Showing papers on "Reynolds number published in 2020"

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

Abstract: This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely, the proximal policy optimization (PPO) method is used to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain. The learning environment supports four flow configurations with Reynolds numbers 100, 200, 300, and 400, respectively. A new smoothing interpolation function is proposed to help the PPO algorithm learn to set continuous actions, which is of great importance to effectively suppress problematic jumps in lift and allow a better convergence for the training process. It is shown that the DRL controller is able to significantly reduce the lift and drag fluctuations and actively reduce the drag by ∼5.7%, 21.6%, 32.7%, and 38.7%, at Re = 100, 200, 300, and 400, respectively. More importantly, it can also effectively reduce drag for any previously unseen value of the Reynolds number between 60 and 400. This highlights the generalization ability of deep neural networks and is an important milestone toward the development of practical applications of DRL to active flow control.

Flow and thermal analysis on Darcy-Forchheimer flow of copper-water nanofluid due to a rotating disk: A static and dynamic approach

Abstract: Background Characterised with augmented heat transport and thermal efficiency, nanofluids are implementable in diversified applications include pharmaceutical industries, hybrid-powered machines, cooling of different appliances, refrigerator, microelectronic, heat exchanger etc. Taking such advantages into mind, physical aspects of entropy optimization and non-linear thermal radiation in Darchy–Forchheimer flow of copper–water nanofluid due to a rotating disk are examined. A new thermal conductivity model of nanofluids involving static and dynamic approach is considered. This model signifies hydrodynamic interaction among the Brownian motion induced fluid particles. The Cattaneo–Christov heat flux theory is taken into account. The second law of thermodynamics is the instrumental for the determination of total entropy generation rate. Methods The system of nonlinear PDEs is converted into system of nonlinear ODEs through favorable transformations. Shooting technique has been applied prospectively to accomplish the desired numerical solution of the transformed equations. Results The behavior of velocity (axial, transverse and tangential) and thermal fields influenced by varied physical parameters is impressed through graphs and numerical tables. Velocity field peters out due to rising porosity parameter as well as volume fraction while thermal field upgrades for higher Biot number and radiation parameter. Significant heat transfer rate is obtained for smaller estimation of radiation parameter. Entropy generation rate and Bejan number exhibit similar trend for radiation parameter and opposite fashion for Reynolds number. Conclusions The diminishing velocity distribution for larger access of porous matrix while elevated temperature distribution for higher temperature parameter (due to nonlinear thermal radiation). Entropy minimization is accomplished for grater estimation of Brinkman and Reynolds numbers.

Effects of magnetic field on micro cross jet injection of dispersed nanoparticles in a microchannel

Abstract: Purpose Water/Al 2 O 3 nanofluid with volume fractions of 0, 0.3 and 0.06 was investigated inside a rectangular microchannel. Jet injection of nanofluid was used to enhance the heat transfer under a homogeneous magnetic field with the strengths of Ha = 0, 20 and 40. Both slip velocity and no-slip boundary conditions were used. Design/methodology/approach The laminar flow was studied using Reynolds numbers of 1, 10 and 50. The results showed that in creep motion state, the constricted cross section caused by fluid jet is not observable and the rise of axial velocity level is only because of the presence of additional size of the microchannel. By increasing the strength of the magnetic field and because of the rise of the Lorentz force, the motion of fluid layers on each other becomes limited. Findings Because of the limitation of sudden changes of fluid in jet injection areas, the magnetic force compresses the fluid to the bottom wall, and this behavior limits the vertical velocity gradients. In the absence of a magnetic field and under the influence of the velocity boundary layer, the fluid motion has more variations. In creeping velocities of fluid, the presence or absence of the magnetic field does not have an essential effect on Nusselt number enhancement. Originality/value In lower velocities of fluid, the effect of the jet is not significant, and the thermal boundary layer affects the entire temperature field. In this case, for Hartmann numbers of 40 and 0, changing the Nusselt number on the heated wall is similar.

