scholarly journals Turbulence Intensity Modulation by Micropolar Fluids

Fluids ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 195
Author(s):  
George Sofiadis ◽  
Ioannis Sarris
Keyword(s):  
Reynolds Number ◽  
Turbulence Intensity ◽  
Fluid Flows ◽  
Turbulent Channel Flow ◽  
High Volume ◽  
Reynolds Numbers ◽  
Intensity Modulation ◽  
Wall Turbulence ◽  
Volume Fractions ◽  
Physical Phenomena

Fluid microstructure nature has a direct effect on turbulence enhancement or attenuation. Certain classes of fluids, such as polymers, tend to reduce turbulence intensity, while others, like dense suspensions, present the opposite results. In this article, we take into consideration the micropolar class of fluids and investigate turbulence intensity modulation for three different Reynolds numbers, as well as different volume fractions of the micropolar density, in a turbulent channel flow. Our findings support that, for low micropolar volume fractions, turbulence presents a monotonic enhancement as the Reynolds number increases. However, on the other hand, for sufficiently high volume fractions, turbulence intensity drops, along with Reynolds number increment. This result is considered to be due to the effect of the micropolar force term on the flow, suppressing near-wall turbulence and enforcing turbulence activity to move further away from the wall. This is the first time that such an observation is made for the class of micropolar fluid flows, and can further assist our understanding of physical phenomena in the more general non-Newtonian flow regime.

scholarly journals Thermoelectric Generation with Impinging Nano-Jets

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 492
Author(s):  
Fatih Selimefendigil ◽  
Hakan F. Oztop ◽  
Mikhail A. Sheremet
Keyword(s):  
Fluid Dynamics ◽  
Power Generation ◽  
Computational Fluid Dynamics ◽  
Reynolds Number ◽  
Conversion Efficiency ◽  
Reynolds Numbers ◽  
Thermoelectric Generation ◽  
Volume Fractions ◽  
Water Jets ◽  
Conversion Efficiencies

In this study, thermoelectric generation with impinging hot and cold nanofluid jets is considered with computational fluid dynamics by using the finite element method. Highly conductive CNT particles are used in the water jets. Impacts of the Reynolds number of nanojet stream combinations (between (Re1, Re2) = (250, 250) to (1000, 1000)), horizontal distance of the jet inlet from the thermoelectric device (between (r1, r2) = (−0.25, −0.25) to (1.5, 1.5)), impinging jet inlet to target surfaces (between w2 and 4w2) and solid nanoparticle volume fraction (between 0 and 2%) on the interface temperature variations, thermoelectric output power generation and conversion efficiencies are numerically assessed. Higher powers and efficiencies are achieved when the jet stream Reynolds numbers and nanoparticle volume fractions are increased. Generated power and efficiency enhancements 81.5% and 23.8% when lowest and highest Reynolds number combinations are compared. However, the power enhancement with nanojets using highly conductive CNT particles is 14% at the highest solid volume fractions as compared to pure water jet. Impacts of horizontal location of jet inlets affect the power generation and conversion efficiency and 43% variation in the generated power is achieved. Lower values of distances between the jet inlets to the target surface resulted in higher power generation while an optimum value for the highest efficiency is obtained at location zh = 2.5ws. There is 18% enhancement in the conversion efficiency when distances at zh = ws and zh = 2.5ws are compared. Finally, polynomial type regression models are obtained for estimation of generated power and conversion efficiencies for water-jets and nanojets considering various values of jet Reynolds numbers. Accurate predictions are obtained with this modeling approach and it is helpful in assisting the high fidelity computational fluid dynamics simulations results.

