Fluid velocity profile development for turbulent flow in smooth annuli

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.

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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

Chemical Engineering and Processing - Process Intensification ◽

10.1016/j.cep.2007.12.008 ◽

2008 ◽

Vol 47 (7) ◽

pp. 1106-1117 ◽

Cited By ~ 40

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

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Turbulent Flow in Axially Rotating Pipes

Journal of Fluids Engineering ◽

10.1115/1.3240633 ◽

1980 ◽

Vol 102 (1) ◽

pp. 97-103 ◽

Cited By ~ 54

Author(s):

Mitsukiyo Murakami ◽

Kouji Kikuyama

Keyword(s):

Velocity Profile ◽

Turbulent Flow ◽

Flow Pattern ◽

Axial Velocity ◽

Hydraulic Loss ◽

Pressure Distributions ◽

Axial Velocity Profile ◽

Fully Developed Turbulent Flow ◽

Smooth Pipe ◽

Pipe Rotation

Experimental results concerning the flow pattern and hydraulic resistance in a rotating pipe are described. A fully developed turbulent flow was introduced into a long smooth pipe rotating about its axis, and changes of the flow pattern, together with hydraulic loss within the pipe, were examined by measuring the velocity and pressure distributions across sections at various distance from the pipe entrance. Increase of pipe rotation continuously reduces the hydraulic loss and gradually changes the axial velocity profile from a turbulent type to a laminar one. Governing factors for these changes are discussed.

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An Improved Step-Type Liquid Level Sensing System for Bridge Structural Dynamic Deflection Monitoring

Sensors ◽

10.3390/s19092155 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2155 ◽

Cited By ~ 2

Author(s):

Xijun Ye ◽

Zhuo Sun ◽

Xu Cai ◽

Liu Mei

Keyword(s):

Measurement Error ◽

Turbulent Flow ◽

Measurement Accuracy ◽

Fluid Velocity ◽

Step Height ◽

Liquid Level ◽

Measurement Point ◽

Structural Dynamic ◽

Sensing System ◽

Step Type

Real-time and accurate monitoring of dynamic deflection is of great significance for health monitoring and condition assessment of bridge structures. This paper proposes an improved step-type liquid level sensing system (LLSS) for dynamic deflection monitoring. Layout of straight-line-type pipeline is replaced by step-type pipeline in this improved deflection monitoring system, which can remove the interference of the inclination angle on the measurement accuracy and is applicable for dynamic deflection monitoring. Fluid dynamics are first analyzed to demonstrate that measurement accuracy is interfered with by the fluid velocity induced by structural vibration, and ANSYS-FLOTRAN is applied for analyzing the influence range caused by the turbulent flow. Finally, a step-type LLSS model is designed and experimented with to verify the influence of the three key parameters (initial displacement excitation, step height, and distance from the measurement point to the elbow) on the measurement accuracy, and the reasonable placement scheme for the measurement point is determined. The results show that the measurement accuracy mainly depends on the turbulent flow caused by step height. The measurement error gets smaller after about 1.0 m distance from the elbow. To ensure that the measurement error is less than 6%, the distance between the measurement point and the elbow should be larger than 1.0 m.

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

Mining Metallurgy & Exploration ◽

10.1007/s42461-019-00133-4 ◽

2019 ◽

Vol 37 (1) ◽

pp. 321-331 ◽

Cited By ~ 1

Author(s):

Ranjeet Kumar Singh ◽

Raj Kishore ◽

Kisor Kumar Sahu ◽

Ganesh Chalavadi ◽

Ratnakar Singh

Keyword(s):

Velocity Profile ◽

Fluid Velocity

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Solution of the problem of convective heat exchange during the turbulent flow of a liquid in a plane-parallel channel using the pai velocity profile

Journal of Engineering Physics ◽

10.1007/bf00861725 ◽

1975 ◽

Vol 29 (2) ◽

pp. 999-1003

Author(s):

I. T. Ivanov ◽

V. K. Orlov

Keyword(s):

Heat Exchange ◽

Velocity Profile ◽

Turbulent Flow ◽

Convective Heat ◽

Convective Heat Exchange ◽

Parallel Channel ◽

Plane Parallel

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Turbulent Flow Over a Facing Step at Several Reynolds Numbers

Volume 6: Fluids and Thermal Systems; Advances for Process Industries, Parts A and B ◽

10.1115/imece2011-64462 ◽

2011 ◽

Author(s):

Jose A. Jimenez-Bernal ◽

Adan Juarez-Montalvo ◽

Claudia del C. Gutierrez-Torres ◽

Juan G. Barbosa Saldan˜a ◽

Luis F. Rodriguez-Jimenez

Keyword(s):

Shear Stress ◽

Velocity Profile ◽

Turbulent Flow ◽

Reynolds Numbers ◽

Spatial Average ◽

Vortex Center ◽

Flow Conditions ◽

Flow Measurements ◽

Image Velocimetry ◽

Average Velocity Profile

An experimental study was performed over forward facing step (FFS). It was located within a transparent rectangular acrylic channel (1.4 m in length, 0.1 m in width and 0.02 m in height). The step is 0.01 m in height and 0.1 m in width, and was located 0.7 m downstream (fully developed region); a spanwise aspect ratio, w/h = 10 was used. The experiments were carried out using particle image velocimetry (PIV), which is a non intrusive experimental technique. The experimental water flow conditions include three Reynolds numbers based on the step height, Reh = 1124, 1404 and 1685. These flow conditions correspond to turbulent flow. Measurements were carried out in two zones; zone A begins at x = 8 cm (measured from the step base), and zone B starts at x = 0, y = 0, the visualization region corresponds to an area of 22.76 mm × 16.89 mm. 100 instantaneous velocity fields were obtained for each Reh. A temporal and spatial average was performed to obtain a velocity profile in zone A; likewise, the corresponding turbulence intensity and shear stress distribution were evaluated. The average velocity profile was evaluated for each Reh. Regarding the vortex center location, it was observed that as Reh increases, the y-direction coordinate moves towards bottom of wall channel. For zone B, it was also observed a reduction of the shear stress as Reh increases.

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