scholarly journals Rates of Dissipation of Turbulent Kinetic Energy in a High Reynolds Number Tidal Channel

2016 ◽  
Vol 33 (4) ◽  
pp. 817-837 ◽  
Author(s):  
Justine M. McMillan ◽  
Alex E. Hay ◽  
Rolf G. Lueck ◽  
Fabian Wolk
Keyword(s):  
Reynolds Number ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
High Speed ◽  
Flow Speed ◽  
Small Scale ◽  
Tidal Channel ◽  
Dissipation Rates ◽  
Channel Separation ◽  
Shear Probe

AbstractThe ability to estimate the rate of dissipation (ε) of turbulent kinetic energy at middepth in a high-speed tidal channel using broadband acoustic Doppler current profilers (ADCPs) is assessed by making comparisons to direct measurements of ε obtained using shear probes mounted on a streamlined underwater buoy. The investigation was carried out in Grand Passage, Nova Scotia, Canada, where the depth-averaged flow speed reached 2 m s−1 and the Reynolds number was 8 × 107. The speed bin–averaged dissipation rates estimated from the ADCP data agree with the shear probe data to within a factor of 2. Both the ADCP and the shear probe measurements indicate a linear dependence of ε on the cube of the flow speed during flood and much lower dissipation rates during ebb. The ebb–flood asymmetry and the small-scale intermittency in ε are also apparent in the lognormal distributions of the shear probe data. Possible sources of bias and error in the ε estimates are investigated, and the most likely causes of the discrepancy between the ADCP and shear probe estimates are the cross-channel separation of the instruments and the high degree of spatial variability that exists in the channel.

scholarly journals Diffusional and accretional growth of water drops in a rising adiabatic parcel: effects of the turbulent collision kernel

2009 ◽  
Vol 9 (7) ◽  
pp. 2335-2353 ◽  
Author(s):  
W. W. Grabowski ◽  
L.-P. Wang
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Collision Kernel ◽  
Small Scale ◽  
Large Set ◽  
Initiation Time ◽  
Cloud Droplets ◽  
Dissipation Rates ◽  
Speedup Factor

Abstract. A large set of rising adiabatic parcel simulations is executed to investigate the combined diffusional and accretional growth of cloud droplets in maritime and continental conditions, and to assess the impact of enhanced droplet collisions due to small-scale cloud turbulence. The microphysical model applies the droplet number density function to represent spectral evolution of cloud and rain/drizzle drops, and various numbers of bins in the numerical implementation, ranging from 40 to 320. Simulations are performed applying two traditional gravitational collection kernels and two kernels representing collisions of cloud droplets in the turbulent environment, with turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3. The overall result is that the rain initiation time significantly depends on the number of bins used, with earlier initiation of rain when the number of bins is low. This is explained as a combination of the increase of the width of activated droplet spectrum and enhanced numerical spreading of the spectrum during diffusional and collisional growth when the number of model bins is low. Simulations applying around 300 bins seem to produce rain at times which no longer depend on the number of bins, but the activation spectra are unrealistically narrow. These results call for an improved representation of droplet activation in numerical models of the type used in this study. Despite the numerical effects that impact the rain initiation time in different simulations, the turbulent speedup factor, the ratio of the rain initiation time for the turbulent collection kernel and the corresponding time for the gravitational kernel, is approximately independent of aerosol characteristics, parcel vertical velocity, and the number of bins used in the numerical model. The turbulent speedup factor is in the range 0.75–0.85 and 0.60–0.75 for the turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3, respectively.

scholarly journals Diffusional and accretional growth of water drops in a rising adiabatic parcel: effects of the turbulent collision kernel

2008 ◽  
Vol 8 (4) ◽  
pp. 14717-14763 ◽  
Author(s):  
W. W. Grabowski ◽  
L.-P. Wang
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Collision Kernel ◽  
Small Scale ◽  
Large Set ◽  
Initiation Time ◽  
Cloud Droplets ◽  
Dissipation Rates ◽  
Speedup Factor