Prediction of turbulent heat transfer using convolutional neural networks

Abstract: With the recent rapid development of artificial intelligence (AI) and wide applications in many areas, some fundamental questions in turbulence research can be addressed, such as: ‘Can turbulence be learned by AI? If so, how and why?’ In order to provide answers to these questions, we applied deep learning to the prediction of turbulent heat transfer based only on wall information using data obtained from direct numerical simulations (DNS) of turbulent channel flow. Through this attempt, we investigated whether deep learning could help to improve our understanding of the physics of turbulent heat transfer. Under the assumption that the wall-normal local heat flux can be explicitly expressed through a multilayer nonlinear network in terms of the nearby wall-shear stresses and wall pressure fluctuations, we applied convolutional neural networks (CNNs) to predict the local heat flux. After optimizing the network hyperparameters using a grid searching method, we found that the network can predict the heat flux very accurately with a correlation coefficient of 0.980 between the DNS and the prediction by CNN for the trained Reynolds number, , and shows similar accuracy at a Reynolds number three times higher than the trained number. This result indicates that relationships between the local heat flux and the nearby inputs are quite insensitive to the Reynolds number within the tested range. In addition, observing the gradient maps of the trained network, we identified the part of the input data that is essential for the local heat flux prediction and the spatial relationship between the local heat flux and the nearby input fields. In addition to obtaining an understanding of the underlying physics, we investigated whether our model could be utilized for turbulence modelling.

Numerical investigation of turbulent flow and heat transfer of nanofluid inside a wavy microchannel with different wavelengths

Abstract: In the present study, turbulent flow and heat transfer inside a three-dimensional wavy microchannel with different wavelengths have been numerically simulated. The main purpose of this study is to investigate the effects of changing the wavelength of the sinusoidal microchannel and CuO nanoparticle concentration on flow and heat transfer properties. For this reason, flow is simulated at Reynolds numbers of 3000, 4500, 6000, and 7500 with volume fractions of 0, 1.5, and 3% in three different geometries and the effects of each parameter have been investigated. Validation of the results showed there is an excellent agreement between the presented results with the previous studies. The average Nusselt number, pressure loss ratio, performance evaluation criterion, and local Nusselt number have been presented. Moreover, the distribution of the static temperature contour has been presented. In the flow with lower Reynolds numbers, the Nusselt number is not changed significantly; however, in flow with Reynolds number of 7500, the Nusselt number is increased. The performance evaluation criterion has the highest value in nanofluid flow with the volume fraction of 3%, indicating the effects of heat transfer with pressure drop caused by nanoparticles, and from engineering and economic perspectives, using nanoparticles in the wavy microchannel is recommended.

Effects of magnetic Reynolds number on swimming of gyrotactic microorganisms between rotating circular plates filled with nanofluids

Abstract: The three-dimensional (3D) nanofluid flow among the rotating circular plates filled with nanoparticles and gyrotactic microorganisms is studied. A generalized form of the magnetic Reynolds number is used for the mathematical modeling of the ferro-nanofluid flow. The torque effects on the lower and upper plates are calculated. A differential transform scheme with the Pade approximation is used to solve the coupled highly nonlinear ordinary differential equations. The results show that the squeeze Reynolds number significantly suppresses the temperature, microorganism, and nanoparticle concentration distribution, and agree well with those obtained by the numerical method.

Enhancement of heat transfer in a convergent/divergent channel by using carbon nanotubes in the presence of a Darcy–Forchheimer medium

Abstract: This article explores the influence of thermal radiation on the flow and heat transfer of single-walled carbon nanotubes over both a convergent and divergent channel. Flow is induced due to a Darcy–Forchheimer medium. Further, the heat transfer mechanism is analyzed in the presence of a thermal radiation process. Guided by some appropriate similarity transformations, the fundamental PDEs are converted into a self-similar system of coupled non-linear ODEs. The findings are obtained with the help of the Runge–Kutta-45-based shooting method. The roles of the Reynolds number, porosity parameter, inertia coefficient parameter, Prandtl number and radiation parameter are presented graphically. Results are displayed and show that the rate of heat transfer is higher in a divergent channel as compared to a convergent channel.

On the flow patterns and thermal behaviour of hybrid nanofluid flow inside a microchannel in presence of radiative solar energy

Abstract: Solar radiative energy represents an important source of renewable energy. Of late, hybrid colloidal nanodispersion as an elevated heat transport agent acquires immense interest among researchers rather than unitary nanoliquids. The aim of this investigation was to quantify the flow patterns and heat transfer behaviour of hybrid nanoliquids in presence of nonlinear solar radiation for various solar thermal apparatus. Alumina–copper nanoingredients with water as host fluid are considered. The leading PDEs of our system are turned into ODEs by using prevalent similarity transformation. After that those ODEs have been solved by RK-4-based shooting method. Subsequently, the influences of relevant parameters on the heat transfer of fluid have been talked over on behalf of graphical and tabular approach. Extracted results are verified with experimental plus simulated data. Results communicate that solar radiation fosters heat transport in suction. Hybrid solution exhibits impressive increment in heat transport for suction. Though injection reduces the effect, the decay rate is slower for hybrid nanocomposite. Flipping nature of velocity is perceived for Reynolds number, rotational parameter, and nanoparticle concentration except the variations of suction/injection parameter. We believe that this comprehensive investigation will have potential applications in solar thermal power fabrication, solar ponds, solar thermo electric cells, etc.

