Research Article
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Year 2021, , 951 - 969, 01.05.2021

Om Parkash Arvind Kumar Basant Sikarwar

https://doi.org/10.18186/thermal.930932
Cited By: 8

Abstract

References

  • [1] O’Brien, MP. Review of the theory of turbulent flow and its relations to sediment transportation. Transactions American Geophysical Union 1933; 14: 487-491. https://doi.org/10.1029/TR014i001p00487
  • [2] Rouse, H. Modern conceptions of the mechanics of fluid turbulence. Transactions ASCE, 1937; 102: 463-505.
  • [3] Ismail, HM. Turbulent transfer mechanism and suspended sediment in closed channels. Transactions ASCE. 1952; 117: 409-446
  • [4] Shook, CA. Daniel, S. M. Flow of suspensions of solids in pipeline: Flow with a stable stationary deposit. Can J Chem Eng, 1965; 43:56–72. https://doi.org/10.1002/cjce.5450430202.
  • [5] Shook, CA, Daniel, SM, Scott, JA, Holgate, JP. Flow of suspensions in pipelines. Can J Chem Eng 1968; 46: 238–244. https://doi.org/10.1002/cjce.5450460405
  • [6] Karabelas, AJ. Vertical distribution of dilute suspensions in turbulent pipe flow. AIChE J 1977; 23: 426–434. https://doi.org/10.1002/aic.690230404.
  • [7] Turian, RM, Hsu, FL, Selim, MS. Friction losses for flow of slurries in pipeline bends, fittings, and valves. Particul Sci Technol 1983; 1(4): 365-392. https://doi.org/10.1080/02726358308906383.
  • [8] Roco, MC, Shook, CA. Modeling of slurry flow: The effect of particle size. Can J Chem Eng 1983; 61:494–503. https://doi.org/10.1002/cjce.5450610402.
  • [9] Roco, MC, Shook, CA. Computational methods for coal slurry pipeline with heterogeneous size distribution. Powder Technol 1984; 39:159–176. https://doi.org/10.1016/0032-5910(84)85034-2.
  • [10] Colwell, JM, Shook, CA. The entry length for slurries in horizontal pipeline flow. Can J Chem Eng 1988; 66(5):714-720. https://doi.org/10.1002/cjce.5450660503.
  • [11] Gillies, RG, Shook, CA, Wilson, KC. An improved two-layer model for horizontal slurry pipeline flow. Can J Chem Eng 1991; 69: 173–178. https://doi.org/10.1002/cjce.5450690120.
  • [12] Gillies, RG, Hill KB, Mckibben, MJ, Shook, CA. Solids transport by laminar Newtonian flows. Powder Technol 1999; 104:269–277. https://doi.org/10.1016/S0032-5910(99)00104-7.
  • [13] Gillies, RG, Shook, CA. Modeling high concentration settling slurry flows. Can J Chem Eng 200; 78:709–716. https://doi.org/10.1002/cjce.5450780413.
  • [14] Matousek, V. Pressure drops and flow patterns in sand-mixture pipes. Exp Therm Fluid Sci 2002; 26(6):693-702. https://doi.org/10.1016/S0894-1777(02)00176-0.
  • [15] Ling, J, Skudarnov, PV, Lin, CX. Ebadian, M. A. Numerical investigations of liquid solid slurry flows in a fully developed turbulent flow region. International Journal of Heat and Fluid Flow 2003; 24:389-398. https://doi.org/10.1016/S0142-727X(03)00018-3.
  • [16] Krampa-Morlu, FN, Bergstrom, D.J, Bugg, JD, Sanders, RS, Schaan, J. Numerical simulation of dense coarse particle slurry flows in a vertical pipe. In 5th International Conference on Multiphase flow, ICMF 2004; 4:460.
  • [17] Kaushal, DR; Sato, K, Toyota, T, Funatsu, K, Tomita, Y. Effect of particle size distribution on pressure drop and concentration profile in pipeline flow of highly concentrated slurry. Int J Multiphas Flow 2005; 31: 809-823. https://doi.org/10.1016/j.ijmultiphaseflow.2005.03.003.
  • [18] Kaushal, DR, Tomita, Y. Experimental investigation for near-wall lift of coarser particles in slurry pipeline using γ-ray densitometer. Powder Technol 2007; 172(3):177-187. https://doi.org/10.1016/j.powtec.2006.11.020.
  • [19] Lin, CX. Ebadian, MA. A numerical study of developing slurry flow in the entrance region of a horizontal pipe. Comput Fluids 2008; 37(8):965-974. https://doi.org/10.1016/j.compfluid.2007.10.008.
  • [20] Lahiri, SK, Ghanta, KC. Prediction of pressure drop of slurry flow in pipeline by hybrid support vector regression and genetic algorithm model. Chin J Chem Eng 2008; 16(6): 841-848. https://doi.org/10.1016/S1004-9541(09)60003-3.
  • [21] Kumar, A, Kaushal, DR, Kumar, U. Bend pressure drop experiments compared with FLUENT. Proceedings of the Institution of Civil Engineers-Engineering and Computational Mechanics 2008; 161(1): 35-42. https://doi.org/10.1680/eacm.2008.161.1.35.
  • [22] Chandel, S, Seshadri, V, Singh, SN. Effect of additive on pressure drop and rheological characteristics of fly ash slurry at high concentration. Particul Sci Technol 2009; 27(3):271-284. https://doi.org/10.1080/02726350902922036.
  • [23] Monteiro, AC, Bansal, PK. Pressure drop characteristics and rheological modeling of ice slurry flow in pipes. Int J Refrig 2010; 33(8):1523-1532. https://doi.org/10.1016/j.ijrefrig.2010.09.009.
  • [24] Naik, HK, Mishra, MK, Rao, KU. Influence of chemical reagents on rheological properties of fly ash–water slurry at varying temperature environment. Coal Combustion and Gasification Products 2011; 3:83-93.
  • [25] Kaushal, DR, Thinglas, T, Tomita, Y, Kuchii, S, Tsukamoto, H. CFD modeling for pipeline flow of fine particles at high concentration. Int J Multiphas Flow 2012; 43: 85-100. https://doi.org/10.1016/j.ijmultiphaseflow.2012.03.005.
  • [26] Kaushal, DR, Kumar, A, Tomita, Y, Kuchii, S, Tsukamoto, H. Flow of mono-dispersed particles through horizontal bend. Int J Multiphas Flow 2013; 52: 71-91. https://doi.org/10.1016/j.ijmultiphaseflow.2012.12.009.
  • [27] Gopaliya, MK, Kaushal, DR. Modeling of sand-water slurry flow through horizontal pipe using CFD. J Hydrol Hydromech 2016; 64(3): 261-272.
  • [28] Melorie, AK, Kaushal, DR. Experimental Investigations of the Effect of Chemical Additives on the Rheological Properties of Highly Concentrated Iron Ore Slurries. KONA Powder Part J. 2017; 35:186-199. https://doi.org/10.14356/kona.2018001
  • [29] Assefa, KM, Kaushal DR. A new model for the viscosity of highly concentrated multi-sized particulate Bingham slurries. Particul Sci Technol 2017; 35(1):77-85. https://doi.org/10.1080/02726351.2015.1131789.
