FLOW CHARACTERIZATION OF MULTI-PHASE PARTICULATE SLURRY IN THERMAL POWER PLANTS USING COMPUTATIONAL FLUID DYNAMICS

Abstract

The key issue associated with the thermal power plant is the disposal of ash-water slurry and the process of its transportation is accomplished using long length pipelines. The designing of such pipelines is a vital endeavor of researchers and designers globally. In this perspective, numerical simulation of 42 mm diameter three-dimensional slurry flow pipeline carrying high concentration of mono-dispersed fine ash particles has been carried out. The study is enunciated by employing Eulerian- Eulerian two-phase model with RNG k-ɛ turbulence model with the aim of visualizing and understanding the characteristics of the slurry flow behavior. The coal ash slurry concentration varies between 50% to 70% (by weight) for velocity ranges, 1-3 ms-1. The modeling is done using Fluent commercial software with the intention of predicting the characteristics of flow for 300 µm particle size. It is observed that pressure drop upsurges non-linearly with solid concentrations and slurry velocity across pipeline. The obtained results of predetermined pressure drop are analytically compared with the experimental results. Moreover, the results are also compared with that of Eulerian-Langrange model using SST K-ω turbulence model and it is found that RNG k-ɛ turbulence model yields more accurate and desirable results.

Keywords

• 3D CFD,Eulerian–Eulerian Two-Phase Model,Thermal Power Plants,Slurry Concentration,Pressure Drop

References

1. [1] Colwell JM, Shook CA. The entry length for slurries in horizontal pipeline flow. Can J Chem Eng 1988; 66(5): 714-720. doi:10.1002/cjce.5450660503.
2. [2] 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. doi:10.1080/02726358308906383
3. [3] Matousek V. Pressure drops and flow patterns in sand-mixture pipes. Exp Therm Fluid Sci 2002; 26(6): 693-702. doi:10.1016/S0894-1777(02)00176-0.
4. [4] Krampa-Morlu FN, Bergstrom DJ, Bugg JD, Sanders RS, Schaan J. Numerical simulation of dense coarse particle slurry flows in a vertical pipe. In 5th Int Conf Multiphase flow, ICMF 2004; 4: 460.
5. [5] Kraft M. Modelling of Particulate Processes. KONA Powder Part J 2005; 23:18-35. doi:10.14356/kona.2005007.
6. [6] Kumar U, Singh SN, Seshadri V. Prediction of flow characteristics of bimodal slurry in horizontal pipe flow. Particul Sci Technol 2008; 26(4): 361-379. doi:10.1080/02726350802084564
7. [7] 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. doi:10.1016/j.compfluid.2007.10.008.
8. [8] Chandel S, Singh SN, Seshadri V. Transportation of high concentration coal ash slurries through pipelines. Int Archive Appl Sci Tech 2010; 1: 1-9.

