CFD and Optimization Study of Frictional Pressure Drop Through Bends

Author(s): Suman Debnath, Anirban Banik, Tarun Kanti Bandyopadhyay*, Apu Kumar Saha.

Journal Name: Recent Patents on Biotechnology

Volume 13 , Issue 1 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: The non-Newtonian pseudoplastic liquid flow through different types of the bend is more complicated compared to the simple straight pipe as the bends are associated with various curve geometry. Bends have wide application in bioengineering, biotechnology and biomedical such as study biofluids, blood rheology study, the design of medical equipment like equipment measuring the cholesterol etc.

Method: The papers deal with the estimation of loss coefficient and frictional pressure drop of Newtonian and non-Newtonian pseudoplastic fluid flow through the different bend of 0.0127 m diameter pipe geometry using commercially available CFD software fluent 6.3. We revised all patents relating to the pipe flow through different types of bend. The present study also deals with the efficient application of Genetic Algorithm (GA) for optimization of frictional pressure drop. Laminar Non-Newtonian Power law model is used for Sodium Carboxy Methyl Cellulose (SCMC) solution to solve the continuity and the momentum equations numerically. Generalized input-output correlation has been developed by Gene Expression Programming (GEP) using Matlab.

Results: The above-mentioned algorithm is used to predict and optimize the pressure drop. It has been found that, the process exhibit the minimum pressure drop across the bend under optimum condition (Angle = 133.160, Concentration = 0.2 Kg/m3 and velocity = 0.53 m/s). The effect of flow rate, bend angle, fluid behaviour on static pressure and pressure drop has also been investigated.

Conclusion: From the study, it can be concluded that the developed GA model has a good agreement with the CFD model. The software predicted data might be used to solve various industrial problems and also to design different equipment.

Keywords: Bends, biotechnology, CFD, non-newtonian pseudo plastic fluid, flow structure, genetic algorithm.