  • [30] Singh, JP, Kumar, S, Mohapatra, SK. Modelling of two-phase solid-liquid flow in horizontal pipe using computational fluid dynamics technique. Int J Hydrogen Energy 2017; 42(31): 20133-20137. https://doi.org/10.1016/j.ijhydene.2017.06.060.
  • [31] Singh, MK, Kumar, S, Ratha, D, Kaur, H. Design of slurry transportation pipeline for the flow of multi-particulate coal ash suspension. Int J Hydrogen Energy 2017; 42 (30):19135-19138.
  • [32] Parkash, O, Kumar, A, Sikarwar, BS, CFD Modeling of Commercial Slurry Flow through Horizontal Pipeline. In Advances in Interdisciplinary Engineering. Springer, Singapore 2019; 153-162. https://doi.org/10.1007/978-981-13-6577-5_16.
  • [33] Hoseinzadeh, S, Ghasemiasl, Havaei, D, Chamkha, AJ. Numerical investigation of rectangular thermal energy storage units with multiple phase change materials. J Mol Liq 2018; 271: 655-660. https://doi.org/10.1016/j.molliq.2018.08.128.
  • [34] Hoseinzadeh, S, Heyns, PS, Chamkha, AJ, Shirkhani, A. Thermal analysis of porous fins enclosure with the comparison of analytical and numerical methods. J Therm Anal Calorim 2019; 1-9.
  • [35] Hoseinzadeh, S, Moafi, A, Shirkhani, A, Chamkha, AJ. Numerical Validation Heat Transfer of Rectangular Cross-Section Porous Fins. Journal of Thermophysics and Heat Transfer 2019; 1-7. https://doi.org/10.2514/1.T5583
  • [36] Hoseinzadeh, S, Hadi Zakeri, M, Shirkhani, A, Chamkha, AJ. Analysis of energy consumption improvements of a zero-energy building in a humid mountainous area. J Renew Sustain Ener 2019; 11(1):015103. https://doi.org/10.1063/1.5046512.
  • [37] Kohzadia, H, Shadarama, A, Hoseinzadeh S. Improvement of the centrifugal pump performance by restricting the cavitation phenomenon. Chem Eng 2018;71. https://doi.org/10.3303/CET1871229
  • [38] Javadi, MA, Hoseinzadeh, S, Khalaji, M, Ghasemiasl, R. Optimization and analysis of exergy, economic and environmental of a combined cycle power plant. Sādhanā 2019; 44(5):121.
  • [39] Ma, Y, Mohebbi, R, Rashidi, MM, Yang, Z. MHD convective heat transfer of Ag-MgO/water hybrid nanofluid in a channel with active heaters and coolers. Int J Heat Mass Transf 2019; 137: 714-726. https://doi.org/10.1016/j.ijheatmasstransfer.2019.03.169.
  • [40] Ma, Y, Mohebbi, R, Rashidi, MM, Manca, O, Yang, Z. Numerical investigation of MHD effects on nanofluid heat transfer in a baffled U-shaped enclosure using lattice Boltzmann method. J Therm Anal Calorim 2019; 135(6):3197-3213.
  • [41] Ma, Y, Mohebbi, R, Rashidi, MM, Yang, Z, Sheremet, MA. Numerical study of MHD nanofluid natural convection in a baffled U-shaped enclosure. Int J Heat Mass Transf 2019; 130, 123-134. https://doi.org/10.1016/j.ijheatmasstransfer.2018.10.072.
  • [42] Mansoury, D, Doshmanziari, FI, Rezaie, S, Rashidi, MM. Effect of Al2O3/water nanofluid on performance of parallel flow heat exchangers. J Therm Anal Calorim 2019; 135(1): 625-643.
  • [43] Bhatti, MM, Mishra, SR, Abbas, T, Rashidi, MM. A mathematical model of MHD nanofluid flow having gyrotactic microorganisms with thermal radiation and chemical reaction effects. Neural Comput Appl 2018; 30(4): 1237-1249.
  • [44] Sheikholeslami, M, Jafaryar, M, Hedayat, M, Shafee, A, Li, Z, Nguyen, TK, Bakouri, M. Heat transfer and turbulent simulation of nanomaterial due to compound turbulator including irreversibility analysis. Int J Heat Mass Transfer 2019; 137: 1290-1300. https://doi.org/10.1016/j.ijheatmasstransfer.2019.04.030.
  • [45] Sheikholeslami, M, Jafaryar, M, Shafee, A, Li, Z, Haq, RU. Heat transfer of nanoparticles employing innovative turbulator considering entropy generation. Int J Heat Mass Transf 2019; 136: 1233-1240. https://doi.org/10.1016/j.ijheatmasstransfer.2019.03.091.
  • [46] Sheikholeslami, M, Haq, RU, Shafee, A, Li, Z, Elaraki, YG, Tlili, I. Heat transfer simulation of heat storage unit with nanoparticles and fins through a heat exchanger. Int J Heat Mass Transf 2019; 135: 470-478. https://doi.org/10.1016/j.ijheatmasstransfer.2019.02.003.
  • [47] Sheikholeslami, M, Haq, RU, Shafee, A, Li, Z. Heat transfer behavior of nanoparticle enhanced PCM solidification through an enclosure with V shaped fins. Int J Heat Mass Transf 2019; 130: 1322-1342. https://doi.org/10.1016/j.ijheatmasstransfer.2018.11.020.
  • [48] Sheikholeslami, M. New computational approach for exergy and entropy analysis of nanofluid under the impact of Lorentz force through a porous media. Comput Methods Appl Mech Eng 2019; 344:319-333. https://doi.org/10.1016/j.cma.2018.09.044.
  • [49] Sheikholeslami, M. Numerical approach for MHD Al2O3-water nanofluid transportation inside a permeable medium using innovative computer method. Comput Methods Appl Mech Eng 2019; 344:306-318. https://doi.org/10.1016/j.cma.2018.09.042.
  • [50] Sheikholeslami, M, Gerdroodbary, MB, Moradi, R, Ahmad, S, Zhixiong, Li. Application of Neural Network for estimation of heat transfer treatment of AlO-HO nanofluid through a channel. Comput Methods Appl Mech Eng 2019; 344:1-12.
  • [51] Sheikholeslami, M, Mahian, O. Enhancement of PCM solidification using inorganic nanoparticles and an external magnetic field with application in energy storage systems. J Clean Prod 2019; 215: 963-977. https://doi.org/10.1016/j.jclepro.2019.01.122.
  • [52] Sheikholeslami, M, Arabkoohsar, A, Khan, I, Shafee, A, Li, Z. Impact of Lorentz forces on Fe3O4 water ferrofluid entropy and exergy treatment within a permeable semi annulus. J Clean Prod 2019; 221: 885-898. https://doi.org/10.1016/j.jclepro.2019.02.075.
  • [53] Sheikholeslami, M, Shafee, A, Zareei, A, Haq, RU, Li, Z. Heat transfer of magnetic nanoparticles through porous media including exergy analysis. J Mol Liq 2019; 279, 719-732. https://doi.org/10.1016/j.molliq.2019.01.128.