Abstract. A large set of rising adiabatic parcel simulations is executed to investigate the combined diffusional and accretional growth of cloud droplets in maritime and continental conditions, and to assess the impact of enhanced droplet collisions due to small-scale cloud turbulence. The microphysical model applies the droplet number density function to represent spectral evolution of cloud and rain/drizzle drops, and various numbers of bins in the numerical implementation, ranging from 40 to 320. Simulations are performed applying two traditional gravitational collection kernels and two kernels representing collisions of cloud droplets in the turbulent environment, with turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3. The overall result is that the rain initiation time significantly depends on the number of bins used, with earlier initiation of rain when the number of bins is low. This is explained as a combination of the increase of the width of activated droplet spectrum and enhanced numerical spreading of the spectrum during diffusional and collisional growth when the number of model bins is low. Simulations applying around 300 bins seem to produce rain at times which no longer depend on the number of bins, but the activation spectra are unrealistically narrow. These results call for an improved representation of droplet activation in numerical models of the type used in this study. Despite the numerical effects that impact the rain initiation time in different simulations, the turbulent speedup factor, the ratio of the rain initiation time for the turbulent collection kernel and the corresponding time for the gravitational kernel, is approximately independent of aerosol characteristics, parcel vertical velocity, and the number of bins used in the numerical model. The turbulent speedup factor is in the range 0.75–0.85 and 0.60–0.75 for the turbulent kinetic energy dissipation rates of 100 and 400 cm2 s−3, respectively.

Intermittency in Estuarine Turbulence: A Framework toward Limiting Bias in Microstructure Measurements

2019 ◽  
Vol 36 (10) ◽  
pp. 1917-1932 ◽  
Author(s):  
Kimberly Huguenard ◽  
Kris Bears ◽  
Brandon Lieberthal
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Time Scales ◽  
Small Scale ◽  
Field Sampling ◽  
Intermittent Turbulence ◽  
Statistical Framework ◽  
Dissipation Rates ◽  
Multiple Comparison Test ◽  
Microstructure Measurements

AbstractIntermittent turbulence behavior has a number of implications for field sampling, namely, that if undersampled, it can result in over- or underestimates of turbulent kinetic energy (TKE) dissipation rates. Sampling thresholds and common distributions have previously been defined for oceanic environments, but estuaries remain relatively underrepresented. Utilizing vertical microstructure profilers is a robust way to directly measure TKE dissipation rates; however, microstructure sensors are delicate and conducting a limited number of profiles in a burst is desirable. In this work, a statistical framework is used to characterize intermittency in a partially mixed estuary. In particular, a multiple comparison test is proposed to evaluate the number of profiles required to sufficiently represent TKE dissipation averages. The technique is tested on a microstructure dataset from the Damariscotta River in Maine, which covers seasonal and fortnightly time scales. The Damariscotta River features a variety of bathymetric and channel complexities, which provide the opportunity to examine intermittency as it relates to different processes. Small-scale intermittency is prominent during stratified conditions in shallow locations as well as near channel-shoal morphology, channel bends, and constrictions.

Measurement of small-scale surface velocity and turbulent kinetic energy dissipation rates using infrared imaging

2020 ◽  
Author(s):  
Shelby Metoyer ◽  
Mohammad Barzegar ◽  
Darek Bogucki ◽  
Brian K. Haus ◽  
Mingming Shao
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Infrared Imaging ◽  
Surface Velocity ◽  
Small Scale ◽  
Spatially Correlated ◽  
Dissipation Rates ◽  
Remote Measurements ◽  
Spatially Varying

AbstractShort range infrared (IR) observations of ocean surface reveal complicated spatially varying and evolving structures. Here we present an approach to use spatially correlated time-series IR images, over a time scale of one tenth of a second, of the water surface to derive underlying surface velocity and turbulence fields. The approach here was tested in a laboratory using grid-generated turbulence and a heater assembly. The technique was compared with in situ measurements to validate our IR derived remote measurements. The IR measured turbulent kinetic energy (TKE) dissipation rates were consistent with in situ measured dissipation using a microstructure profiler (VMP). We used measurements of the gradient of the velocity field to calculate TKE dissipation rates at the surface. Based on theoretical and experimental retrievals and designed an approach for oceanic field IR applications.