An Experimental Investigation of the Effect of Freestream Turbulence on the Wake of a Separated Low-Pressure Turbine Blade at Low Reynolds Numbers

1999 ◽  
Vol 122 (2) ◽  
pp. 431-433 ◽  
Author(s):  
C. G. Murawski ◽  
K. Vafai
Keyword(s):  
Reynolds Number ◽  
Turbine Blade ◽  
Turbulence Intensity ◽  
Reynolds Numbers ◽  
Low Pressure ◽  
Freestream Turbulence ◽  
Suction Surface ◽  
Low Reynolds Numbers ◽  
Low Pressure Turbine ◽  
Flow Reynolds Number

An experimental study was conducted in a two-dimensional linear cascade, focusing on the suction surface of a low pressure turbine blade. Flow Reynolds numbers, based on exit velocity and suction length, have been varied from 50,000 to 300,000. The freestream turbulence intensity was varied from 1.1 to 8.1 percent. Separation was observed at all test Reynolds numbers. Increasing the flow Reynolds number, without changing freestream turbulence, resulted in a rearward movement of the onset of separation and shrinkage of the separation zone. Increasing the freestream turbulence intensity, without changing Reynolds number, resulted in shrinkage of the separation region on the suction surface. The influences on the blade’s wake from altering freestream turbulence and Reynolds number are also documented. It is shown that width of the wake and velocity defect rise with a decrease in either turbulence level or chord Reynolds number. [S0098-2202(00)00202-9]

Using Turbulence Intensity and Reynolds Number to Predict Flow Separation on a Highly Loaded, Low-Pressure Gas Turbine Blade at Low Reynolds Numbers

2018 ◽  
Author(s):  
Kenneth Van Treuren ◽  
Tyler Pharris ◽  
Olivia Hirst
Keyword(s):  
Reynolds Number ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Turbulence Intensity ◽  
Flow Separation ◽  
Reynolds Numbers ◽  
Low Pressure ◽  
High Altitudes ◽  
Low Reynolds Numbers ◽  
Low Pressure Turbine

The low-pressure turbine has become more important in the last few decades because of the increased emphasis on higher overall pressure and bypass ratios. The desire is to increase blade loading to reduce blade counts and stages in the low-pressure turbine of a gas turbine engine. Increased turbine inlet temperatures for newer cycles results in higher temperatures in the low-pressure turbine, especially the latter stages, where cooling technologies are not used. These higher temperatures lead to higher work from the turbine and this, combined with the high loadings, can lead to flow separation. Separation is more likely in engines operating at high altitudes and reduced throttle setting. At the high Reynolds numbers found at takeoff, the flow over a low-pressure turbine blade tends to stay attached. At lower blade Reynolds numbers (25,000 to 200,000), found during cruise at high altitudes, the flow on the suction surface of the low-pressure turbine blades is inclined to separate. This paper is a study on the flow characteristics of the L1A turbine blade at three low Reynolds numbers (60,000, 108,000, and 165,000) and 15 turbulence intensities (1.89% to 19.87%) in a steady flow cascade wind tunnel. With this data, it is possible to examine the impact of Reynolds number and turbulence intensity on the location of the initiation of flow separation, the flow separation zone, and the reattachment location. Quantifying the change in separated flow as a result of varying Reynolds numbers and turbulence intensities will help to characterize the low momentum flow environments in which the low-pressure turbine must operate and how this might impact the operation of the engine. Based on the data presented, it is possible to predict the location and size of the separation as a function of both the Reynolds number and upstream freestream turbulence intensity (FSTI). Being able to predict this flow behavior can lead to more effective blade designs using either passive or active flow control to reduce or eliminate flow separation.

Modelling smooth- and transitionally rough-wall turbulent channel flow by leveraging inner–outer interactions and principal component analysis

2019 ◽  
Vol 863 ◽  
pp. 407-453 ◽  
Author(s):  
Sicong Wu ◽  
Kenneth T. Christensen ◽  
Carlos Pantano
Keyword(s):  
Principal Component Analysis ◽  
Outer Layer ◽  
Principal Component ◽  
Turbulent Channel Flow ◽  
Reynolds Numbers ◽  
Wall Turbulence ◽  
Rough Wall ◽  
Smooth Wall ◽  
Wall Flow ◽  
Near Wall