  • [54] Sheikholeslami, M, Jafaryar, M, Shafee, A, Li, Z. Simulation of nanoparticles application for expediting melting of PCM inside a finned enclosure. Physica A: Statistical Mechanics and its Applications 2019; 523: 544-556. https://doi.org/10.1016/j.physa.2019.02.020.
  • [55] Jajja, SA, Ali, W, Ali, HM, Ali, AM. Water cooled minichannel heat sinks for microprocessor cooling: Effect of fin spacing. Appl Therm Eng 2014; 64: 76-82. https://doi.org/10.1016/j.applthermaleng.2013.12.007
  • [56] Jajja, SA, Ali, W, Ali, HM. Multiwalled carbon nanotube nanofluid for thermal management of high heat generating computer processor. Heat Transfer—Asian Research 2014; 43(7): 653-666. https://doi.org/10.1002/htj.21107.
  • [57] Ali, HM, Ali, H, Liaquat, H, Maqsood, HTB, Nadir, MA. Experimental investigation of convective heat transfer augmentation for car radiator using ZnO–water nanofluids. Energy 2015; 84: 317-324. https://doi.org/10.1016/j.energy.2015.02.103
  • [58] Ali, H., Azhar, MD., Saleem, M, Saeed, QS, Saieed, A. Water based Mgo nanofluids for thermal management of car radiator. Journal of Thermal Science 2015; 19(6): 2039-2048.
  • [59] Siddiqui, AM, Arshad, W, Ali, HM, Ali, M, Nasir, MA. Evaluation of nanofluids performance for simulated microprocessor. Therm Sci 2017; 21(5). https://doi.org/10.2298/TSCI150131159S.
  • [60] Ali, HM, Arshad, W. Thermal performance investigation of staggered and inline pin fin heat sinks using water based rutile and anatase TiO2 nanofluids. Energ Convers Manage 2015; 106: 793-803. https://doi.org/10.1016/j.enconman.2015.10.015.
  • [61] Ali, HM, Arshad, W. Effect of channel angle of pin-fin heat sink on heat transfer performance using water based graphene nanoplatelets nanofluids. Int J Heat Mass Transf 2017; 106: 465-472. https://doi.org/10.1016/j.ijheatmasstransfer.2016.08.061.
  • [62] Arshad, W, Ali, HM. Graphene nanoplatelets nanofluids thermal and hydrodynamic performance on integral fin heat sink. Int J Heat Mass Transf 2017; 107: 995-1001. https://doi.org/10.1016/j.ijheatmasstransfer.2016.10.127.
  • [63] Arshad, W, Ali, HM. Experimental investigation of heat transfer and pressure drop in a straight minichannel heat sink using TiO2 nanofluid. Int J Heat Mass Transf 2017; 110: 248-256. https://doi.org/10.1016/j.ijheatmasstransfer.2017.03.032.
  • [64] Ali, H, Babar, H, Shah, T, Sajid, M, Qasim, M, Javed, S. Preparation techniques of TiO2 nanofluids and challenges: a review. Applied Sciences 2018; 8(4): 587. https://doi.org/10.3390/app8040587.
  • [65] Sajid, MU, Ali, HM. Thermal conductivity of hybrid nanofluids: a critical review. Int J Heat Mass Transf 2018; 126: 211-234. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.021. [66] Tariq, HA, Shoukat, AA, Anwar, M, Israr, A, Ali, HM. Water cooled micro-hole cellular structure as a heat dissipation media: an experimental and numerical study. Journal of Thermal Science 2018; 1: 1-13. https://doi.org/10.2298/TSCI180219184T.
  • [67] Khan, MS, Abid, M, Ali, HM, Amber, KP, Bashir, MA, Javed, S. Comparative performance assessment of solar dish assisted s-CO2 Brayton cycle using nanofluids. Appl Therm Eng 2019; 148: 295-306. https://doi.org/10.1016/j.applthermaleng.2018.11.021.
  • [68] Babar, H, Sajid, M, Ali, HM. Viscosity of hybrid nanofluids: a critical review. Journal of Thermal Science 2019; 15.
  • [69] Sajid, MU, Ali, HM. Recent advances in application of nanofluids in heat transfer devices: a critical review. Renew Sust Energ Rev 2019; 103: 556-592. https://doi.org/10.1016/j.rser.2018.12.057.
  • [70] Mohanty, S, Parkash, O, Arora R. Analytical and comparative investigations on counter flow heat exchanger using computational fluid dynamics. Journal of Computational & Applied Research in Mechanical Engineering 2019; 10.22061/JCARME.2019.4665.1564.
  • [71] Mohanty, S, Arora, R, Parkash, O. Performance prediction and comparative analysis for a designed, developed, and modeled counter flow heat exchanger using computational fluid dynamics. Computational Thermal Sciences: An International Journal 2019; 11(5):423-443. https://doi.org/ 10.1615/ComputThermalScien.2019028520.
  • [72] Ahmed, SU, Arora, R, Parkash, O. Flow characteristics of multiphase glass beads-water slurry through horizontal pipeline using Computational Fluid Dynamics." International Journal of Automotive and Mechanical Engineering 2019; 16(2): 6605-6623.
  • [73] Ahmed, SU, Arora, R, Parkash, O. Prediction of Flow Parameters of Glass Beads-Water Slurry flow through Horizontal Pipeline using Computational Fluid Dynamics. Jordan Journal of Mechanical & Industrial Engineering 2018; 12(3):197-213.
  • [74] Ahmed, S.U., Arora, R., Parkash, O. Numerical investigations on flow characteristics of sand-water slurry through horizontal pipeline using computational fluid dynamics. J. Therm. Eng 2020; 6(2):128-139.
  • [75] Parkash, O, Arora, R. Flow characterization of multi-phase particulate slurry in thermal power plants using computational fluid dynamics. J Therm Eng 2020; 6(1):187-203. https://doi.org/10.18186/thermal.672785.
  • [76] Arora, R, Arora, R. Thermodynamic optimization of an irreversible regenerated brayton heat engine using modified ecological criteria. J Therm Eng 2020; 6(1): 28-42. https://doi.org/10.18186/thermal.671079.
  • [77] Kaushik, SC, Kumar, R, Arora, R. Thermo-economic optimization and parametric study of an irreversible regenerative Brayton cycle. J Therm Eng 2016; 4(2):861-870. https://doi.org/10.18186/jte.70740.
  • [78] Dalkiliç, AS, Cebi, A, Celen, A. Numerical analyses on the prediction of nusselt numbers for upward and downward flows of water in a smooth pipe: effects of buoyancy and property variations. J Therm Eng 2019; 5(3): 166-180. https://doi.org/10.18186/thermal.540367.
  • [79] Anil, S, Dizman, T, Celen, A, Bilge, D, Dalkılıç, AS, Wongwises, S. CFD analysis of smoke and temperature control system of an indoor parking lot with jet fans. J Therm Eng 2015; 1(2): 116-130. https://doi.org/10.18186/jte.02276.

CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS

Year 2021, , 951 - 969, 01.05.2021

Om Parkash Arvind Kumar Basant Sikarwar

https://doi.org/10.18186/thermal.930932
Cited By: 8

Abstract

The present work shows the slurry flow characteristics of glass beads having density 2470 kg/m3 at different Prandtl number through a horizontal pipeline. The simulation is conducted by Eulerian two-phase model using RNG k-ε turbulence closure in available commercial software ANSYS FLUENT. The transportation of solid particulates has the settling behaviour in the slurry pipeline and that leads to the sedimentation and blockage of the pipeline resulting more power and pressure drop in the pipeline. Therefore, it is important to know the transport capability of the solid particulates at different Prandtl fluids to minimise the pressure loss. The fluid properties at four Prandtl numbers i.e. 1.34, 2.14, 3.42 and 5.83 is used to carry the solid concentration ranges from 30-50 % (by volume) at mean flow-velocity ranging from 3 to 5 ms-1 . The obtained computational results are validated with the published data in the literature. The results show that the pressure-drop rises with escalation in flow velocity and solid concentration at all Prandtl number. It is found that the suspension stability enhancement is considerable for lower range of Prandtl number and decreases for higher range of Prandtl number. Finally, glass beads concentration contours, velocity contours, concentration profile, velocity profiles and pressure drop are predicted to understand the slurry flow for chosen Prandtl numbers.

