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Year 2021, Volume: 7 Issue: 4, 904 - 917, 01.05.2021
https://doi.org/10.18186/thermal.930791

Abstract

References

  • [1] Guo Y, Wang F, Jia M, Zhang S. Parallel hybrid model for mechanical draft counter flow wet-cooling tower. Appl Therm Eng 2017;125:1379–88. doi:10.1016/j.applthermaleng.2017.07.138.
  • [2] Ali HM, Azhar MD, Saleem M, Saeed QS, Saieed A. Heat transfer enhancement of car radiator using aqua based magnesium oxide nanofluids. Therm Sci 2015;19:2039–48. doi:10.2298/TSCI150526130A.
  • [3] Ali H, Liaquat H, Maqsood B. Experimental investigation of convective heat transfer augmentation for car radiator using ZnO - water nano fluids. Energy 2015;84:317–24. doi:10.1016/j.energy.2015.02.103.
  • [4] Li X, Gurgenci H, Guan Z, Wang X, Xia L. A review of the crosswind effect on the natural draft cooling towers. Appl Therm Eng 2019;150:250–70. doi:10.1016/j.applthermaleng.2018.12.147.
  • [5] Rai P, Khan AI. Performance ANalysis of Cooling Tower: A Review. Int J Innov Res Sci Eng 2016;2:96–102. [6] Dhorat A, Al-Obaidi MA, Mujtaba IM. Dynamic modelling and operational optimisation of natural draft cooling towers. Therm Sci Eng Prog 2019;9:30–43. doi:10.1016/j.tsep.2018.10.013.
  • [7] Arunkumar S, Sivakumar, D. B. Senthilkumar T. Fabrication and Performance Analysis of Cooling Tower. Int J Eng Sci Comput 2016:5359–62.
  • [8] Kumar D, Zehra T, Junejo A, Bhanbhro SA, Basit M. 4E (Energy, Exergy, Economic and Environmental) Analysis of the Novel Design of Wet Cooling Tower. J Therm Eng 2020;6:252–67. doi:10.18186/thermal.710981.
  • [9] Merkel F. Evporative cooling. Z Verein Deutsch Ingen 1925;70:123–8.
  • [10] Miao W, Jin W, Jiajin W, Cheng SHI. Contrastive Analysis of Cooling Performance between a High-level Water Collecting Cooling Tower and a Typical Cooling Tower. J Therm Sci 2018;27:39–47.
  • [11] Kl-oppers JC, Kroger DG. Cooling tower performance evaluation: Merkel, Poppe, and eNTU methods of analysis. J Eng Gas Turb Power 2005;127:1–7.
  • [12] Khan JR, Zubair SM. Performance characteristics of counter flow wet cooling towers. Energy Convers Manag 2002;44:2073–91.
  • [13] Prasad M. Economic upgradation and optimal use of multi-cell cross flow evaporative water cooling tower through modular performance appraisal. Appl Therm Eng 2003;24:579–93.
  • [14] Hajidavalloo E, Shakeri R, Mehrabian MA. Thermal performance of cross flow cooling towers in variable wet bulb temperature. Energy Convers Manag 2010;51:1298–303.
  • [15] SPX. Cooling Tower Performance: Basic Theory and Practice. Overland Park: SPX Cooling Technologies Inc..; 2013.
  • [16] SPX. Cooling Tower Fundamentals. U.S.A.: SPX Cooling Technologies Inc..; 2009.
  • [17] Olatunji O, Akinlabi S, Ajayi O, Madushele N. A survey of Artificial Neural Network-based Prediction ModelsJ for Thermal Properties of Biomass. Procedia Manuf 2019;33:184–91. doi:10.1016/j.promfg.2019.04.103.
  • [18] Adedeji PA, Akinlabi S, Ajayi O, Madushele N. Non-linear autoregressive neural network (NARNET) with SSA filtering for a university enegy consumption forecast. 16th Glob. Conf. Sustain. Manuf. Sustain. Manuf. Glob. Circ. Econ., 2019, p. 176–83. doi:.1037//0033-2909.I26.1.78.
  • [19] Adedeji P, Madushele N, Akinlabi S. Adaptive Neuro-fuzzy Inference System ( ANFIS ) for a multi-campus institution energy consumption forecast in South Africa 2018:950–8.
