal double-layered heat sink using nanofluid. The optimum thickness of metal foam (nickel) for different Reynolds numbers ranging from 10 to 100 for the laminar regime and Darcy numbers ranging from 10−4 to 10−2 is obtained. At the optimum porous thicknesses, nanofluid (silver–water) with three volume fractions of nanoparticles equal to 2, 3, and 4% is employed to enhance the heat sink thermal performance. Darcy–Brinkman–Forchheimer model and the local thermal non-equilibrium model or two equations method are employed to model the momentum equation and energy equations in the porous region, respectively. It was found that in the cases of Darcy numbers 10−4, 10−3, and 10−2 the dimensionless optimum porous thicknesses are 0.8, 0.8, and 0.2, respectively. It was also obtained that the maximum PEC number is 2.12 and it corresponds to the case with Darcy number 10−2, Reynolds number 40, and volume fraction of nanoparticles 0.04. The validity of local thermal equilibrium (LTE) assumption was investigated, and it was found that increasing the Darcy number which results in an enhancement in porous particle diameter leads to some errors in results, under LTE condition.

Experimental study of inertial particles clustering and settling in homogeneous turbulence

Abstract: We study experimentally the spatial distribution, settling and interaction of sub-Kolmogorov inertial particles with homogeneous turbulence. Utilizing a zero-mean-flow air turbulence chamber, we drop size-selected solid particles and study their dynamics with particle imaging and tracking velocimetry at multiple resolutions. The carrier flow is simultaneously measured by particle image velocimetry of suspended tracers, allowing the characterization of the interplay between both the dispersed and continuous phases. The turbulence Reynolds number based on the Taylor microscale ranges from particles can be several times larger than the still-air terminal velocity, and the clusters can fall even faster. This is caused by downward fluid fluctuations preferentially sweeping the particles, and we propose that this mechanism is influenced by both large and small scales of the turbulence. The particle–fluid slip velocities show large variance, and both the instantaneous particle Reynolds number and drag coefficient can greatly differ from their nominal values. Finally, for sufficient loadings, the particles generally augment the small-scale fluid velocity fluctuations, which however may account for a limited fraction of the turbulent kinetic energy.

Applications of nanofluids in photovoltaic thermal systems: A review of recent advances

Abstract: The aim of this study is to present a critical review of the impact of nanofluids on the performance enhancement of PV/T systems. The review has analyzed the effects of nanoparticle type, size, volume fraction and concentration ratio on the performance of PV/T systems. Furthermore, the type of base-fluid, flow channels, and flow types have also been studied comprehensively in relation to nanofluids characteristics and properties. Results have shown that the inclusion of nanofluid enhances the overall efficiency of the PV/T systems. It has been concluded that the organic fluids are better base fluids than water, and nanofluids with better thermal conductivity enhance the maximum efficiency once optimum size, volume fraction and correct concentration ratio of nanofluid are selected. Moreover, straight microchannel and the addition of Fe3O4, SiC and TiO2 nanofluids with low concentration ratio provides better efficiency and flexibility. The motive beyond that is the micro-channels turbulent flow occurs at low Reynolds number. Accordingly, maximum efficiency can be obtained at higher velocity laminar flows. Increasing the velocity to higher ranges of turbulent flow does not allow proper time for heat transfer and can cause clustering of nanoparticles. The observations of this review are proposed to PV/T systems and it is helpful for the thermal system design practitioners towards achieving high efficiency in any thermal system.

Numerical study of mixing in wavy micromixers: comparison between raccoon and serpentine mixer

Abstract: We numerically analyze the mixing and pressure drop characteristics for flow through wavy micromixer of two geometrical configurations, namely raccoon and serpentine for different values of amplitude of the waviness of the mixer (α), wavelength of the waviness (λ), Reynolds number(Re) and Schmidt number(Sc). Three different flow regimes are identified depending on the parameters influencing the mixing index. The mixing index for both the raccoon and serpentine mixer is very close to unity in the first regime (0.1

Efficiency assessment of using graphene nanoplatelets-silver/water nanofluids in microchannel heat sinks with different cross-sections for electronics cooling

Abstract: This study aims to model the nanofluid flow in microchannel heat sinks having the same length and hydraulic diameter but different cross-sections (circular, trapezoidal and square).,The nanofluid is graphene nanoplatelets-silver/water, and the heat transfer in laminar flow was investigated. The range of coolant Reynolds number in this investigation was 200 ≤ Re ≤ 1000, and the concentrations of nano-sheets were from 0 to 0.1 vol. %.,Results show that higher temperature leads to smaller Nusselt number, pressure drop and pumping power, and increasing solid nano-sheet volume fraction results in an expected increase in heat transfer. However, the influence of temperature on the friction factor is insignificant. In addition, by increasing the Reynolds number, the values of pressure drop, pumping power and Nusselt number augments, but friction factor diminishes.,Data extracted from a recent experimental work were used to obtain thermo-physical properties of nanofluids.,The effects of temperature, microchannel cross-section shape, the volume concentration of nanoparticles and Reynolds number on thermal and hydraulics behavior of the nanofluid were investigated. Results are presented in terms of velocity, Nusselt number, pressure drop, friction loss and pumping power in various conditions. Validation of the model against previous papers showed satisfactory agreement.