Keywords

3D Horizontal Pipeline Prandtl Number Pressure Drop Particulates Concentration Velocity Distribution

References

  • [1] O’Brien, MP. Review of the theory of turbulent flow and its relations to sediment transportation. Transactions American Geophysical Union 1933; 14: 487-491. https://doi.org/10.1029/TR014i001p00487
  • [2] Rouse, H. Modern conceptions of the mechanics of fluid turbulence. Transactions ASCE, 1937; 102: 463-505.
  • [3] Ismail, HM. Turbulent transfer mechanism and suspended sediment in closed channels. Transactions ASCE. 1952; 117: 409-446
  • [4] Shook, CA. Daniel, S. M. Flow of suspensions of solids in pipeline: Flow with a stable stationary deposit. Can J Chem Eng, 1965; 43:56–72. https://doi.org/10.1002/cjce.5450430202.
  • [5] Shook, CA, Daniel, SM, Scott, JA, Holgate, JP. Flow of suspensions in pipelines. Can J Chem Eng 1968; 46: 238–244. https://doi.org/10.1002/cjce.5450460405
  • [6] Karabelas, AJ. Vertical distribution of dilute suspensions in turbulent pipe flow. AIChE J 1977; 23: 426–434. https://doi.org/10.1002/aic.690230404.
  • [7] Turian, RM, Hsu, FL, Selim, MS. Friction losses for flow of slurries in pipeline bends, fittings, and valves. Particul Sci Technol 1983; 1(4): 365-392. https://doi.org/10.1080/02726358308906383.
  • [8] Roco, MC, Shook, CA. Modeling of slurry flow: The effect of particle size. Can J Chem Eng 1983; 61:494–503. https://doi.org/10.1002/cjce.5450610402.
  • [9] Roco, MC, Shook, CA. Computational methods for coal slurry pipeline with heterogeneous size distribution. Powder Technol 1984; 39:159–176. https://doi.org/10.1016/0032-5910(84)85034-2.
  • [10] Colwell, JM, Shook, CA. The entry length for slurries in horizontal pipeline flow. Can J Chem Eng 1988; 66(5):714-720. https://doi.org/10.1002/cjce.5450660503.
  • [11] Gillies, RG, Shook, CA, Wilson, KC. An improved two-layer model for horizontal slurry pipeline flow. Can J Chem Eng 1991; 69: 173–178. https://doi.org/10.1002/cjce.5450690120.
  • [12] Gillies, RG, Hill KB, Mckibben, MJ, Shook, CA. Solids transport by laminar Newtonian flows. Powder Technol 1999; 104:269–277. https://doi.org/10.1016/S0032-5910(99)00104-7.
  • [13] Gillies, RG, Shook, CA. Modeling high concentration settling slurry flows. Can J Chem Eng 200; 78:709–716. https://doi.org/10.1002/cjce.5450780413.
  • [14] Matousek, V. Pressure drops and flow patterns in sand-mixture pipes. Exp Therm Fluid Sci 2002; 26(6):693-702. https://doi.org/10.1016/S0894-1777(02)00176-0.
  • [15] Ling, J, Skudarnov, PV, Lin, CX. Ebadian, M. A. Numerical investigations of liquid solid slurry flows in a fully developed turbulent flow region. International Journal of Heat and Fluid Flow 2003; 24:389-398. https://doi.org/10.1016/S0142-727X(03)00018-3.
  • [16] Krampa-Morlu, FN, Bergstrom, D.J, Bugg, JD, Sanders, RS, Schaan, J. Numerical simulation of dense coarse particle slurry flows in a vertical pipe. In 5th International Conference on Multiphase flow, ICMF 2004; 4:460.
  • [17] Kaushal, DR; Sato, K, Toyota, T, Funatsu, K, Tomita, Y. Effect of particle size distribution on pressure drop and concentration profile in pipeline flow of highly concentrated slurry. Int J Multiphas Flow 2005; 31: 809-823. https://doi.org/10.1016/j.ijmultiphaseflow.2005.03.003.
  • [18] Kaushal, DR, Tomita, Y. Experimental investigation for near-wall lift of coarser particles in slurry pipeline using γ-ray densitometer. Powder Technol 2007; 172(3):177-187. https://doi.org/10.1016/j.powtec.2006.11.020.
  • [19] Lin, CX. Ebadian, MA. A numerical study of developing slurry flow in the entrance region of a horizontal pipe. Comput Fluids 2008; 37(8):965-974. https://doi.org/10.1016/j.compfluid.2007.10.008.
  • [20] Lahiri, SK, Ghanta, KC. Prediction of pressure drop of slurry flow in pipeline by hybrid support vector regression and genetic algorithm model. Chin J Chem Eng 2008; 16(6): 841-848. https://doi.org/10.1016/S1004-9541(09)60003-3.
  • [21] Kumar, A, Kaushal, DR, Kumar, U. Bend pressure drop experiments compared with FLUENT. Proceedings of the Institution of Civil Engineers-Engineering and Computational Mechanics 2008; 161(1): 35-42. https://doi.org/10.1680/eacm.2008.161.1.35.
  • [22] Chandel, S, Seshadri, V, Singh, SN. Effect of additive on pressure drop and rheological characteristics of fly ash slurry at high concentration. Particul Sci Technol 2009; 27(3):271-284. https://doi.org/10.1080/02726350902922036.
  • [23] Monteiro, AC, Bansal, PK. Pressure drop characteristics and rheological modeling of ice slurry flow in pipes. Int J Refrig 2010; 33(8):1523-1532. https://doi.org/10.1016/j.ijrefrig.2010.09.009.
  • [24] Naik, HK, Mishra, MK, Rao, KU. Influence of chemical reagents on rheological properties of fly ash–water slurry at varying temperature environment. Coal Combustion and Gasification Products 2011; 3:83-93.
  • [25] Kaushal, DR, Thinglas, T, Tomita, Y, Kuchii, S, Tsukamoto, H. CFD modeling for pipeline flow of fine particles at high concentration. Int J Multiphas Flow 2012; 43: 85-100. https://doi.org/10.1016/j.ijmultiphaseflow.2012.03.005.
  • [26] Kaushal, DR, Kumar, A, Tomita, Y, Kuchii, S, Tsukamoto, H. Flow of mono-dispersed particles through horizontal bend. Int J Multiphas Flow 2013; 52: 71-91. https://doi.org/10.1016/j.ijmultiphaseflow.2012.12.009.
  • [27] Gopaliya, MK, Kaushal, DR. Modeling of sand-water slurry flow through horizontal pipe using CFD. J Hydrol Hydromech 2016; 64(3): 261-272.
  • [28] Melorie, AK, Kaushal, DR. Experimental Investigations of the Effect of Chemical Additives on the Rheological Properties of Highly Concentrated Iron Ore Slurries. KONA Powder Part J. 2017; 35:186-199. https://doi.org/10.14356/kona.2018001
  • [29] Assefa, KM, Kaushal DR. A new model for the viscosity of highly concentrated multi-sized particulate Bingham slurries. Particul Sci Technol 2017; 35(1):77-85. https://doi.org/10.1080/02726351.2015.1131789.