9. [9] 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. doi: 10.1080/02726350902922036
10. [10] Naik HK, Mishra MK, Rao KU. Influence of chemical reagents on rheological properties of fly ash–water slurry at varying temperature environment. Coal Combus Gasification Products 2011; 3: 83-93.
11. [11] Senapati PK, Mishra BK, Parida BK. Analysis of friction mechanism and homogeneity of suspended load for high concentration fly ash & bottom ash mixture slurry using rheological and pipeline experimental data. Powder Technol 2013; 250: 154-163. doi: 10.1016/j.powtec.2013.10.014.
12. [12] Jiang YY, Zhang P. Numerical investigation of slush nitrogen flow in a horizontal pipe. Chem Eng Sci 2012; 73: 169-180. doi:10.1016/j.ces.2012.01.027.
13. [13] 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. doi: 10.1016/j.ijmultiphaseflow.2012.03.005.
14. [14] 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. doi:10.1016/j.ijmultiphaseflow.2012.12.009
15. [15] Nabil T, El-Sawaf I, El-Nahhas K. Sand-Water Slurry Flow Modelling in a Horizontal Pipeline by Computational fluid Dynamics Technique. Int Water Tech J 2014; 4(1): 13.
16. [16] Silva R, Garcia FAP, Faia Pedro MGM, Rasteiro M.G. Settling suspensions flow modelling: A review. KONA Powder Part J 2015; 32: 41-56. doi: 10.14356/kona.2015009.
17. [17] Gopaliya MK, Kaushal DR. Analysis of effect of grain size on various parameters of slurry flow through pipeline using CFD. Particul Sci Technol 2015; 33(4): 369-384. doi:10.1080/02726351.2014.971988
18. [18] Pani GK, Rath P, Barik R, Senapati PK. The effect of selective additives on the rheological behavior of power plant ash slurry. Particul Sci Technol 2015; 33(4): 418-422. doi:10.1080/02726351.2014.990657
19. [19] Assefa KM, Kaushal DR. Experimental study on the rheological behaviour of coal ash slurries. J Hydrol Hydromech 2015; 63(4): 303-310.
20. [20] Swamy M, Díez NG, Twerda A. Numerical modelling of the slurry flow in pipelines and prediction of flow regimes. WIT Trans Eng Sci 2015; 89: 311-322.
21. [21] Wu D, Yang B, Liu Y. Pressure drop in loop pipe flow of fresh cemented coal gangue–fly ash slurry: Experiment and simulation. Adv Powder Technol 2015; 26(3): 920-927. doi:10.1016/j.apt.2015.03.009.
22. [22] Messa GV, Malavasi S. Improvements in the numerical prediction of fully-suspended slurry flow in horizontal pipes. Powder Technol 2015; 270: 358-367. doi:10.1016/j.powtec.2014.02.005.
23. [23] Gopaliya MK, Kaushal DR. Modeling of sand-water slurry flow through horizontal pipe using CFD. J Hydrol Hydromech 2016; 64(3): 261-272.
24. [24] Ofei TN, Ismail AY. Eulerian-Eulerian simulation of particle-liquid slurry flow in horizontal pipe. J Pet Eng 2016; 1-10. doi:10.1155/2016/5743471.
25. [25] Peng W, Cao X. Numerical simulation of solid particle erosion in pipe bends for liquid–solid flow. Powder Technol 2016; 294: 266-279. doi:10.1016/j.powtec.2016.02.030.
26. [26] Kaushal DR, Kumar A, Tomita Y, Kuchii S, Tsukamoto H. Flow of Bi-modal Slurry through Horizontal Bend. KONA Powder Part J 2017; 34: 258-274. doi:10.14356/kona.2017016.
27. [27] 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. doi:10.1080/02726351.2015.1131789.
28. [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; 2018001. doi:10.14356/kona.2018001.
29. [29] Naveh R, Tripathi NM, Kalman H. Experimental pressure drop analysis for horizontal dilute phase particle-fluid flows, Powder Technol 2017; 321: 355-368. doi:10.1016/j.powtec.2017.08.029.
30. [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. doi:10.1016/j.ijhydene.2017.06.060
31. [31] Arora R, Kaushik SC, Kumar Raj, Arora R. Soft computing based multi-objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making. Appl Soft Comput 2016; 46: 267-283. doi:10.1016/j.asoc.2016.05.001.
32. [32] Kumar R, Kaushik SC, Kumar Raj, Hans R. Multi-objective thermodynamic optimization of irreversible regenerative Brayton cycle using evolutionary algorithm and decision making. Ain Shams Eng J 2016; 7 (2): 741-753. doi:10.1016/j.asej.2015.06.007.
33. [33] Arora R, Kaushik SC, Kumar R. Multi-objective thermodynamic optimization of solar parabolic dish Stirling heat engine with regenerative losses using NSGA-II and decision making. Appl Sol Energ 2016; 52 (4): 295-304. doi:10.3103/S0003701X16040046.
34. [34] Arora R, Kaushik SC, Kumar R, Arora R. Multi-objective thermo-economic optimization of solar parabolic dish Stirling heat engine with regenerative losses using NSGA-II and decision making. Int J Elec Power 2016; 74: 25-35. doi:10.1016/j.ijepes.2015.07.010.
