Showing papers on "Turbulence published in 2021"

Use of Organic and Copper-Based Nanoparticles on the Turbulator Installment in a Shell Tube Heat Exchanger: A CFD-Based Simulation Approach by Using Nanofluids

Abstract: Heat exchangers with unique specifications are administered in the food industry, which has expanded its sphere of influence even to the automotive industry due to this feature. It has been used for convenient maintenance and much easier cleaning. In this study, two different nanomaterials, such as Cu-based nanoparticles and an organic nanoparticle of Chloro-difluoromethane (R22), were used as nanofluids to enhance the efficiency of heat transfer in a turbulator. It is simulated by computational fluid dynamics software (Ansys-Fluent) to evaluate the Nusselt number versus Reynolds number for different variables. These variables are diameter ratio, torsion pitch ratio, and two different nanofluids through the shell tube heat exchanger. It is evident that for higher diameter ratios, the Nusselt number has been increased significantly in higher Reynolds numbers as the heat transfer has been increased in turbulators. For organic fluids (R22), the Nusselt number has been increased significantly in higher Reynolds numbers as the heat transfer has been increased in turbulators due to the proximity of heat transfer charges. At higher torsion pitch ratios, the Nusselt number has been increased significantly in the higher Reynolds number as the heat transfer has been increased in turbulators, especially in higher velocities and pipe turbulence torsions.

A review on turbulent flow over rough surfaces: Fundamentals and theories

Abstract: Surface roughness can significantly influence the fluid dynamics and heat transfer in convective flows by inducing perturbations in the velocity profile which affect surface drag, turbulent mixing and heat transfer. While surface roughness can often negatively affect the performance of systems, it can also lead to performance improvements, such as in convective flows where roughness elements have been shown to enhance heat transfer. Turbulent flows over rough surfaces have been studied for about a century leading to significant developments in this field. Direct Numerical Simulation (DNS) has made significant contributions to the knowledge of turbulent flows over rough surfaces as well as evaluation of the developed theories. Moreover, the turbulent closures model has seen wide use for simulation of rough-wall turbulent flows in practical applications where DNS is hindered by its complexity and computational resources. Despite a significant number of experimental and CFD studies and the latest advances in this field, a recent review was not available. Therefore, this review surveys the past and recent experimental and numerical studies to address the fundamentals and theories related to the structure of turbulent flows over rough walls. This study chiefly investigates the structure of mean flow profile over rough surfaces, and its correlation with smooth wall mean flow profile. This review study can contribute to prospective experimental and CFD work, and for characterising rough-wall turbulent flows and heat transfer in different academic and engineering applications such as aerodynamics, hydraulics, meteorology, and manufacturing. The review concludes that despite significant progress, the structure of turbulent flow is still not fully understood. This is mainly due to a lack of systematic studies on the structure of turbulent flow and also due to the variety of roughness which influence the dynamics of the flow in the roughness sublayers. The current roughness scale (sand-grain roughness height) fails to completely characterise roughness in many cases. Therefore, there is a need for a universal roughness scale that can describe every type of roughness and be used in any rough-flow regimes, including fully rough and transitionally rough regimes.

Predicting the Drag of Rough Surfaces

Abstract: Reliable full-scale prediction of drag due to rough wall-bounded turbulent fluid flow remains a challenge. Currently, the uncertainty is at least 10%, with consequences, for example, on energy and ...

The JOREK non-linear extended MHD code and applications to large-scale instabilities and their control in magnetically confined fusion plasmas

Abstract: JOREK is a massively parallel fully implicit non-linear extended magneto-hydrodynamic (MHD) code for realistic tokamak X-point plasmas. It has become a widely used versatile simulation code for studying large-scale plasma instabilities and their control and is continuously developed in an international community with strong involvements in the European fusion research programme and ITER organization. This article gives a comprehensive overview of the physics models implemented, numerical methods applied for solving the equations and physics studies performed with the code. A dedicated section highlights some of the verification work done for the code. A hierarchy of different physics models is available including a free boundary and resistive wall extension and hybrid kinetic-fluid models. The code allows for flux-surface aligned iso-parametric finite element grids in single and double X-point plasmas which can be extended to the true physical walls and uses a robust fully implicit time stepping. Particular focus is laid on plasma edge and scrape-off layer (SOL) physics as well as disruption related phenomena. Among the key results obtained with JOREK regarding plasma edge and SOL, are deep insights into the dynamics of edge localized modes (ELMs), ELM cycles, and ELM control by resonant magnetic perturbations, pellet injection, as well as by vertical magnetic kicks. Also ELM free regimes, detachment physics, the generation and transport of impurities during an ELM, and electrostatic turbulence in the pedestal region are investigated. Regarding disruptions, the focus is on the dynamics of the thermal quench (TQ) and current quench triggered by massive gas injection and shattered pellet injection, runaway electron (RE) dynamics as well as the RE interaction with MHD modes, and vertical displacement events. Also the seeding and suppression of tearing modes (TMs), the dynamics of naturally occurring TQs triggered by locked modes, and radiative collapses are being studied.