  • [30] Singh, JP, Kumar, S, Mohapatra, SK. Modelling of two-phase solid-liquid flow in horizontal pipe using computational fluid dynamics technique. Int J Hydrogen Energy 2017; 42(31): 20133-20137. https://doi.org/10.1016/j.ijhydene.2017.06.060.
  • [31] Singh, MK, Kumar, S, Ratha, D, Kaur, H. Design of slurry transportation pipeline for the flow of multi-particulate coal ash suspension. Int J Hydrogen Energy 2017; 42 (30):19135-19138.
  • [32] Parkash, O, Kumar, A, Sikarwar, BS, CFD Modeling of Commercial Slurry Flow through Horizontal Pipeline. In Advances in Interdisciplinary Engineering. Springer, Singapore 2019; 153-162. https://doi.org/10.1007/978-981-13-6577-5_16.
  • [33] Hoseinzadeh, S, Ghasemiasl, Havaei, D, Chamkha, AJ. Numerical investigation of rectangular thermal energy storage units with multiple phase change materials. J Mol Liq 2018; 271: 655-660. https://doi.org/10.1016/j.molliq.2018.08.128.
  • [34] Hoseinzadeh, S, Heyns, PS, Chamkha, AJ, Shirkhani, A. Thermal analysis of porous fins enclosure with the comparison of analytical and numerical methods. J Therm Anal Calorim 2019; 1-9.
  • [35] Hoseinzadeh, S, Moafi, A, Shirkhani, A, Chamkha, AJ. Numerical Validation Heat Transfer of Rectangular Cross-Section Porous Fins. Journal of Thermophysics and Heat Transfer 2019; 1-7. https://doi.org/10.2514/1.T5583
  • [36] Hoseinzadeh, S, Hadi Zakeri, M, Shirkhani, A, Chamkha, AJ. Analysis of energy consumption improvements of a zero-energy building in a humid mountainous area. J Renew Sustain Ener 2019; 11(1):015103. https://doi.org/10.1063/1.5046512.
  • [37] Kohzadia, H, Shadarama, A, Hoseinzadeh S. Improvement of the centrifugal pump performance by restricting the cavitation phenomenon. Chem Eng 2018;71. https://doi.org/10.3303/CET1871229
  • [38] Javadi, MA, Hoseinzadeh, S, Khalaji, M, Ghasemiasl, R. Optimization and analysis of exergy, economic and environmental of a combined cycle power plant. Sādhanā 2019; 44(5):121.
  • [39] Ma, Y, Mohebbi, R, Rashidi, MM, Yang, Z. MHD convective heat transfer of Ag-MgO/water hybrid nanofluid in a channel with active heaters and coolers. Int J Heat Mass Transf 2019; 137: 714-726. https://doi.org/10.1016/j.ijheatmasstransfer.2019.03.169.
  • [40] Ma, Y, Mohebbi, R, Rashidi, MM, Manca, O, Yang, Z. Numerical investigation of MHD effects on nanofluid heat transfer in a baffled U-shaped enclosure using lattice Boltzmann method. J Therm Anal Calorim 2019; 135(6):3197-3213.
  • [41] Ma, Y, Mohebbi, R, Rashidi, MM, Yang, Z, Sheremet, MA. Numerical study of MHD nanofluid natural convection in a baffled U-shaped enclosure. Int J Heat Mass Transf 2019; 130, 123-134. https://doi.org/10.1016/j.ijheatmasstransfer.2018.10.072.
  • [42] Mansoury, D, Doshmanziari, FI, Rezaie, S, Rashidi, MM. Effect of Al2O3/water nanofluid on performance of parallel flow heat exchangers. J Therm Anal Calorim 2019; 135(1): 625-643.
  • [43] Bhatti, MM, Mishra, SR, Abbas, T, Rashidi, MM. A mathematical model of MHD nanofluid flow having gyrotactic microorganisms with thermal radiation and chemical reaction effects. Neural Comput Appl 2018; 30(4): 1237-1249.
  • [44] Sheikholeslami, M, Jafaryar, M, Hedayat, M, Shafee, A, Li, Z, Nguyen, TK, Bakouri, M. Heat transfer and turbulent simulation of nanomaterial due to compound turbulator including irreversibility analysis. Int J Heat Mass Transfer 2019; 137: 1290-1300. https://doi.org/10.1016/j.ijheatmasstransfer.2019.04.030.
  • [45] Sheikholeslami, M, Jafaryar, M, Shafee, A, Li, Z, Haq, RU. Heat transfer of nanoparticles employing innovative turbulator considering entropy generation. Int J Heat Mass Transf 2019; 136: 1233-1240. https://doi.org/10.1016/j.ijheatmasstransfer.2019.03.091.
  • [46] Sheikholeslami, M, Haq, RU, Shafee, A, Li, Z, Elaraki, YG, Tlili, I. Heat transfer simulation of heat storage unit with nanoparticles and fins through a heat exchanger. Int J Heat Mass Transf 2019; 135: 470-478. https://doi.org/10.1016/j.ijheatmasstransfer.2019.02.003.
  • [47] Sheikholeslami, M, Haq, RU, Shafee, A, Li, Z. Heat transfer behavior of nanoparticle enhanced PCM solidification through an enclosure with V shaped fins. Int J Heat Mass Transf 2019; 130: 1322-1342. https://doi.org/10.1016/j.ijheatmasstransfer.2018.11.020.
  • [48] Sheikholeslami, M. New computational approach for exergy and entropy analysis of nanofluid under the impact of Lorentz force through a porous media. Comput Methods Appl Mech Eng 2019; 344:319-333. https://doi.org/10.1016/j.cma.2018.09.044.
  • [49] Sheikholeslami, M. Numerical approach for MHD Al2O3-water nanofluid transportation inside a permeable medium using innovative computer method. Comput Methods Appl Mech Eng 2019; 344:306-318. https://doi.org/10.1016/j.cma.2018.09.042.
  • [50] Sheikholeslami, M, Gerdroodbary, MB, Moradi, R, Ahmad, S, Zhixiong, Li. Application of Neural Network for estimation of heat transfer treatment of AlO-HO nanofluid through a channel. Comput Methods Appl Mech Eng 2019; 344:1-12.
  • [51] Sheikholeslami, M, Mahian, O. Enhancement of PCM solidification using inorganic nanoparticles and an external magnetic field with application in energy storage systems. J Clean Prod 2019; 215: 963-977. https://doi.org/10.1016/j.jclepro.2019.01.122.
  • [52] Sheikholeslami, M, Arabkoohsar, A, Khan, I, Shafee, A, Li, Z. Impact of Lorentz forces on Fe3O4 water ferrofluid entropy and exergy treatment within a permeable semi annulus. J Clean Prod 2019; 221: 885-898. https://doi.org/10.1016/j.jclepro.2019.02.075.
  • [53] Sheikholeslami, M, Shafee, A, Zareei, A, Haq, RU, Li, Z. Heat transfer of magnetic nanoparticles through porous media including exergy analysis. J Mol Liq 2019; 279, 719-732. https://doi.org/10.1016/j.molliq.2019.01.128.
  • [54] Sheikholeslami, M, Jafaryar, M, Shafee, A, Li, Z. Simulation of nanoparticles application for expediting melting of PCM inside a finned enclosure. Physica A: Statistical Mechanics and its Applications 2019; 523: 544-556. https://doi.org/10.1016/j.physa.2019.02.020.