35. [35] Shirvan KM, Ellahi R, Mamourian M, Moghiman M. Effect of wavy surface characteristics on heat transfer in a wavy square cavity filled with nanofluid. Int J Heat Mass Tran 2017; 107: 1110–1118. doi:10.1016/j.ijheatmasstransfer.2016.11.022.
36. [36] Ellahi R, Tariq MH, Hassan M, Vafai K. On boundary layer magnetic flow of nano-ferroliquid under the influence of low oscillating over stretchable rotating disk. J Mol Liq 2017; 229: 339–345. doi:10.1016/j.molliq.2016.12.073.
37. [37] Esfahani JA, Akbarzadeh M, Rashidi S, Rosen MA, Ellahi R. Influences of wavy wall and nanoparticles on entropy generation in a plate heat exchanger. Int J Heat Mass Tran 2017; 109: 1162-1171. doi:10.1016/j.ijheatmasstransfer.2017.03.006.
38. [38] Hassan M, Zeeshan A, Majeed A, Ellahi R. Particle shape effects on ferrofuids flow and heat transfer under influence of low oscillating magnetic field. J Magn Magn Mater 2017; 443: 36–44. doi:10.1016/j.jmmm.2017.07.024.
39. [39] Rashidi S, Akar S, Bovand M, Ellahi R. Volume of fluid model to simulate the nanofluid flow and entropy generation in a single slope solar still. Renew Energ 2018; 115: 400-410. doi:10.1016/j.renene.2017.08.059
40. [40] Ijaz N, Zeehan A, Bhatti MM, Ellahi R. Analytical study on liquid-solid particles interaction in the presence of heat and mass transfer through a wavy channel. J Mol Liq 2018; 250: 80–87. doi:10.1016/j.molliq.2017.11.123.
41. [41] Zeeshan A, Shehzad N, Ellahi R. Analysis of activation energy in Couette-Poiseuille flow of nanofluid in the presence of chemical reaction and convective boundary conditions. Results Phys 2018; 8: 502–512. doi:10.1016/j.rinp.2017.12.024.
42. [42] Ellahi, R. Special Issue on Recent Developments of Nanofluids. Appl Sci 2018; 8: 192. doi:10.3390/app8020192.
43. [43] Ghadikolaei SS, Hosseinzadeh Kh, Ganji DD. Analysis of unsteady MHD Eyring-Powell squeezing flow in stretching channel with considering thermal radiation and Joule heating effect. Case Studies in Thermal Engineering 2017; 10: 579-594. doi:10.1016/j.csite.2017.11.004
44. [44] Ghadikolaei SS, Hosseinzadeh Kh, Ganji DD, Hatami M. Fe3O4–(CH2OH)2 nanofluid analysis in a porous medium under MHD radiative boundary layer and dusty fluid. J Mol Liq 2018; 258: 172-185. doi:10.1016/j.molliq.2018.02.106
45. [45] Arora R, Arora R. Multiobjective optimization and analytical comparison of single‐ and 2‐stage (series/parallel) thermoelectric heat pumps. Int J Energ Res 2018; 42 (4): 1760-1778. doi:10.1002/er.3988.
46. [46] Ghadikolaei SS, Yassari M, Sadeghi H, Hosseinzadeh Kh, Ganji DD. Investigation on thermophysical properties of Tio2–Cu/H2O hybrid nanofluid transport dependent on shape factor in MHD stagnation point flow. Powder Technol 2017; 322: 428-438. doi:10.1016/j.powtec.2017.09.006.
47. [47] Ghadikolaei SS, Hosseinzadeh Kh, Yassari M, Sadeghi H, Ganji DD. Boundary layer analysis of micropolar dusty fluid with TiO2nanoparticles in a porous medium under the effect of magnetic field and thermal radiation over a stretching sheet. J Mol Liq 2017; 244: 374-389. doi:10.1016/j.molliq.2017.08.111
48. [48] Hosseinzadeh Kh, Amiri AJ, Ardahaie SS, Ganji DD. Effect of variable lorentz forces on nanofluid flow in movable parallel plates utilizing analytical method. Case Studies in Thermal Engineering 2017; 10: 595-610. doi:10.1016/j.csite.2017.11.001
49. [49] Arora R, Arora R. Multicriteria optimization based comprehensive comparative analyses of single- and two-stage (series/parallel) thermoelectric generators including the influence of Thomson effect. J Renew Sustain Ener 2018; 10 (4): 044701.doi:10.1063/1.5019972.
50. [50] Arora R, Arora R. Performance Characteristics and Thermodynamic Investigations on Single-Stage Thermoelectric Generator and Heat Pump Systems. Pertanika J Sci Technol 2018; 26 (4): 1975-1998.
51. [51] Hosseinzadeh Kh, Alizadeh M, Ganji DD. Hydrothermal analysis on MHD squeezing nanofluid flow in parallel plates by analytical method. International Journal of Mechanical and Materials Engineering 2018; 13:4. doi:10.1186/s40712-018-0089-7.
52. [52] Kumar R, Kaushik SC, Kumar R. Power optimization of an Irreversible regenerative Brayton Cycle using isothermal heat addition. J Therm Eng 2015; 1(4): 279-286. doi:10.18186/jte.44164.
53. [53] Kaushik SC, Kumar R, Arora R. Thermo-economic optimization and parameteric study of an irreversible Brayton heat engine cycle. J Therm Eng 2016; 2 (4): 861-870.