[1]
WR Dean M. Fluid motion in a curved channel. Proc R Soc Lond, A 1928; 121(787): 402-20.
[2]
Dean W. LXXII. The stream-line motion of fluid in a curved pipe (Second paper). J Sci 1928; 5(30): 673-95.
[3]
Berger S, Talbot L, Yao L. Flow in curved pipes. Annu Rev Fluid Mech 1983; 15(1): 461-512.
[4]
Soh WY, Berger S. Laminar entrance flow in a curved pipe. J Fluid Mech 1984; 148: 109-35.
[5]
Das S, Biswas M, Mitra A. Friction factor for gas‐non‐newtonian liquid flow in horizontal bends. Can J Chem Eng 1991; 69(1): 179-87.
[6]
Mashelkar R, Devarajan G. Secondary flows of non-Newtonian fluids: Part I–laminar boundary layer flow of a generalized non-Newtonian fluid in a coiled tube. Trans Inst Chem Eng 1976; 54(2): 100-7.
[7]
Mishra P, Gupta S. Momentum transfer in curved pipes. 1. Newtonian fluids. Ind Eng Chem Process Des Dev 1979; 18(1): 130-7.
[8]
Edwards M, Jadallah M, Smith R. Head losses in pipe fittings at low Reynolds numbers. Chem Eng Res Des 1985; 63(1): 43-50.
[9]
Bandyopadhyay TK, Das SK. Non-Newtonian pseudoplastic liquid flow through small diameter piping components. J Petrol Sci Eng 2007; 55(1-2): 156-66.
[10]
Bandyopadhyay T, Das S. Non-Newtonian and gas-non-newtonian liquid flow through elbows-CFD analysis. J Appl Mech 2013; 6(1): 131-41.
[11]
Sherwin S, Shah O, Doorly D, et al. The influence of out-of-plane geometry on the flow within a distal end-to-side anastomosis. J Biomech Eng 2000; 122(1): 86-95.
[12]
Ferreira C. Algorithm for solving gene expression programming: a new adaptive problems. Comput Syst 2001; 13(2): 87-129.
[13]
Dey P, Sarkar A, Das AK. Prediction of unsteady mixed convection over circular cylinder in the presence of nanofluid-A comparative study of ANN and GEP. J Nav Arch Mar Eng 2015; 12(1): 57-71.
[14]
Dey P, Sarkar A, Das AK. Development of GEP and ANN model to predict the unsteady forced convection over a cylinder. Neural Comput Appl 2016; 27(8): 2537-49.
[15]
Dey P, Sarkar A, Das AK. Capability to predict the steady and unsteady reduced aerodynamic forces on a square cylinder by ANN and GEP. Neural Comput Appl 2017; 28(8): 1933-45.
[16]
Dey P, Das AK. A utilization of GEP (Gene Expression Programming) metamodel and PSO (Particle Swarm Optimization) tool to predict and optimize the forced convection around a cylinder. Energy 2016; 95: 447-58.
[17]
Dey P, Das AK. Prediction and optimization of unsteady forced convection around a rounded cornered square cylinder in the range of Re. Neural Comput Appl 2017; 28(6): 1503-13.
[18]
Azarkish H, Sarvari S, Behzadmehr A. Optimum design of a longitudinal fin array with convection and radiation heat transfer using a genetic algorithm. Int J Therm Sci 2010; 49(11): 2222-9.
[19]
Copiello D, Fabbri G. Multi-objective genetic optimization of the heat transfer from longitudinal wavy fins. Int J Heat Mass Transfer 2009; 52(5-6): 1167-76.
[20]
Fabbri G. A genetic algorithm for fin profile optimization. Int J Heat Mass Transfer 1997; 40(9): 2165-72.
[21]
Hajabdollahi F, Rafsanjani HH, Hajabdollahi Z, Hamidi Y. Multi-objective optimization of pin fin to determine the optimal fin geometry using genetic algorithm. Appl Math Model 2012; 36(1): 244-54.
[22]
Mishra M, Das P. Thermoeconomic design-optimisation of crossflow plate-fin heat exchanger using Genetic Algorithm. Int J Exergy 2009; 6(6): 837-52.
[23]
Arabpour A, Karimipour A, Toghraie D, Akbari OA. Investigation into the effects of slip boundary condition on nanofluid flow in a double-layer microchannel. J Therm Anal Calorim 2018; 131(3): 2975-91.
[24]
Sajadifar SA, Karimipour A, Toghraie D. Fluid flow and heat transfer of non-Newtonian nanofluid in a microtube considering slip velocity and temperature jump boundary conditions. Eur J Mech BFluids 2017; 61: 25-32.
[25]
Afrand M, Toghraie D, Karimipour A, Wongwises S. A numerical study of natural convection in a vertical annulus filled with gallium in the presence of magnetic field. J Magn Magn Mater 2017; 430: 22-8.
[26]
Aghanajafi A, Toghraie D, Mehmandoust B. Numerical simulation of laminar forced convection of water-CuO nanofluid inside a triangular duct. Physica E Low Dimens Syst Nanostruct 2017; 85: 103-8.
[27]
Akbari OA, Afrouzi HH, Marzban A, Toghraie D, Malekzade H, Arabpour A. Investigation of volume fraction of nanoparticles effect and aspect ratio of the twisted tape in the tube. J Therm Anal Calorim 2017; 129(3): 1911-22.
[28]
Akbari OA, Toghraie D, Karimipour A, Marzban A, Ahmadi GR. The effect of velocity and dimension of solid nanoparticles on heat transfer in non-Newtonian nanofluid. Physica E Low Dimens Syst Nanostruct 2017; 86: 68-75.
[29]
Alipour H, Karimipour A, Safaei MR, Semiromi DT, Akbari OA. Influence of T-semi attached rib on turbulent flow and heat transfer parameters of a silver-water nanofluid with different volume fractions in a three-dimensional trapezoidal microchannel. Physica E Low Dimens Syst Nanostruct 2017; 88: 60-76.
[30]
Arabpour A, Karimipour A, Toghraie D. The study of heat transfer and laminar flow of kerosene/multi-walled carbon nanotubes (MWCNTs) nanofluid in the microchannel heat sink with slip boundary condition. J Therm Anal Calorim 2018; 131(2): 1553-66.
[31]
Esfe MH, Hajmohammad H, Toghraie D, Rostamian H, Mahian O, Wongwises S. Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems. Energy 2017; 137: 160-71.
[32]
Gravndyan Q, Akbari OA, Toghraie D, et al. The effect of aspect ratios of rib on the heat transfer and laminar water/TiO2 nanofluid flow in a two-dimensional rectangular microchannel. J Mol Liq 2017; 236: 254-65.
[33]
Hosseinnezhad R, Akbari OA, Afrouzi HH, Biglarian M, Koveiti A, Toghraie D. Numerical study of turbulent nanofluid heat transfer in a tubular heat exchanger with twin twisted-tape inserts. J Therm Anal Calorim 2018; 132(1): 741-59.
[34]
Mashayekhi R, Khodabandeh E, Bahiraei M, Bahrami L, Toghraie D, Akbari OA. Application of a novel conical strip insert to improve the efficacy of water–Ag nanofluid for utilization in thermal systems. a two-phase simulation. Energy Convers Manage 2017; 151: 573-86.
[35]
Nazari S, Toghraie D. Numerical simulation of heat transfer and fluid flow of Water-CuO Nanofluid in a sinusoidal channel with a porous medium. Physica E Low Dimens Syst Nanostruct 2017; 87: 134-40.
[36]
Rezaei O, Akbari OA, Marzban A, Toghraie D, Pourfattah F, Mashayekhi R. The numerical investigation of heat transfer and pressure drop of turbulent flow in a triangular microchannel. Physica E Low Dimens Syst Nanostruct 2017; 93: 179-89.
[37]
Shamsi MR, Akbari OA, Marzban A, Toghraie D, Mashayekhi R. Increasing heat transfer of non-Newtonian nanofluid in rectangular microchannel with triangular ribs. Physica E Low Dimens Syst Nanostruct 2017; 93: 167-78.
[38]
Toghraie D. Numerical thermal analysis of water’s boiling heat transfer based on a turbulent jet impingement on heated surface. Physica E Low Dimens Syst Nanostruct 2016; 84: 454-65.
[39]
Fonte TA, Taylor CA, Kim HJ, Sophie K. Method and system for modeling blood flow with boundary conditions for optimized diagnostic performance. US14/447,195, 2015.
[40]
Taylor CA. Method and system for quantifying limitations in coronary artery blood flow during physical activity in patients with coronary artery disease. US9,668,700, 2017.


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 13
ISSUE: 1
Year: 2019
Page: [74 - 86]
Pages: 13
DOI: 10.2174/1872208312666180820153706
Price: $58

Article Metrics

PDF: 25
HTML: 2
EPUB: 1
PRC: 1