scholarly journals 3J-2 Access of Asymmetric Fluid Velocity Profile in High Precision Ultrasonic Velocimetry

2006 ◽  
Author(s):  
F. Patat ◽  
E. Mandard ◽  
D. Kouame ◽  
R. Battault ◽  
J.-P. Remenieras
Keyword(s):  
Velocity Profile ◽  
High Precision ◽  
Fluid Velocity

Methodology for developing a high-precision ultrasound flow meter and fluid velocity profile reconstruction

2008 ◽  
Vol 55 (1) ◽  
pp. 161-172 ◽  
Author(s):  
Emmanuelle Mandard ◽  
Denis Kouame ◽  
Rodolphe Battault ◽  
Jean-Pierre Remenieras ◽  
Frederic Patat
Keyword(s):  
Velocity Profile ◽  
High Precision ◽  
Fluid Velocity ◽  
Flow Meter ◽  
Profile Reconstruction

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Mapping of protein fouling by FTIR-ATR as experimental tool to study membrane fouling and fluid velocity profile in various geometries and validation by CFD simulation

2008 ◽  
Vol 47 (7) ◽  
pp. 1106-1117 ◽  
Author(s):  
David Delaunay ◽  
Murielle Rabiller-Baudry ◽  
José M. Gozálvez-Zafrilla ◽  
Béatrice Balannec ◽  
Matthieu Frappart ◽  
...  
Keyword(s):  
Velocity Profile ◽  
Membrane Fouling ◽  
Cfd Simulation ◽  
Fluid Velocity ◽  
Protein Fouling ◽  
Experimental Tool

Estimation of the Fluid Velocity Profile in the Stratification Zone of a Falcon Concentrator

2019 ◽  
Vol 37 (1) ◽  
pp. 321-331 ◽  
Author(s):  
Ranjeet Kumar Singh ◽  
Raj Kishore ◽  
Kisor Kumar Sahu ◽  
Ganesh Chalavadi ◽  
Ratnakar Singh
Keyword(s):  
Velocity Profile ◽  
Fluid Velocity

Measurement of the fluid-velocity profile using a self-mixing superluminescent diode

2011 ◽  
Vol 22 (2) ◽  
pp. 025402 ◽  
Author(s):  
Luigi Rovati ◽  
Stefano Cattini ◽  
Nithiyanantham Palanisamy
Keyword(s):  
Velocity Profile ◽  
Fluid Velocity ◽  
Superluminescent Diode

scholarly journals Fluid velocity profile reconstruction for non-Newtonian shear dispersive flow

2001 ◽  
Vol 5 (2) ◽  
pp. 87-104 ◽  
Author(s):  
Paul R. Shorten ◽  
David J. N. Wall
Keyword(s):  
Inverse Problem ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Average Concentration ◽  
Two Dimensional ◽  
Cross Sectional ◽  
Dispersive Flow ◽  
Ill Posed ◽  
Profile Reconstruction ◽  
Well Posed

An inverse problem associated with the mass transport of a material concentration down a pipe where the flowing non-Newtonian medium has a two-dimensional velocity profile is examined. The problem of determining the two-dimensional fluid velocity profile from temporally varying cross-sectional average concentration measurements at upstream and downstream locations is considered. The special case of a known input upstream concentration with a time zero step, and a strictly decreasing velocity profile is shown to be a well-posed problem. This inverse problem is in general ill-posed and mollification is used to obtain a well conditioned problem.

After transition in a soft-walled microchannel

2015 ◽  
Vol 780 ◽  
pp. 649-686 ◽  
Author(s):  
S. S. Srinivas ◽  
V. Kumaran
Keyword(s):  
Velocity Profile ◽  
Root Mean Square ◽  
Reynolds Stress ◽  
Fluid Velocity ◽  
Maximum Velocity ◽  
Reynolds Numbers ◽  
Dynamical Instability ◽  
Mean Square ◽  
Velocity Fluctuations ◽  
Soft Wall

