scholarly journals Do Heterogeneous Sediment Properties and Turbulent Velocity Fluctuations Have Something in Common? Some History and a New Stochastic Process

2013 ◽  
pp. 13-22 ◽  
Author(s):  
Fred J. Molz ◽  
Mark M. Meerschaert ◽  
Tom J. Kozubowski ◽  
Paul D. Hyden
Keyword(s):  
Stochastic Process ◽  
Turbulent Velocity ◽  
Velocity Fluctuations ◽  
Sediment Properties

scholarly journals Fuzzy rules based model for solute dispersion in an open channel dead zone

2002 ◽  
Vol 4 (1) ◽  
pp. 39-51
Author(s):  
Helen Kettle ◽  
Keith Beven ◽  
Barry Hankin
Keyword(s):  
Finite Volume ◽  
Fuzzy Number ◽  
Open Channel ◽  
Transverse Velocity ◽  
Dead Zone ◽  
Fuzzy Rules ◽  
Turbulent Velocity ◽  
Velocity Fluctuations ◽  
Laboratory Flume ◽  
The Mean

A method has been developed to estimate turbulent dispersion based on fuzzy rules that use local transverse velocity shears to predict turbulent velocity fluctuations. Turbulence measurements of flow around a rectangular dead zone in an open channel laboratory flume were conducted using an acoustic Doppler velocimeter (ADV) probe. The mean velocity and turbulence characteristics in and around the shear zone were analysed for different flows and geometries. Relationships between the mean transverse velocity shear and the turbulent velocity fluctuations are encapsulated in a simple set of fuzzy rules. The rules are included in a steady-state hybrid finite-volume advection–diffusion scheme to simulate the mixing of hot water in an open-channel dead zone. The fuzzy rules produce a fuzzy number for the magnitude of the average velocity fluctuation at each cell boundary. These are then combined within the finite-volume model using the single-value simulation method to give a fuzzy number for the temperature in each cell. The results are compared with laboratory flume data and a computational fluid dynamics (CFD) simulation from PHOENICS. The fuzzy model compares favourably with the experiment data and offers an alternative to traditional CFD models.

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

Spectral Analysis and Discussion on the Velocity Fluctuation in Drag Reducing Channel Flow by Surfactant Additives

2016 ◽  
Author(s):  
Yuichi Kaiho ◽  
Shumpei Hara ◽  
Takahiro Tsukahara ◽  
Yasuo Kawaguchi
Keyword(s):  
Channel Flow ◽  
Time Scale ◽  
Velocity Fluctuation ◽  
Surfactant Solution ◽  
Turbulent Velocity ◽  
Laser Doppler Velocimeter ◽  
Wall Region ◽  
Solution Flow ◽  
Velocity Fluctuations ◽  
Turbulent Velocity Fluctuation

It is known as the Toms effect that the wall friction coefficient is reduced by adding a small amount of polymer or surfactant into a water flow. In the drag-reducing flow, it is expected that a time scale of turbulent velocity fluctuation is changed by relaxation time due to viscoelasticity. In the present study, experimental analysis of the turbulent velocity fluctuation was performed with temporal characteristics in surfactant solution flow. The velocity fluctuations were measured by using a two-component laser Doppler velocimeter system on turbulent channel flow. And then, we performed statistical operation on those data and examined the time scale. From spectra analysis, it was found that very low frequency velocity fluctuations existed near the wall region in the surfactant solution flow. It was also revealed that the strong anisotropy occurred not only with the intensity but also with frequency distribution in turbulent velocity fluctuations. Moreover, the turbulence contributes nothing to the Reynolds shear stress and behaves as a wave motion. It was concluded that the turbulent eddies and viscoelasticity were two factors contributing to turbulent generation in the viscoelastic turbulent flow, with each factor having its own time scale.

scholarly journals Brownian Motion and Quantum Mechanics

2019 ◽  
Vol 19 (02) ◽  
pp. 2050017
Author(s):  
Roumen Tsekov
Keyword(s):  
Quantum Mechanics ◽  
Brownian Motion ◽  
Turbulent Velocity ◽  
Simultaneous Action ◽  
Quantum Brownian Motion ◽  
Velocity Fluctuations ◽  
Diffusive Behavior ◽  
Stochastic Sources ◽  
Quantum Noises

A theoretical parallel between the classical Brownian motion and quantum mechanics is explored via two stochastic sources. It is shown that, in contrast to the classical Langevin force, quantum mechanics is driven by turbulent velocity fluctuations with diffusive behavior. In the case of simultaneous action of the thermal and quantum noises, the quantum Brownian motion is described as well.

Structure of the Mean Velocity and Turbulence in Premixed Axisymmetric Acetylene Flames

1994 ◽  
Vol 116 (3) ◽  
pp. 631-642 ◽  
Author(s):  
M. Matovic ◽  
S. Oka ◽  
F. Durst
Keyword(s):  
Flow Field ◽  
Probability Density ◽  
Turbulent Velocity ◽  
Mean Velocity ◽  
Velocity Fluctuations ◽  
Common Features ◽  
Density Distributions ◽  
The Mean ◽  
Probability Density Distributions ◽  
Insight Into

Laser-Doppler measurements of axial mean velocities and the corresponding rms values of turbulent velocity fluctuations are reported for premixed, axisymmetric, acetylene flames together with the probability density distributions of the turbulent velocity fluctuations. All this information provides an insight into the structure of the flow field. Characteristic zones of the flow field are defined that show common features for all acetylene flames studied by the authors. These features are discussed in the paper and are suggested to characterize, in general, interesting parts of the flames.

Measurements of turbulent velocity fluctuations in a planar Couette cell

1995 ◽  
Vol 7 (8) ◽  
pp. 1949-1955 ◽  
Author(s):  
Stein Malerud ◽  
Knut Jo/rgen Målo/y ◽  
Walter I. Goldburg
Keyword(s):  
Turbulent Velocity ◽  
Velocity Fluctuations ◽  
Couette Cell

Elements of a General Correlation for Turbulent Heat Transfer

1996 ◽  
Vol 118 (2) ◽  
pp. 287-293 ◽  
Author(s):  
P. K. Maciejewski ◽  
A. M. Anderson
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Temperature Rise ◽  
Adiabatic Temperature ◽  
Turbulent Velocity ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Velocity Fluctuations ◽  
Adiabatic Temperature Rise ◽  
General Correlation

Typically, heat transfer researchers present results in the form of an empirically based relationship between a length-based Nusselt number, a length-based Reynolds number, and a fluid Prandtl number. This approach has resulted in a multitude of heat transfer correlations, each tied to a specific geometry type. Two recent studies have contributed key ideas that support the development of a more general correlation for turbulent heat transfer that is based on local parameters. Maciejewski and Moffat (1992a, b) found that wall heat transfer rates scale with streamwise turbulent velocity fluctuations and Anderson and Moffat (1992a, b) found that the adiabatic temperature rise is the driving potential for heat transfer. Using these two concepts and a novel approach to dimensional analysis, the present authors have formulated a general correlation for turbulent heat transfer. This correlation predicts wall heat flux as a function of the turbulent velocity fluctuations, the adiabatic temperature rise, and the fluid properties (density, specific heat, thermal conductivity, and viscosity). The correlation applies to both internal and external flows and is tested in air, water, and FC77. The correlation predicts local values of surface heat flux to within ± 12.0 percent at 95 percent confidence.

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