Direct numerical simulations (DNS) of turbulent channel flow over rough surfaces, formed from hexagonally packed arrays of hemispheres on both walls, were performed at friction Reynolds numbers $Re_{\unicode[STIX]{x1D70F}}=200$, $400$ and $600$. The inner normalized roughness height $k^{+}=20$ was maintained for all Reynolds numbers, meaning all flows were classified as transitionally rough. The spacing between hemispheres was varied within $d/k=2$–$4$. The statistical properties of the rough-wall flows were contrasted against a complementary smooth-wall DNS at $Re_{\unicode[STIX]{x1D70F}}=400$ and literature data at $Re_{\unicode[STIX]{x1D70F}}=2003$ revealing strong modifications of the near-wall turbulence, although the outer-layer structure was found to be qualitatively consistent with smooth-wall flow. Amplitude modulation (AM) analysis was used to explore the degree of interaction between the flow in the roughness sublayer and that of the outer layer utilizing all velocity components. This analysis revealed stronger modulation effects, compared to smooth-wall flow, on the near-wall small-scale fluctuations by the larger-scale structures residing in the outer layer irrespective of roughness arrangement and Reynolds number. A predictive inner–outer model based on these interactions, and exploiting principal component analysis (PCA), was developed to predict the statistics of higher-order moments of all velocity fluctuations, thus addressing modelling of anisotropic effects introduced by roughness. The results show excellent agreement between the predicted near-wall statistics up to fourth-order moments compared to the original statistics from the DNS, which highlights the utility of the PCA-enhanced AM model in generating physics-based predictions in both smooth- and rough-wall turbulence.

Numerical Simulation of Vortex Shedding From a Circular Cylinder in the Presence of a Free Surface

2003 ◽  
Author(s):  
S. Nagaya ◽  
R. E. Baddour
Keyword(s):  
Numerical Simulation ◽  
Reynolds Number ◽  
Free Surface ◽  
Circular Cylinder ◽  
Vortex Shedding ◽  
Fluid Flows ◽  
Reynolds Numbers ◽  
Cfd Simulations ◽  
Induced Drag

CFD simulations of crossflows around a 2-D circular cylinder and the resulting vortex shedding from the cylinder are conducted in the present study. The capability of the CFD solver for vortex shedding simulation from a circular cylinder is validated in terms of the induced drag and lifting forces and associated Strouhal numbers computations. The validations are done for uniform horizontal fluid flows at various Reynolds numbers in the range 103 to 5×105. Crossflows around the circular cylinder beneath a free surface are also simulated in order to investigate the characteristics of the interaction between vortex shedding and a free surface at Reynolds number 5×105. The influence of the presence of the free surface on the vortex shedding due to the cylinder is discussed.

Large-Eddy Simulation of Flow in a Low-Pressure Turbine Cascade

2012 ◽  
Author(s):  
Gorazd Medic ◽  
Om Sharma
Keyword(s):  
Reynolds Number ◽  
Turbulence Intensity ◽  
Free Stream ◽  
Reynolds Numbers ◽  
Scale Model ◽  
Subgrid Scale ◽  
Low Pressure Turbine ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Subgrid Scale Model

Flow over three low-pressure turbine airfoils presented in [1] is analyzed for a range of Reynolds numbers (30,000 to 150,000) by means of large-eddy simulation. Baseline computational grid for these 2D linear cascade configurations consisted of 35 millions cells, and additional finer grids of 70 millions cells were used for grid sensitivity studies. For these low Reynolds number flows, this represents a quasi-DNS resolution which minimizes the role of the subgrid-scale model — however, WALE subgrid-scale model [7] was still employed. The configurations were analyzed for low free-stream turbulence intensity, as well as for 4% turbulence intensity at free-stream. Laminar separation exists on the suction side, and, depending on the Reynolds number, the flow at the outer edge of the separation either transitions, and the separation closes before the trailing edge, or not. Detailed comparisons to measurements are presented for computed surface pressure and total pressure losses over the range of Reynolds numbers for all three airfoils; these show that LES analyses are able to capture the main trends across all three geometries.

