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 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.

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

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
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