Turbulent kinetic energy production and flow structures in flows past smooth and rough walls

2019 ◽  
Vol 866 ◽  
pp. 897-928 ◽  
Author(s):  
P. Orlandi
Keyword(s):  
Reynolds Number ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Turbulent Flows ◽  
Energy Production ◽  
Compressive Strain ◽  
Turnover Time ◽  
Flow Structures ◽  
Wall Region ◽  
Rate Of Dissipation

Data available in the literature from direct numerical simulations of two-dimensional turbulent channels by Lee & Moser (J. Fluid Mech., vol. 774, 2015, pp. 395–415), Bernardini et al. (J. Fluid Mech., 742, 2014, pp. 171–191), Yamamoto & Tsuji (Phys. Rev. Fluids, vol. 3, 2018, 012062) and Orlandi et al. (J. Fluid Mech., 770, 2015, pp. 424–441) in a large range of Reynolds number have been used to find that $S^{\ast }$ the ratio between the eddy turnover time ($q^{2}/\unicode[STIX]{x1D716}$, with $q^{2}$ being twice the turbulent kinetic energy and $\unicode[STIX]{x1D716}$ the isotropic rate of dissipation) and the time scale of the mean deformation ($1/S$), scales very well with the Reynolds number in the wall region. The good scaling is due to the eddy turnover time, although the turbulent kinetic energy and the rate of isotropic dissipation show a Reynolds dependence near the wall; $S^{\ast }$, as well as $-\langle Q\rangle =\langle s_{ij}s_{ji}\rangle -\langle \unicode[STIX]{x1D714}_{i}\unicode[STIX]{x1D714}_{i}/2\rangle$ are linked to the flow structures, and also the latter quantity presents a good scaling near the wall. It has been found that the maximum of turbulent kinetic energy production $P_{k}$ occurs in the layer with $-\langle Q\rangle \approx 0$, that is, where the unstable sheet-like structures roll-up to become rods. The decomposition of $P_{k}$ in the contribution of elongational and compressive strain demonstrates that the two contributions present a good scaling. However, the good scaling holds when the wall and the outer structures are separated. The same statistics have been evaluated by direct simulations of turbulent flows in the presence of different types of corrugations on both walls. The flow physics in the layer near the plane of the crests is strongly linked to the shape of the surface and it has been demonstrated that the $u_{2}$ (normal to the wall) fluctuations are responsible for the modification of the flow structures, for the increase of the resistance and of the turbulent kinetic energy production.

scholarly journals Discussion on improved method of turbulence model for supercritical water flow and heat transfer

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 2729-2741
Author(s):  
Zhenchuan Wang ◽  
Guoli Qi ◽  
Meijun Li
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Turbulence Model ◽  
Buoyancy Effect ◽  
Improved Method ◽  
Production Term ◽  
Flow And Heat Transfer ◽  
Effect Model

The turbulence model fails in supercritical fluid-flow and heat transfer simulation, owing to the drastic change of thermal properties. The inappropriate buoyancy effect model and the improper turbulent Prandtl number model are several of these factors lead to the original low-Reynolds number turbulence model unable to predict the wall temperature for vertically heated tubes under the deteriorate heat transfer conditions. This paper proposed a simplified improved method to modify the turbulence model, using the generalized gradient diffusion hypothesis approximation model for the production term of the turbulent kinetic energy due to the buoyancy effect, using a turbulence Prandtl number model for the turbulent thermal diffusivity instead of the constant number. A better agreement was accomplished by the improved turbulence model compared with the experimental data. The main reason for the over-predicted wall temperature by the original turbulence model is the misuse of the buoyancy effect model. In the improved model, the production term of the turbulent kinetic energy is much higher than the results calculated by the original turbulence model, especially in the boundary-layer. A more accurate model for the production term of the turbulent kinetic energy is the main direction of further modification for the low Reynolds number turbulence model.

Evaluation of Turbulent Kinetic Energy Dissipation Rate in a Free Jet Flow from PIV Measurements

2012 ◽  
Vol 7 (1) ◽  
pp. 53-69
Author(s):  
Vladimir Dulin ◽  
Yuriy Kozorezov ◽  
Dmitriy Markovich
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Dissipation Rate ◽  
Energy Dissipation Rate ◽  
Turbulent Velocity ◽  
Small Scale ◽  
Velocity Fluctuations ◽  
Piv Measurements ◽  
Free Jet