CFD Study of the Pressure Distribution on the Back of a 3-D Bluff Body (CUBE) of Air Flow in Different Reynolds Numbers

2009 ◽  
Author(s):  
Mohammad Javad Izadi ◽  
Pegah Asghari ◽  
Malihe Kamkar Delakeh
Keyword(s):  
Reynolds Number ◽  
Bluff Body ◽  
Free Stream ◽  
Fluid Flows ◽  
Reynolds Numbers ◽  
The Body ◽  
Stream Velocity ◽  
Bluff Bodies ◽  
Unsteady Forces ◽  
Flow Round

The study of flow around bluff bodies is important, and has many applications in industry. Up to now, a few numerical studies have been done in this field. In this research a turbulent unsteady flow round a cube is simulated numerically. The LES method is used to simulate the turbulent flow around the cube since this method is more accurate to model time-depended flows than other numerical methods. When the air as an ideal fluid flows over the cube, flow separate from the back of the body and unsteady vortices appears, causing a large wake behind the cube. The Near-Wake (wake close to the body) plays an important role in determining the steady and unsteady forces on the body. In this study, to see the effect of the free stream velocity on the surface pressure behind the body, the Reynolds number is varied from one to four million and the pressure on the back of the cube is calculated numerically. From the results of this study, it can be seen that as the velocity or the Reynolds number increased, the pressure on the surface behind the cube decreased, but the rate of this decrease, increased as the free stream flow velocity increased. For high free stream velocities the base pressure did not change as much and therefore the base drag coefficient stayed constant (around 1.0).

scholarly journals Reynolds number trend of hierarchies and scale interactions in turbulent boundary layers

2017 ◽  
Vol 375 (2089) ◽  
pp. 20160077 ◽  
Author(s):  
W. J. Baars ◽  
N. Hutchins ◽  
I. Marusic
Keyword(s):  
Reynolds Number ◽  
Large Scale ◽  
Reynolds Numbers ◽  
Shear Layers ◽  
Wall Turbulence ◽  
Small Scale ◽  
Velocity Fluctuations ◽  
Scale Interaction ◽  
High Reynolds ◽  
Near Wall

Small-scale velocity fluctuations in turbulent boundary layers are often coupled with the larger-scale motions. Studying the nature and extent of this scale interaction allows for a statistically representative description of the small scales over a time scale of the larger, coherent scales. In this study, we consider temporal data from hot-wire anemometry at Reynolds numbers ranging from Re τ ≈2800 to 22 800, in order to reveal how the scale interaction varies with Reynolds number. Large-scale conditional views of the representative amplitude and frequency of the small-scale turbulence, relative to the large-scale features, complement the existing consensus on large-scale modulation of the small-scale dynamics in the near-wall region. Modulation is a type of scale interaction, where the amplitude of the small-scale fluctuations is continuously proportional to the near-wall footprint of the large-scale velocity fluctuations. Aside from this amplitude modulation phenomenon, we reveal the influence of the large-scale motions on the characteristic frequency of the small scales, known as frequency modulation. From the wall-normal trends in the conditional averages of the small-scale properties, it is revealed how the near-wall modulation transitions to an intermittent-type scale arrangement in the log-region. On average, the amplitude of the small-scale velocity fluctuations only deviates from its mean value in a confined temporal domain, the duration of which is fixed in terms of the local Taylor time scale. These concentrated temporal regions are centred on the internal shear layers of the large-scale uniform momentum zones, which exhibit regions of positive and negative streamwise velocity fluctuations. With an increasing scale separation at high Reynolds numbers, this interaction pattern encompasses the features found in studies on internal shear layers and concentrated vorticity fluctuations in high-Reynolds-number wall turbulence. This article is part of the themed issue ‘Toward the development of high-fidelity models of wall turbulence at large Reynolds number’.