Patterns in Wall-Bounded Shear Flows

Abstract: Experiments and numerical simulations have shown that turbulence in transitional wall-bounded shear flows frequently takes the form of long oblique bands if the domains are sufficiently large to accommodate them. These turbulent bands have been observed in plane Couette flow, plane Poiseuille flow, counter-rotating Taylor–Couette flow, torsional Couette flow, and annular pipe flow. At their upper Reynolds number threshold, laminar regions carve out gaps in otherwise uniform turbulence, ultimately forming regular turbulent–laminar patterns with a large spatial wavelength. At the lower threshold, isolated turbulent bands sparsely populate otherwise laminar domains, and complete laminarization takes place via their disappearance. We review results for plane Couette flow, plane Poiseuille flow, and free-slip Waleffe flow, focusing on thresholds, wavelengths, and mean flows, with many of the results coming from numerical simulations in tilted rectangular domains that form the minimal flow unit for the turbulent–laminar bands.

Peristaltic propulsion of Jeffrey nano-liquid and heat transfer through a symmetrical duct with moving walls in a porous medium

Abstract: In this article, a computational study has been performed on the peristaltic propulsion of nanofluid flow through a porous rectangular duct. A non-Newtonian fluid model, i.e. Jeffrey model is considered to examine the behavior of nanoparticles. A Cartesian coordinate system is adopted for the three-dimensional duct. Furthermore, the three-dimensional rectangular duct contains porous wavy walls. An approximation of long wavelength and small Reynolds number have been applied to formulate the governing equations of momentum, energy, continuity, and concentration equation. These resulting coupled partial differential equations are further solved using Homotopy perturbation method and Genetic algorithm with a combination of Nelder Mead method. The purpose of the Genetic algorithm and Nelder Mead method is to reduce the residual error. For this purpose, numerical comparison of residual error is presented with a Homotopy perturbation method.

Impact of a helical-twisting device on the thermal–hydraulic performance of a nanofluid flow through a tube

Abstract: Creating greater thermal efficiency is desirable yet challenging in many industrial applications. Employing nanofluids for increasing the thermal behavior of the working fluid and consequently the overall heat transfer rate is an interesting solution that has widely been studied in the literature. This study, however, proposes the use of a special geometry of disturber (i.e., a helical-twisting shape) for a circular duct with nanofluid (water–CuO) as the heat transfer medium and a forced convective flow for this objective. To assess the effect of this instrument, FVM is employed to simulate the hydrothermal performance of the flow through the duct and the disturber. The main parameters of the study include the effects of the width of the disturber blade and inlet velocity on Darcy factor and the heat transfer coefficient. The homogenous model was used for the properties of nanomaterial. The results of the simulations show that better turbulence, i.e., a greater performance, is observed as the width of the flow disturber blade increases and the Reynolds number picks up.

Convective heat transfer performance of hybrid nanofluid in a horizontal pipe considering nanoparticles shapes effect

Abstract: In this paper, the problem of steady forced convection heat transfer and fluid flow characteristics of a hybrid nanofluid flowing through an isothermally heated horizontal tube considering various nanoparticle shapes has been investigated numerically. The three dimensionless cylindrical coordinate equations are discretized using the finite volume method and solved via a FORTRAN program. A numerical parametric investigation is carried out for a tube filled with regular water, (TiO2/water) nanofluid and (Ag–TiO2/water) hybrid nanofluid. Four different types of nanoparticle shapes are considered in this study, spherical, cylindrical, platelets and blades, with different volume fractions ranging from 0 to 8% using water as a base liquid. The influence of nanoparticle shape, nanoparticle concentration and Reynolds number on the local Nusselt number and the friction factor is essentially examined. The results showed that the friction factor of both nanofluid and hybrid nanofluid flow was increased as the nanoparticle volume fraction increased for all kinds of nanoparticle shapes, whereas it decreased as the Reynolds number increased. Nusselt number increased with increase in the nanoparticle concentration and Reynolds number. The highest heat transfer rate was acquired for the maximum nanoparticle volume concentration by using blade nanoparticle shape followed by platelet shape, cylindrical shape and lastly the sphere shape. It was found that the maximum values of the friction factor were registered for platelet-shape nanoparticles.