Experimental Study 0f Drag Reduction Phenomena in the Horizontal Tube with Nano SiO2 by Neural Network - Genetic Algorithm

Document Type : Research Article


1 Department of Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, I.R. IRAN

2 Department of Chemical Engineering, South Tehran Branch, Islamic Azad University, Tehran, I.R. IRAN


In this study, nano-silica oxide's effect as a Drag Reducing Agent (DRA) of water flow in a 12.7 and 25.4 mm galvanized pipe was investigated. The studied parameters include Nano silica oxide concentration, Flow rate, temperature,  and tube pipe diameter. To develop the conditions in preparing the Nano-particle on Drag Reduction (DR), nano-particles were provided in the top water-based fluid. To have a comprehensive analysis of process folding conditions, the experiments were carried out with three different drag-reducing concentration agents with three various temperatures and three different flow rates. Moreover, as a new method in this study, the experimental (Drag reduction percent) outputs were evaluated and analyzed using the Artificial neural network which is optimized by a genetic algorithm. In the consequence of algorithm genetic, the highest rate of drag reduction occurred at a horizontal pipeline 12.7 mm, temperature 41.07 °C, and a concentration of 0.628 with a 1441.84 flow rate was 25.84%.


Main Subjects

[1] Toms B.A., On the Early Experiments on Drag Reduction by Polymers, Phys. Fluids., 20(10): 3-5 (1977).
[2] Gu W., Kawaguchi Y., Wang D., Akihiro S., Experimental Study of Turbulence Transport in a Dilute Surfactant Solution Flow Investigated by PIV,  J. Fluids Eng., 132(5): 051204 (2010).
[3] Kamel A.H., Shah S N., Maximum Drag Reduction Asymptote for Surfactant-Based Fluids in Circular Coiled Tubing, J. Fluids Eng., 135(3): 031201 (2013).
[4] Savins J.G., A Stress-Controlled Drag-Reduction PhenomenonRheol. Acta, 6(4):323-30 (1967).
[5] Abubakar A., Al-Hashmi A., Al-Wahaibi T., Al-Wahaibi Y., Al-Ajmi A., Eshrati M., Parameters of Drag Reducing Polymers and Drag Reduction Performance in Single-Phase Water Flow, J. Adv. Mech. Eng. Sci., 6: 202073 (2014).
[6] Karami H.R., Rahimi M., Ovaysi S., Degradation of Drag Reducing Polymers in Aqueous SolutionsKorean J. Chem. Eng., 35(1): 34-43 (2018).
[7] Volokh K., An Explanation of the Drag Reduction via Polymer SoluteActa Mechanica, 229(10): 4295-4301 (2018).
[8] Chai Y.,  Li X., Geng J., Pan J., Huang Y., Jing D., Mechanistic Study of Drag Reduction in Turbulent Pipeline Flow over Anionic Polymer and Surfactant Mixtures, Colloid Polym. Sci., 297(7-8): 1025-1035 (2019).
[9] Nalwa H., Nanostructured Materials and Nanotechnology (Concise ed.). Academic Press, San Diego (2002)
[10] Trisaksri V., Wongwises S., Critical Review of Heat Transfer Characteristics of Nanofluids, Renewable Sustainable Energy Rev., 11(3): 512-23 (2007).
[11] Keblinski P., Eastman J A., Cahill D G., Nanofluids for Thermal Transport, Mater. Today, 8(6): 36-44 (2005).
[12] Singh K., Sharma S., Gangacharyulu D., Experimental Study of Thermophysical Properties of Al2O3/Water Nanofluid,  Int. J. Mech. Eng., 3(2): 229-33 (2013).
[13] Cabaleiro D., Colla L., Agresti F., Lugo L., Fedele L., Transport Properties and Heat Transfer Coefficients of ZnO/(Ethylene Glycol+ Water) Nanofluids, Int. J. Heat Mass Transfer, 89:433-443 (2015).
[15] Mohebbi K., Rafee R., Talebi F., Effects of Rib Shapes on Heat Transfer Characteristics of Turbulent Flow of Al2O3-Water Nanofluid Inside Ribbed Tubes,  Iran. J. Chem. Chem. Eng. (IJCCE), 34(3): 61-77 (2015).
[16] Jafari A., Shahmohammadi A., Mousavi S M., CFD Investigation of Gravitational Sedimentation Effect on Heat Transfer of a Nano-Ferrofluid, Iran. J. Chem. Chem. Eng. (IJCCE), 34(1): 87-96 (2015).
[17] Liu R., Wei X., Tao D., Zhao Y., Study of Preparation and Tribological Properties of Rare Earth Nanoparticles in Lubricating Oil, Tribol. Int., 43(5-6): 1082-1086 (2010).
[18] Garbacz H., Grądzka-Dahlke M.,Kurzydłowski K J., The Tribological Properties of Nano-Titanium Obtained by Hydrostatic Extrusion, Wear, 263(1-6): 572-578 (2007).
[20] Li X., Cao Z., Zhang Z., Dang H., Surface-Modification in Situ of Nano-SiO2 and its Structure and Tribological Properties, Appl. Surf. Sci., 252(22): 7856-7861 (2006).