Effect of Reynolds Number and Surface Roughness on the Efficiency of Centrifugal Pumps

2003 ◽  
Vol 125 (4) ◽  
pp. 670-679 ◽  
Author(s):  
J. F. Gu¨lich
Keyword(s):  
Reynolds Number ◽  
Reynolds Numbers ◽  
Physical Models ◽  
Wall Turbulence ◽  
Pump Efficiency ◽  
Hydraulic Efficiency ◽  
Centrifugal Pumps ◽  
Roughness Effects ◽  
Specific Speed ◽  
Near Wall Turbulence

A procedure has been developed to predict the effects of roughness and Reynolds number on the change in efficiency from a model or baseline to a prototype pump (“efficiency scaling”). The analysis of individual losses takes into account different roughnesses of impeller, diffuser/volute, impeller side disks, and casing walls in the impeller side rooms. The method also allows to predict the effect of roughness and Reynolds number on the hydraulic efficiency. The calculations are based on physical models but the weighting of impeller versus diffuser/volute roughness and the fraction of scalable losses within impeller and diffuser/volute are determined empirically from the analysis of tests with industrial pumps. The fraction of scalable impeller/diffuser/volute losses is found to decrease with growing specific speed. Roughness effects in the diffuser/volute are stronger than in the impeller, but the dominance of the stator over the rotor decreases with increasing specific speed. The procedure includes all flow regimes from laminar to turbulent and from hydraulically smooth to fully rough. It is validated by tests with viscosities between 0.2 to 3000 cSt and Reynolds numbers between 1500 and 108. The hydraulic losses depend on the patterns of roughness, near-wall turbulence, and the actual velocity distribution in the hydraulic passages. These effects—which are as yet not amenable to analysis—limit the accuracy of any efficiency prediction procedure for decelerated flows.

Fluid Flow Analysis in a Pebble Bed Modular Reactor Using RANS Turbulence Models

2008 ◽  
Author(s):  
Margaret Mkhosi ◽  
Richard Denning ◽  
Audeen Fentiman
Keyword(s):  
Reynolds Number ◽  
Fluid Flow ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Turbulence Intensity ◽  
Turbulence Models ◽  
Reynolds Numbers ◽  
Pebble Bed ◽  
Rans Turbulence Models ◽  
Flow Domain

The computational fluid dynamics code FLUENT has been used to analyze turbulent fluid flow over pebbles in a pebble bed modular reactor. The objective of the analysis is to evaluate the capability of the various RANS turbulence models to predict mean velocities, turbulent kinetic energy, and turbulence intensity inside the bed. The code was run using three RANS turbulence models: standard k-ε, standard k-ω and the Reynolds stress turbulence models at turbulent Reynolds numbers, corresponding to normal operation of the reactor. For the k-ε turbulence model, the analyses were performed at a range of Reynolds numbers between 1300 and 22 000 based on the approach velocity and the sphere diameter of 6 cm. Predictions of the mean velocities, turbulent kinetic energy, and turbulence intensity for the three models are compared at the Reynolds number of 5500 for all the RANS models analyzed. A unit-cell approach is used and the fluid flow domain consists of three unit cells. The packing of the pebbles is an orthorhombic arrangement consisting of seven layers of pebbles with the mean flow parallel to the z-axis. For each Reynolds number analyzed, the velocity is observed to accelerate to twice the inlet velocity within the pebble bed. From the velocity contours, it can be seen that the flow appears to have reached an asymptotic behavior by the end of the first unit cell. The velocity vectors for the standard k-ε and the Reynolds stress model show similar patterns for the Reynolds number analyzed. For the standard k-ω, the vectors are different from the other two. Secondary flow structures are observed for the standard k-ω after the flow passes through the gap between spheres. This feature is not observable in the case of both the standard k-ε and the RSM. Analysis of the turbulent kinetic energy contours shows that there is higher turbulence kinetic energy near the inlet than inside the bed. As the Reynolds number increases, kinetic energy inside the bed increases. The turbulent kinetic energy values obtained for the standard k-ε and the RSM are similar, showing maximum turbulence kinetic energy of 7.5 m2·s−2, whereas the standard k-ω shows a maximum of about 20 m2·s−2. Another observation is that the turbulence intensity is spread throughout the flow domain for the k-ε and RSM whereas for the k-ω, the intensity is concentrated at the front of the second sphere. Preliminary analysis performed for the pressure drop using the standard k-ε model for various velocities show that the dependence of pressure on velocity varies as V1.76.

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