Unsupervised deep learning for super-resolution reconstruction of turbulence

Abstract: Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for training. This limitation hinders more practical applications of super-resolution reconstruction. Therefore, we present an unsupervised learning model that adopts a cycle-consistent generative adversarial network (CycleGAN) that can be trained with unpaired turbulence data for super-resolution reconstruction. Our model is validated using three examples: (i) recovering the original flow field from filtered data using direct numerical simulation (DNS) of homogeneous isotropic turbulence; (ii) reconstructing full-resolution fields using partially measured data from the DNS of turbulent channel flows; and (iii) generating a DNS-resolution flow field from large-eddy simulation (LES) data for turbulent channel flows. In examples (i) and (ii), for which paired data are available for supervised learning, our unsupervised model demonstrates qualitatively and quantitatively similar performance as that of the best supervised learning model. More importantly, in example (iii), where supervised learning is impossible, our model successfully reconstructs the high-resolution flow field of statistical DNS quality from the LES data. Furthermore, we find that the present model has almost universal applicability to all values of Reynolds numbers within the tested range. This demonstrates that unsupervised learning of turbulence data is indeed possible, opening a new door for the wide application of super-resolution reconstruction of turbulent fields.

Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

Abstract: We investigate the applicability of machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at the friction Reynolds number of $Re_\tau=110$ in a minimum domain which can maintain coherent structures of turbulence. Training data set are prepared by direct numerical simulation (DNS). The present ML-ROM is constructed by combining a three-dimensional convolutional neural network autoencoder (CNN-AE) and a long short-term memory (LSTM). The CNN-AE works to map high-dimensional flow fields into a low-dimensional latent space. The LSTM is then utilized to predict a temporal evolution of the latent vectors obtained by the CNN-AE. The combination of CNN-AE and LSTM can represent the spatio-temporal high-dimensional dynamics of flow fields by only integrating the temporal evolution of the low-dimensional latent dynamics. The turbulent flow fields reproduced by the present ML-ROM show statistical agreement with the reference DNS data in time-ensemble sense, which can also be found through an orbit-based analysis. Influences of the population of vortical structures contained in the domain and the time interval used for temporal prediction on the ML- ROM performance are also investigated. The potential and limitation of the present ML-ROM for turbulence analysis are discussed at the end of our presentation.

Effect of hybrid nanofluid on heat transfer performance of parabolic trough solar collector receiver

Abstract: In this study, three-dimensional heat transfer and flow characteristics of hybrid nanofluids under turbulent flow condition in a parabolic trough solar collector (PTC) receiver has been investigated. Ag–ZnO/Syltherm 800, Ag–TiO2/Syltherm 800, and Ag–MgO/Syltherm 800 hybrid nanofluids with 1.0%, 2.0%, 3.0%, and 4.0% nanoparticle volume fractions are used as working fluids. Reynolds number is between 10,000 and 80,000. The temperature of the fluid is taken as 500 K. The C++ homemade code has been written for the nonuniform heat flux boundary condition for the outer surface of the receiver. Variations of thermal efficiency, heat transfer coefficient, friction factor, PEC number, Nusselt number, and temperature distribution are presented for three different types of hybrid nanofluids and four different nanoparticle volume fractions with different Reynolds numbers. Also, the graphs of the average percent increase according to Syltherm 800 are given for the working parameters. According to the results of the study, all hybrid nanofluids are found to provide superiority over the base fluid (Syltherm 800) with respect to heat transfer and flow features. Heat transfer augments with the growth of Reynolds number and nanoparticle volume fraction. Thermal efficiency, which is one of the important parameters for PTC, decreases with increasing Reynolds number and increases with the increasing volume fraction of nanoparticle. It is obtained that the most efficient working fluid for the PTC receiver is the Ag–MgO/Syltherm 800 hybrid nanofluid with 4.0% nanoparticle volume fraction.

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

Abstract: 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.