  • [55] Jajja, SA, Ali, W, Ali, HM, Ali, AM. Water cooled minichannel heat sinks for microprocessor cooling: Effect of fin spacing. Appl Therm Eng 2014; 64: 76-82. https://doi.org/10.1016/j.applthermaleng.2013.12.007
  • [56] Jajja, SA, Ali, W, Ali, HM. Multiwalled carbon nanotube nanofluid for thermal management of high heat generating computer processor. Heat Transfer—Asian Research 2014; 43(7): 653-666. https://doi.org/10.1002/htj.21107.
  • [57] Ali, HM, Ali, H, Liaquat, H, Maqsood, HTB, Nadir, MA. Experimental investigation of convective heat transfer augmentation for car radiator using ZnO–water nanofluids. Energy 2015; 84: 317-324. https://doi.org/10.1016/j.energy.2015.02.103
  • [58] Ali, H., Azhar, MD., Saleem, M, Saeed, QS, Saieed, A. Water based Mgo nanofluids for thermal management of car radiator. Journal of Thermal Science 2015; 19(6): 2039-2048.
  • [59] Siddiqui, AM, Arshad, W, Ali, HM, Ali, M, Nasir, MA. Evaluation of nanofluids performance for simulated microprocessor. Therm Sci 2017; 21(5). https://doi.org/10.2298/TSCI150131159S.
  • [60] Ali, HM, Arshad, W. Thermal performance investigation of staggered and inline pin fin heat sinks using water based rutile and anatase TiO2 nanofluids. Energ Convers Manage 2015; 106: 793-803. https://doi.org/10.1016/j.enconman.2015.10.015.
  • [61] Ali, HM, Arshad, W. Effect of channel angle of pin-fin heat sink on heat transfer performance using water based graphene nanoplatelets nanofluids. Int J Heat Mass Transf 2017; 106: 465-472. https://doi.org/10.1016/j.ijheatmasstransfer.2016.08.061.
  • [62] Arshad, W, Ali, HM. Graphene nanoplatelets nanofluids thermal and hydrodynamic performance on integral fin heat sink. Int J Heat Mass Transf 2017; 107: 995-1001. https://doi.org/10.1016/j.ijheatmasstransfer.2016.10.127.
  • [63] Arshad, W, Ali, HM. Experimental investigation of heat transfer and pressure drop in a straight minichannel heat sink using TiO2 nanofluid. Int J Heat Mass Transf 2017; 110: 248-256. https://doi.org/10.1016/j.ijheatmasstransfer.2017.03.032.
  • [64] Ali, H, Babar, H, Shah, T, Sajid, M, Qasim, M, Javed, S. Preparation techniques of TiO2 nanofluids and challenges: a review. Applied Sciences 2018; 8(4): 587. https://doi.org/10.3390/app8040587.
  • [65] Sajid, MU, Ali, HM. Thermal conductivity of hybrid nanofluids: a critical review. Int J Heat Mass Transf 2018; 126: 211-234. https://doi.org/10.1016/j.ijheatmasstransfer.2018.05.021. [66] Tariq, HA, Shoukat, AA, Anwar, M, Israr, A, Ali, HM. Water cooled micro-hole cellular structure as a heat dissipation media: an experimental and numerical study. Journal of Thermal Science 2018; 1: 1-13. https://doi.org/10.2298/TSCI180219184T.
  • [67] Khan, MS, Abid, M, Ali, HM, Amber, KP, Bashir, MA, Javed, S. Comparative performance assessment of solar dish assisted s-CO2 Brayton cycle using nanofluids. Appl Therm Eng 2019; 148: 295-306. https://doi.org/10.1016/j.applthermaleng.2018.11.021.
  • [68] Babar, H, Sajid, M, Ali, HM. Viscosity of hybrid nanofluids: a critical review. Journal of Thermal Science 2019; 15.
  • [69] Sajid, MU, Ali, HM. Recent advances in application of nanofluids in heat transfer devices: a critical review. Renew Sust Energ Rev 2019; 103: 556-592. https://doi.org/10.1016/j.rser.2018.12.057.
  • [70] Mohanty, S, Parkash, O, Arora R. Analytical and comparative investigations on counter flow heat exchanger using computational fluid dynamics. Journal of Computational & Applied Research in Mechanical Engineering 2019; 10.22061/JCARME.2019.4665.1564.
  • [71] Mohanty, S, Arora, R, Parkash, O. Performance prediction and comparative analysis for a designed, developed, and modeled counter flow heat exchanger using computational fluid dynamics. Computational Thermal Sciences: An International Journal 2019; 11(5):423-443. https://doi.org/ 10.1615/ComputThermalScien.2019028520.
  • [72] Ahmed, SU, Arora, R, Parkash, O. Flow characteristics of multiphase glass beads-water slurry through horizontal pipeline using Computational Fluid Dynamics." International Journal of Automotive and Mechanical Engineering 2019; 16(2): 6605-6623.
  • [73] Ahmed, SU, Arora, R, Parkash, O. Prediction of Flow Parameters of Glass Beads-Water Slurry flow through Horizontal Pipeline using Computational Fluid Dynamics. Jordan Journal of Mechanical & Industrial Engineering 2018; 12(3):197-213.
  • [74] Ahmed, S.U., Arora, R., Parkash, O. Numerical investigations on flow characteristics of sand-water slurry through horizontal pipeline using computational fluid dynamics. J. Therm. Eng 2020; 6(2):128-139.
  • [75] Parkash, O, Arora, R. Flow characterization of multi-phase particulate slurry in thermal power plants using computational fluid dynamics. J Therm Eng 2020; 6(1):187-203. https://doi.org/10.18186/thermal.672785.
  • [76] Arora, R, Arora, R. Thermodynamic optimization of an irreversible regenerated brayton heat engine using modified ecological criteria. J Therm Eng 2020; 6(1): 28-42. https://doi.org/10.18186/thermal.671079.
  • [77] Kaushik, SC, Kumar, R, Arora, R. Thermo-economic optimization and parametric study of an irreversible regenerative Brayton cycle. J Therm Eng 2016; 4(2):861-870. https://doi.org/10.18186/jte.70740.
  • [78] Dalkiliç, AS, Cebi, A, Celen, A. Numerical analyses on the prediction of nusselt numbers for upward and downward flows of water in a smooth pipe: effects of buoyancy and property variations. J Therm Eng 2019; 5(3): 166-180. https://doi.org/10.18186/thermal.540367.
  • [79] Anil, S, Dizman, T, Celen, A, Bilge, D, Dalkılıç, AS, Wongwises, S. CFD analysis of smoke and temperature control system of an indoor parking lot with jet fans. J Therm Eng 2015; 1(2): 116-130. https://doi.org/10.18186/jte.02276.
There are 78 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Om Parkash This is me 0000-0001-7080-570X