[21] Peng D., Kang Y., Hwang R., Shyr S., Chang Y., Tribological Properties of Diamond and SiO2 Nanoparticles Added in Paraffin, Tribol. Int., 42(6): 911-917 (2009).
[22] Drzazga M., Gierczycki A., Dzido G., Lemanowicz M., Influence of Nonionic Surfactant Addition on Drag Reduction of Water Based Nanofluid in a Small Diameter Pipe, Chin. J. Chem. Eng., 21(1): 104-108 (2013).
[23] Fotukian S., Esfahany M.N., Experimental Study of Turbulent Convective Heat Transfer and Pressure Drop of Dilute Cuo/Water Nanofluid Inside a Circular Tube, Int. Commun. Heat Mass Transfer, 37(2): 214-219 (2010).
[24] Tao X.,  Jiazheng Z., Kang X., The Ball-Bearing Effect of Diamond Nanoparticles as an Oil Additive, J. Phys. D: Appl. Phys., 29(11): 2932 (1996).
[25] Hu Z S., Lai R., Lou F., Wang L., Chen Z., Chen G., Dong J., Preparation and Tribological Properties of Nanometer Magnesium Borate as Lubricating Oil Additive, Wear, 252(5-6): 370-374 (2002).
[26] Phuoc T.X., Massoudi M., Chen R-H., Viscosity and Thermal Conductivity of Nanofluids Containing Multi-Walled Carbon Nanotubes Stabilized by Chitosan,  Int. J. Therm. Sci., 50(1): 12-18 (2011).
[27] Zulkifli N., Kalam M., Masjuki H., Yunus R., Experimental Analysis of Tribological Properties of Biolubricant with Nanoparticle Additive, Procedia Eng., 68:152-157 (2013).
[28] Pouranfard A., Mowla D., Esmaeilzadeh F., An Experimental Study of Drag Reduction by Nanofluids Through Horizontal Pipe Turbulent Flow of a Newtonian Liquid,  J. Ind. Eng. Chem., 20(2): 633-637 (2014).
[29] Pouranfard A., Mowla D., Esmaeilzadeh F., An Experimental Study of Drag Reduction by Nanofluidsin Slug Two-Phase Flow of Air and Water Through Horizontal Pipes, Chin. J. Chem. Eng., 23(3):471-475 (2015).
[30] Gierczycki A., Drzazga M., Lemanowicz M., Dzido G., Drag Reduction in the Flow of Cuo Based Nanofluid, Inżynieria i Aparatura Chemiczna, (1): 8-9 (2015).
[32] Chen Z., Liu Y., Luo J., Superlubricity of Nanodiamonds Glycerol Colloidal Solution Between Steel Surfaces, Colloids Surf., A, 489:400-406 (2016).
[33] Gulzar M., Masjuki H., Kalam M., Varman M., Zulkifli N., Mufti R., Zahid R., Yunus R., Dispersion Stability and Tribological Characteristics of TiO2/SiO2 Nanocomposite-Enriched Biobased Lubricant,  Tribol. Trans., 60(4): 670-680 (2017).
[34] Zhang Y., Wei L., Hu H., Zhao Z., Huang Z., Huang A., Shen F., Liang J., Qin Y., Tribological Properties of Nano Cellulose Fatty Acid Esters as Ecofriendly and Effective Lubricant Additives, Cellulose, 25(5):3091-103 (2018).
[35] Ren X., Yang L., Li C., Cheng G., Liu N, editors., Design and Analysis of Underwater Drag Reduction Property of Biomimetic Surface with Micro-nano Composite Structure, International Conference on Mechanical Design; Springer (2019).
[36] Virk P S., Drag Reduction FundamentalsAIChE J., 21(4):625-56 (1975).
[37] Zadrazil I., Bismarck A., Hewitt G., Markides C., Shear Layers in the Turbulent Pipe Flow of Drag Reducing Polymer Solutions, Chem. Eng. Sci., 72:142-54 (2012).
[38] Hadi N., Niaei A., Nabavi S.R., Alizadeh R., Shirazi M.N., Izadkhah B., An Intelligent Approach to Design and Optimization of M-Mn/H-ZSM-5 (M: Ce, Cr, Fe, Ni) Catalysts In Conversion of Methanol To Propylene, J. Taiwan Inst. Chem. Eng., 59: 173-185 (2016).
[39] Ehsani M.R., Bateni H., Parchikolaei G.R., Modeling the Oxidative Coupling of Methane Using Artificial Neural Network and Optimizing of its Operational Conditions Using Genetic Algorithm,  Korean J. Chem. Eng., 29(7): 855-861 (2012).
[40] Nourbakhsh H., Emam-Djomeh Z., Omid M., Mirsaeedghazi H., Moini S., Prediction of Red Plum Juice Permeate Flux During Membrane Processing With ANN Optimized Using RSM,  Comput Electron Agric, 102:1-9 (2014).
[41] Izadkhah B., Nabavi S., Niaei A., Salari D., Badiki T.M., Çaylak N., Design and Optimization of Bi-Metallic Ag-ZSM5 Catalysts for Catalytic Oxidation of Volatile Organic Compounds, J. Ind. Eng. Chem., 18(6):2083-2091 (2012).
[43] Tarjomannejad A., Prediction of the Liquid Vapor Pressure Using the Artificial Neural Network-Group Contribution Method, Iran. J. Chem. Chem. Eng. (IJCCE), 34(4): 97-111 (2015).
[44] Soleimanzadeh H., Niaei A., Salari D., Tarjomannejad A., Penner S., Grünbacher M., Hosseini S.A., Mousavi S.M., Modeling and optimization of V2O5/TiO2 Nanocatalysts for NH3-Selective Catalytic Reduction (SCR) of NOx by RSM and ANN Techniques, J. Environ. Manage., 238: 360-367 (2019).