From coarse wall measurements to turbulent velocity fields through deep learning

Abstract: This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse wall measurements. The method is applied both for the resolution enhancement of wall fields and the estimation of wall-parallel velocity fields from coarse wall measurements of shear stress and pressure. The analysis has been carried out with a database of a turbulent open-channel flow with a friction Reynolds number R e τ = 180 generated through direct numerical simulation. Coarse wall measurements have been generated with three different downsampling factors f d = [ 4 , 8 , 16 ] from the high-resolution fields, and wall-parallel velocity fields have been reconstructed at four inner-scaled wall-normal distances y + = [ 15 , 30 , 50 , 100 ]. We first show that SRGAN can be used to enhance the resolution of coarse wall measurements. If compared with the direct reconstruction from the sole coarse wall measurements, SRGAN provides better instantaneous reconstructions, in terms of both mean-squared error and spectral-fractional error. Even though lower resolutions in the input wall data make it more challenging to achieve highly accurate predictions, the proposed SRGAN-based network yields very good reconstruction results. Furthermore, it is shown that even for the most challenging cases, the SRGAN is capable of capturing the large-scale structures that populate the flow. The proposed novel methodology has a great potential for closed-loop control applications relying on non-intrusive sensing.

Physics-guided deep learning framework for predictive modeling of the Reynolds stress anisotropy

Abstract: Despite a cost-effective option in practical engineering, Reynolds-averaged Navier-Stokes simulations are facing the ever-growing demand for more accurate turbulence models. Recently, emerging machine learning techniques are making promising impact in turbulence modeling, but in their infancy for widespread industrial adoption. Towards this end, this work proposes a universal, inherently interpretable machine learning framework of turbulence modeling, which mainly consists of two parallel machine-learning-based modules to respectively infer the integrity basis and closure coefficients. At every phase of the model development, both data representing the evolution dynamics of turbulence and domain-knowledge representing prior physical considerations are properly fed and reasonably converted into modeling knowledge. Thus, the developed model is both data- and knowledge-driven. Specifically, a version with pre-constrained integrity basis is provided to demonstrate detailedly how to integrate domain-knowledge, how to design a fair and robust training strategy, and how to evaluate the data-driven model. Plain neural network and residual neural network as the building blocks in each module are compared. Emphases are made on three-fold: (i) a compact input feature parameterizing the newly-proposed turbulent timescale is introduced to release nonunique mappings between conventional input arguments and output Reynolds stress; (ii) the realizability limiter is developed to overcome under-constraint of modeled stress; and (iii) constraints of fairness and noisy-sensitivity are first included in the training procedure. In such endeavors, an invariant, realizable, unbiased and robust data-driven turbulence model is achieved, and does gain good generalization across channel flows at different Reynolds numbers and duct flows with various aspect ratios.

Convolutional-network models to predict wall-bounded turbulence from wall quantities

Abstract: Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a fully convolutional neural network (FCN) which directly predicts the fluctuations, while the second one reconstructs the flow fields using a linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), and is hence named FCN-POD. Both models are trained using data from direct numerical simulations at friction Reynolds numbers of the original training data. We expect that these non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence.

Reynolds number scaling of the peak turbulence intensity in wall flows

Abstract: The celebrated wall-scaling works for many statistical averages in turbulent flows near smooth walls, but the streamwise velocity fluctuation, owing to the natural constraint of boundedness on the dissipation rate at the wall. This new formula agrees well with the existing data and, in contrast to the logarithmic growth, supports the classical wall-scaling for turbulent intensity at asymptotically high Reynolds numbers.

Forcing statistics in resolvent analysis: application in minimal turbulent Couette flow

Abstract: An analysis of the statistics of the nonlinear terms in resolvent analysis is performed in this work for turbulent Couette flow at Reynolds number 400. Data from a direct numerical simulation of a minimal flow unit is used to compute the covariance matrix of the velocity. From the same data, we computed the nonlinear terms of the Navier–Stokes equations (treated as forcing), which allowed us to compute the covariance matrix of the forcing. The quantitative relation between the two covariances via the resolvent operator is confirmed here for the first time, accounting for relevant signal processing issues related to the windowing procedure for frequency-domain quantities. Such exact correspondence allowed the eduction of the most relevant force components for the dominant structures in this flow, which participate in the self-sustaining cycle of turbulence: (i) streamwise vortices and streaks, and (ii) spanwise-coherent fluctuations of spanwise velocity. The results show a dominance by a subset of the nonlinear terms for the prediction of the full statistics of streamwise vortices and streaks; a single term is seen to be dominant for spanwise motions. A relevant feature observed in these cases is that the forcing covariance is dominated by its first eigenfunction, showing that nonlinear terms also have a coherent structure at low frequencies in this flow. Different forcing components are also coherent between them, which leads to constructive and destructive interferences that greatly modify the flow response. These are key features of forcing ‘colour’ for the present flow.