Arvind Kumar This is me 0000-0003-0495-199X

Basant Sikarwar This is me 0000-0001-8532-7528

Publication Date May 1, 2021
Submission Date April 28, 2019
Published in Issue Year 2021

Cite

APA Parkash, O., Kumar, A., & Sikarwar, B. (2021). CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS. Journal of Thermal Engineering, 7(4), 951-969. https://doi.org/10.18186/thermal.930932
AMA Parkash O, Kumar A, Sikarwar B. CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS. Journal of Thermal Engineering. May 2021;7(4):951-969. doi:10.18186/thermal.930932
Chicago Parkash, Om, Arvind Kumar, and Basant Sikarwar. “CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS”. Journal of Thermal Engineering 7, no. 4 (May 2021): 951-69. https://doi.org/10.18186/thermal.930932.
EndNote Parkash O, Kumar A, Sikarwar B (May 1, 2021) CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS. Journal of Thermal Engineering 7 4 951–969.
IEEE O. Parkash, A. Kumar, and B. Sikarwar, “CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS”, Journal of Thermal Engineering, vol. 7, no. 4, pp. 951–969, 2021, doi: 10.18186/thermal.930932.
ISNAD Parkash, Om et al. “CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS”. Journal of Thermal Engineering 7/4 (May 2021), 951-969. https://doi.org/10.18186/thermal.930932.
JAMA Parkash O, Kumar A, Sikarwar B. CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS. Journal of Thermal Engineering. 2021;7:951–969.
MLA Parkash, Om et al. “CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS”. Journal of Thermal Engineering, vol. 7, no. 4, 2021, pp. 951-69, doi:10.18186/thermal.930932.
Vancouver Parkash O, Kumar A, Sikarwar B. CFD MODELING OF SLURRY PIPELINE AT DIFFERENT PRANDTL NUMBERS. Journal of Thermal Engineering. 2021;7(4):951-69.

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