The present paper reports PIV (Particle Image Velocimetry) measurements of turbulent velocity fluctuations statistics in development region of an axisymmetric free jet (Re = 28 000). To minimize measurement uncertainty, adaptive calibration, image processing and data post-processing algorithms were utilized. On the basis of theoretical analysis and direct measurements, the paper discusses effect of PIV spatial resolution on measured statistical characteristics of turbulent fluctuations. Underestimation of the second-order moments of velocity derivatives and of the turbulent kinetic energy dissipation rate due to a finite size of PIV interrogation area and finite thickness of laser sheet was analyzed from model spectra of turbulent velocity fluctuations. The results are in a good agreement with the measured experimental data. The paper also describes performance of possible ways to account for unresolved small-scale velocity fluctuations in PIV measurements of the dissipation rate. In particular, a turbulent viscosity model can be efficiently used to account for the unresolved pulsations in a free turbulent flow

Grid Requirements for Multiscale Resolution in Separated Unsteady High Speed Turbulent Flows

2006 ◽  
Author(s):  
D. Basu ◽  
A. Hamed ◽  
K. Das
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Turbulent Flows ◽  
Sound Pressure Level ◽  
High Speed ◽  
Sound Pressure ◽  
Pressure Fluctuations ◽  
Pressure Level ◽  
Navier Stokes ◽  
Detached Eddy Simulation

This study deals with the computational grid requirements in multiscale simulations of separated turbulent flows at high Reynolds number. The two-equation k-ε based DES (Detached Eddy Simulation) model is implemented in a full 3-D Navier-Stokes solver and numerical results are presented for transonic flow solution over an open cavity. Results for the vorticity, pressure fluctuations, SPL (Sound Pressure level) spectra and for modeled and resolved TKE (Turbulent Kinetic Energy) are presented and compared with available experimental data and with LES results. The results indicate that grid resolution significantly influences the resolved scales and the peak amplitude of the unsteady sound pressure level (SPL) and turbulent kinetic energy spectra.

Turbulent flow normal to a triangular cylinder

1997 ◽  
Vol 331 ◽  
pp. 107-125 ◽  
Author(s):  
D. K. HEIST ◽  
F. C. GOULDIN
Keyword(s):  
Energy Balance ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
High Speed ◽  
Turbulent Transport ◽  
Low Frequency ◽  
Recirculation Region ◽  
Constant Density ◽  
Energy Balances

Laser Doppler Velocimetry (LDV) measurements are presented for a nominally two-dimensional constant-density flow over a surface-mounted triangular cylinder. The thickness of the boundary layer approaching the triangular cylinder is much less than the height of the triangle. Momentum and turbulent kinetic energy balances are presented and comparisons are made with other separated and reattaching flows. Also, time domain information is presented in the form of autocorrelations and spectra. From the energy balances, the importance of the pressure transport term at the high-speed edge of the shear layer is seen. Observations of the relationships between the shapes of the spectra and the details of the energy balance are made. For example, the slope of the velocity spectra varies from the free-stream value of −5/3 to a value of −1 in the middle of the recirculation region. Concurrent with this increase in slope is a decrease in the role of shear production in the turbulent kinetic energy balance and an increase in the role of advection and turbulent transport. From the two-component LDV measurements, a very low-frequency unsteadiness is shown to contribute energy preferentially to different components of the velocity fluctuations depending on the location in the flow.

NUMERICAL RESEARCH ON THE EFFECT OF FIBERS ON THE PROPERTY OF TURBULENT FIBER SUSPENSION IN A CHANNEL

2009 ◽  
Vol 23 (03) ◽  
pp. 509-512 ◽  
Author(s):  
SUHUA SHEN ◽  
JIANZHONG LIN
Keyword(s):  
Reynolds Number ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Fiber Suspension ◽  
Additional Stress ◽  
Mean Velocity ◽  
Navier Stokes Equation ◽  
Fiber Concentration

To explore the rheological property in turbulent channel flow of fiber suspensions, the equation of probability distribution function for mean fiber orientation and the Reynolds averaged Navier-Stokes equation with the term of additional stress resulted from fibers were solved with numerical methods to get the distributions of the mean velocity and turbulent kinetic energy. The simulation results show that the effect of fibers on turbulent channel flow is equivalent to an additional viscosity. The turbulent velocity profiles of fiber suspension become gradually sharper by increasing the fiber concentration and/or decreasing the Reynolds number. The turbulent kinetic energy will increase with increasing Reynolds number and fiber concentration.

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