Thermal-hydraulic performance and design parameters in a curved-corrugated channel with L-shaped baffles and nanofluid

Abstract: High heat generation is seen as a major issue in most mechanical and manufacturing industries, carrying with huge sub-problems. One of the possible cooling techniques is the combination of two or more passive methods, in particular for those parts with complex geometry. In this study, flow structure and heat transfer characteristics of the novel channel namely: curved-corrugated channel are numerically studied with using ZnO-water nanofluid and presence of L-shaped baffles. The influences of corrugations, baffles manner arrangement, and geometric parameters; corner angle (γ= 30°,45°,60°, and 90°) and blockage ratio (BR=0.25,0.3. 0.35, and 0.4), at different Reynolds number (8000–32000) and volume fraction of ZnO particles (0–4%) are evaluated using thermal-hydraulic performance method. The multi-phase mixture and the κ-e model are used to simulate turbulent nanofluid flows inside the curved-corrugated channels at constant temperature condition (T = 355 K). The results reveal that the formation of vortex flow and increased turbulence due to effects of corrugations and baffles can improve the heat transfer enhancement. Inline arrangement of baffle is superior to the staggered arrangement in thermal-hydraulic performance (PEC), and at lowest angle 30° provide the best PEC. Regarding the friction ratio and compared to those of a plain channel, the effect of blockage ratio is considerable as it yields a multiplier impact of the corner angle. Reducing the blockage ratio and the corner angle, i.e. 0.25 and 30°, yields to the best PEC at 1.99. New correlations for Nusselt number and friction factor for baffled curved-corrugated channel with using nanofluid are also reported.

Turbulence transport in the solar corona: Theory, modeling, and Parker Solar Probe

Abstract: A primary goal of the Parker Solar Probe (PSP) Mission is to answer the outstanding question of how the solar corona plasma is heated to the high temperatures needed for the acceleration of the solar wind. Various heating mechanisms have been suggested, but one that is gaining increasing credence is associated with the dissipation of low frequency magnetohyrodynamic (MHD) turbulence. However, the MHD turbulence models come in several flavors: one in which outwardly propagating Alfven waves experience reflection from the large-scale flow and density gradients associated with the solar corona, and the resulting counterpropagating Alfven waves couple nonlinearly to produce quasi-2D turbulence that dissipates and heats the corona, thereby driving the solar wind. The second approach eschews a dominant outward flux of Alfven waves but argues instead that quasi-2D turbulence dominates the lower coronal plasma and is generated in the constantly upwelling magnetic carpet, experiencing dissipation as it is advected through the corona and into the solar wind, yielding temperatures in the corona that exceed a million degrees. We review the two turbulence models, describe the modeling that has been done, and relate PSP observations to the basic predictions of both models. Although PSP measurements are made in the super-Alfvenic solar wind, the observations are close to the coronal region, thus providing a glimpse into the likely properties of coronal turbulence. Observations of low-frequency MHD turbulence by PSP in the super-Alfvenic solar wind allow us to place constraints on models of the turbulently heated solar corona that drive the supersonic solar wind.

Shock-induced heating and transition to turbulence in a hypersonic boundary layer

Abstract: The interaction between an incident shock wave and a Mach-6 undisturbed hypersonic laminar boundary layer over a cold wall is addressed using direct numerical simulations (DNS) and wall-modelled large-eddy simulations (WMLES) at different angles of incidence. At sufficiently high shock-incidence angles, the boundary layer transitions to turbulence via breakdown of near-wall streaks shortly downstream of the shock impingement, without the need of any inflow free-stream disturbances. The transition causes a localized significant increase in the Stanton number and skin-friction coefficient, with high incidence angles augmenting the peak thermomechanical loads in an approximately linear way. Statistical analyses of the boundary layer downstream of the interaction for each case are provided that quantify streamwise spatial variations of the Reynolds analogy factors and indicate a breakdown of the Morkovin's hypothesis near the wall, where velocity and temperature become correlated. A modified strong Reynolds analogy with a fixed turbulent Prandtl number is observed to perform best. Conventional transformations fail at collapsing the mean velocity profiles on the incompressible log law. The WMLES prompts transition and peak heating, delays separation and advances reattachment, thereby shortening the separation bubble. When the shock leads to transition, WMLES provides predictions of DNS peak thermomechanical loads within at a computational cost lower than DNS by two orders of magnitude. Downstream of the interaction, in the turbulent boundary layer, the WMLES agrees well with DNS results for the Reynolds analogy factor, the mean profiles of velocity and temperature, including the temperature peak, and the temperature/velocity correlation.