In comparison to the flow in a rigid channel, there is a multifold reduction in the transition Reynolds number for the flow in a microchannel when one of the walls is made sufficiently soft, due to a dynamical instability induced by the fluid–wall coupling, as shown by Verma & Kumaran (J. Fluid Mech., vol. 727, 2013, pp. 407–455). The flow after transition is characterised using particle image velocimetry in the $x{-}y$ plane, where $x$ is the streamwise direction and $y$ is the cross-stream coordinate along the small dimension of the channel of height 0.2–0.3 mm. The flow after transition is characterised by a mean velocity profile that is flatter at the centre and steeper at the walls in comparison to that for a laminar flow. The root mean square of the streamwise fluctuating velocity shows a characteristic sharp increase away from the wall and a maximum close to the wall, as observed in turbulent flows in rigid-walled channels. However, the profile is asymmetric, with a significantly higher maximum close to the soft wall in comparison to that close to the hard wall, and the Reynolds stress is found to be non-zero at the soft wall, indicating that there is a stress exerted by fluid velocity fluctuations on the wall. The maximum of the root mean square of the velocity fluctuations and the Reynolds stress (divided by the fluid density) in the soft-walled microchannel for Reynolds numbers in the range 250–400, when scaled by suitable powers of the maximum velocity, are comparable to those in a rigid channel at Reynolds numbers in the range 5000–20 000. The near-wall velocity profile shows no evidence of a viscous sublayer for $(yv_{\ast }/{\it\nu})$ as low as two, but there is a logarithmic layer for $(yv_{\ast }/{\it\nu})$ up to approximately 30, where the von Karman constants are very different from those for a rigid-walled channel. Here, $v_{\ast }$ is the friction velocity, ${\it\nu}$ is the kinematic viscosity and $y$ is the distance from the soft surface. The surface of the soft wall in contact with the fluid is marked with dye spots to monitor the deformation and motion along the fluid–wall interface. Low-frequency oscillations in the displacement of the surface are observed after transition in both the streamwise and spanwise directions, indicating that the velocity fluctuations are dynamically coupled to motion in the solid.

scholarly journals Stability analysis and breakup length calculations for steady planar liquid jets

2010 ◽  
Vol 668 ◽  
pp. 384-411 ◽  
Author(s):  
M. R. TURNER ◽  
J. J. HEALEY ◽  
S. S. SAZHIN ◽  
R. PIAZZESI
Keyword(s):  
Stability Analysis ◽  
Velocity Profile ◽  
Shear Layer ◽  
Fluid Velocity ◽  
Finite Thickness ◽  
Liquid Jet ◽  
Absolute Instability ◽  
Breakup Length ◽  
The Stability ◽  
Thickness Shear

This study uses spatio-temporal stability analysis to investigate the convective and absolute instability properties of a steady unconfined planar liquid jet. The approach uses a piecewise linear velocity profile with a finite-thickness shear layer at the edge of the jet. This study investigates how properties such as the thickness of the shear layer and the value of the fluid velocity at the interface within the shear layer affect the stability properties of the jet. It is found that the presence of a finite-thickness shear layer can lead to an absolute instability for a range of density ratios, not seen when a simpler plug flow velocity profile is considered. It is also found that the inclusion of surface tension has a stabilizing effect on the convective instability but a destabilizing effect on the absolute instability. The stability results are used to obtain estimates for the breakup length of a planar liquid jet as the jet velocity varies. It is found that reducing the shear layer thickness within the jet causes the breakup length to decrease, while increasing the fluid velocity at the fluid interface within the shear layer causes the breakup length to increase. Combining these two effects into a profile, which evolves realistically with velocity, gives results in which the breakup length increases for small velocities and decreases for larger velocities. This behaviour agrees qualitatively with existing experiments on the breakup length of axisymmetric jets.

The Effect of Drillpipe Rotation on Pressure Losses and Fluid Velocity Profile in Foam Drilling

2008 ◽  
Author(s):  
Mingqin Duan ◽  
Stefan Z. Miska ◽  
Mengjiao Yu ◽  
Nicholas E. Takach ◽  
Ramadan Mohammed Ahmed ◽  
...  
Keyword(s):  
Velocity Profile ◽  
Fluid Velocity ◽  
Pressure Losses ◽  
Drillpipe Rotation ◽  
Foam Drilling
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