54. [54] Arora R, Kaushik SC, Kumar R. Performance optimization of Brayton heat engine at maximum efficient power using temp. dependent specific heat of working fluid. J Therm Eng 2015; 1 (2): 345-354. doi:10.18186/jte.15036.
55. [55] Arora R, Kaushik SC, Kumar R. Multi-objective optimization of an irreversible regenerative Brayton cycle using genetic algorithm. In 2015 Int Conf on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), IEEE 2015; 340-346. doi: 10.1109/ABLAZE.2015.7155017.
56. [56] Arora R, Kaushik SC, Kumar R. Multi-objective optimization of solar powered ericsson cycle using genetic algorithm and fuzzy decision making. In 2015 Int Conf on Advances in Computer Engineering and Applications (ICACEA), IEEE 2015; 553-558. doi:10.1109/ICACEA.2015.7164754.
57. [57] Arora R, Kaushik SC, Kumar R. Multi-objective thermodynamic optimisation of solar parabolic dish Stirling heat engine using NSGA-II and decision making. International Journal of Renewable Energy Technology 2017; 8 (1): 64-92. doi:10.1504/IJRET.2017.080873.
58. [58] Kumar Rajesh, Kaushik SC, Kumar Raj. Performance Analysis of an Irreversible Regenerative Brayton Cycle based on Ecological Optimization Criterion. International Journal of Thermal and Environment Engineering 2015; 9(1): 25-32.
59. [59] Kumar R, Kaushik SC, Kumar R. Efficient power of Brayton heat engine with friction. International Journal of Engineering Research and Technology 2013; 6 (5): 643-650.
60. [60] Arora R, Kaushik SC, Arora R. Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II. Energy 2015; 91: 242-254. doi:10.1016/j.energy.2015.08.044
61. [61] Arora R, Kaushik SC, Arora R. Thermodynamic modeling and multi-objective optimization of two-stage thermoelectric generator in electrically series and parallel configurations. Appl Therm Eng 2016; 25 (103): 1312-1323. doi:10.1016/j.applthermaleng.2016.05.009.
62. [62] Arora R, Arora R. Experimental Investigations and Exergetic Assessment of 1 kW Solar PV Plant. Pertanika J Sci Technol 2018; 26 (4): 1881-1897.
63. [63] Arora R, Arora R, Sridhara SN. Performance assessment of 186 kWp grid interactive solar photovoltaic plant in Northern India. Int. J. Ambient Energy 2019; 1-28. doi:10.1080/01430750.2019.1630312
64. [64] Ahmed SU, Arora R. Quality characteristics optimization in CNC end milling of A36 K02600 using Taguchi’s approach coupled with artificial neural network and genetic algorithm. International Journal of System Assurance Engineering and Management 2019; 10(4):676-95. doi:10.1007/s13198-019-00796-8.
65. [65] Chiteka K, Arora R, Jain V. CFD Prediction of dust deposition and installation parametric optimisation for soiling mitigation in non-tracking solar PV modules. Int. J. Ambient Energy 2019; 5:1-14. doi:10.1080/01430750.2019.1594373.
66. [66] Mohanty S, Arora R, Parkash O. Performance prediction and comparative analysis for a designed, developed, and modeled counterflow heat exchanger using computational fluid dynamics. Computational Thermal Sciences: An International Journal 2019; 11(5): 423-443. doi:10.1615/ComputThermalScien.2019028520.
67. [67] Arora R, Arora R. Parametric Investigations and Thermodynamic Optimization of Regenerative Brayton Heat Engine. In Advances in Fluid and Thermal Engineering 2019; 753-762. Springer, Singapore. doi:10.1007/978-981-13-6416-7_70.
68. [68] Parkash O, Kumar A, Sikarwar BS. CFD Modeling of Commercial Slurry Flow Through Horizontal Pipeline. In Advances in Interdisciplinary Engineering 2019; 153-162. Springer, Singapore. doi:10.1007/978-981-13-6577-5_16.
69. [69] Maputi ES, Arora R. Design optimization of a three-stage transmission using advanced optimization techniques. International Journal for Simulation and Multidisciplinary Design Optimization 2019;10: A8. doi:10.1051/smdo/2019009.
70. [70] Maputi ES, Arora R. Multi-objective spur gear design using teaching learning-based optimization and decision-making techniques. Cogent Engineering 2019; 6(1):1665396. DOI: 10.1080/23311916.2019.1665396
71. [71] Chiteka K, Sridhara SN, Arora R. Numerical investigation of installation and environmental parameters on soiling of roof-mounted solar photovoltaic array. Cogent Engineering 2019;6(1):1649007. DOI: 10.1080/23311916.2019.1649007
72. [72] Chiteka K, Arora R, Sridhara SN. A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models. Energ Syst 2019; 1-22. doi:10.1007/s12667-019-00348-w.