Numerical evaluation of turbulence heat transfer and fluid flow of hybrid nanofluids in a pipe with innovative vortex generator

Abstract: In the present work, hybrid nanofluids flow and heat transfer in a pipe equipped with vortex generator are evaluated numerically. At the first part, the impact of the type of working fluid (two various hybrid nanofluids in comparison with pure water at φ = 3%) and at the second part, impact of the volume concentration of selected hybrid nanofluid (based on section one) on the turbulence thermal performance of the pipe with innovative vortex generator are evaluated numerically. All the simulations were performed for the Reynolds number range between 4125 and 5363. The considered hybrid nanofluids include silver (Ag) and graphene (HEG) nanoparticles/water and MWCNT–Fe3O4/water. The proposed vortex generator has 18 blades to create secondary flows. Also, five output ports are considered at the conical part of vortex generator (four side outputs and one axial one). Results indicated that using both two techniques of heat transfer enhancement in a pipe including proposed vortex generator and hybrid nanofluids leads to higher heat transfer rate. As a result, the MWCNT–Fe3O4/water hybrid nanofluid has better thermal performance in all studied Reynolds number. At low Reynolds number (Re = 4125), the maximum thermal performance was achieved at φ = 1% by 11.3% growth in thermal performance. Also, case with φ = 5% has a minimum improvement, 10.5%. Furthermore, at high Reynolds number (Re = 5363), the highest and lowest growths belong to cases φ = 3% and φ = 7% by 9.9 and 7% improvement in thermal performance, respectively.

Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning

Abstract: Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LESs) has received substantial attention recently. Despite some success, particularly in a priori (offline) tests, challenges have been identified that include numerical instabilities in a posteriori (online) tests and generalization (i.e., extrapolation) of trained data-driven SGS models, for example, to higher Reynolds numbers. Here, using the stochastically forced Burgers turbulence as the test-bed, we show that deep neural networks trained using properly pre-conditioned (augmented) data yield stable and accurate a posteriori LES models. Furthermore, we show that transfer learning enables accurate/stable generalization to a flow with 10 × higher Reynolds number.

Turbulence in space plasmas: Who needs it?

Abstract: The complex nonlinear dynamical phenomenon described as turbulence, is known to have a great impact on fluids, magnetohydrodynamic systems, and on plasmas. This paper reviews selected topics on turbulence in these systems, emphasizing the multiscale space-time properties of the turbulence cascade as it transfers energy from large scale reservoirs, through the inertial range, finally dissipating at kinetic scales and producing internal or thermal energy. Application to space and astrophysical plasmas is a pervasive theme. This paper is based on the Maxwell Prize address given at the 2019 American Physical Society Division of Plasma Physics meeting in Fort Lauderdale.

Thresholds for Particle Clumping by the Streaming Instability

Abstract: The streaming instability (SI) is a mechanism to aerodynamically concentrate solids in protoplanetary disks and trigger the formation of planetesimals. The SI produces strong particle clumping if the ratio of solid to gas surface density -- an effective metallicity -- exceeds a critical value. This critical value depends on particle sizes and disk conditions such as radial drift-inducing pressure gradients and levels of turbulence. To quantify these thresholds, we perform a suite of vertically-stratified SI simulations over a range of dust sizes and metallicities. We find a critical metallicity as low as 0.4% for the optimum particle sizes and standard radial pressure gradients (normalized value of $\Pi = 0.05$). This sub-Solar metallicity is lower than previous results due to improved numerical methods and computational effort. We discover a sharp increase in the critical metallicity for small solids, when the dimensionless stopping time (Stokes number) is $\leq 0.01$. We provide simple fits to the size-dependent SI clumping threshold, including generalizations to different disk models and levels of turbulence. We also find that linear, unstratified SI growth rates are a surprisingly poor predictor of particle clumping in non-linear, stratified simulations, especially when the finite resolution of simulations is considered. Our results widen the parameter space for the SI to trigger planetesimal formation.