  • [20] Guo Q, Qi X, Wei Z, Yin Q, Sun P, Guo P, et al. Modeling and characteristic analysis of fouling in a wet cooling tower based on wavelet neural networks. Appl Therm Eng 2019;152:907–16. doi:10.1016/j.applthermaleng.2019.02.041.
  • [21] Hosoz M. Performance prediction of a cooling tower using artificial neural network. Energy Convers Manag 2007;48:1349–59. doi:10.1016/j.enconman.2006.06.024.
  • [22] Homod RZ. Review on the HVAC System Modeling Types and the Shortcomings of Their Application. J Energy 2013:1–10.
  • [23] Rashidi MM, Bég OA, Parsa AB, Nazari F. Analysis and optimization of a transcritical power cycle with regenerator using artificial neural networks and genetic algorithms. Proc Inst Mech Eng Part A J Power Energy 2011;225:701–17. doi:10.1177/0957650911407700.
  • [24] Rashidi MM, Ali M, Freidoonimehr N, Nazari F. Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm. Energy 2013;55:497–510. doi:10.1016/j.energy.2013.01.036.
  • [25] Rashidi MM, Galanis N, Nazari F, Parsa AB, Shamekhi L. Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network Clausius Rankine cycle. Energy 2011;36:5728–40. doi:10.1016/j.energy.2011.06.036.
  • [26] Zhang Y, Zhang H, Wang Y, You S, Zheng W. Optimal configuration and operating condition of counter flow cooling towers using particle swarm optimization algorithm. Appl Therm Eng 2019;151:318–27. doi:10.1016/j.applthermaleng.2019.01.097.
  • [27] Gao M, Sun F, Zhou S, Shi Y, Zhao Y, Wang N. Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions. Int J Therm Sci 2009;48:583–9. doi:10.1016/j.ijthermalsci.2008.03.012.
  • [28] Qi X, Liu Z, Li D. Numerical simulation of shower cooling tower based on artificial neural network. Energy Convers Manag 2008;49:724–32. doi:10.1016/j.enconman.2007.07.032.
  • [29] Kalatuzov VA. A procedure for constructing the normative characteristics of chimney-type evaporative cooling towers from the results of field measurements. Therm Eng 2007;54:901–5. doi:10.1134/s0040601507110092.
  • [30] Nasrabadi M, Finn DP. Mathematical modeling of a low temperature low approach direct cooling tower for the provision of high temperature chilled water for conditioning of building spaces. Appl Therm Eng 2014;64:273–82.
  • [31] Adetula Y, Bamidele EA. Timely Completion of Nigerian Iron and Steel Projects: the imperative of technical manpower development. 29th Annu. Conf. AGM Niger. Metall. Soc. Ajaokuta Steel Co., 2013.
  • [32] Ajaokuta Steel Company Limited. Company Overview : Ajaokuta Steel Company. Ajaokuta Steel Co 2019. http://www.ajaokutasteel.com/site/pagef.php?cnt=Company Overview (accessed May 11, 2019).
  • [33] Kroger DG. Air-Cooled Heat Exchangers and Cooling Towers. Tulsa, Oklahoma: PennWell Corp..; 2004.
  • [34] Kashani MMH, Dobrego K V. Heat and mass transfer in natural draft cooling towers. J Eng Phys Thermophys 2013;86:1072–82. doi:10.1007/s10891-013-0930-z.
  • [35] Chen X, Sun F, Chen Y, Gao M. Novel method for improving the cooling performance of natural draft wet cooling towers. Appl Therm Eng 2019;147:562–70. doi:10.1016/j.applthermaleng.2018.10.076.
  • [36] Akkaya A V. Performance analyzing of an organic rankine cycle under different ambient conditions. J Therm Eng 2017;3:1498–504. doi:10.18186/journal-of-thermal-engineering.338897.
  • [37] Naik BK, Muthukumar P. A novel approach for performance assessment of mechanical draft wet cooling towers. Appl Therm Eng 2017;121:14–26. doi:10.1016/j.applthermaleng.2017.04.042.
  • [38] Naik BK, Choudhary V, Muthukumar P, Somayaji C. Performance Assessment of a Counter Flow Cooling Tower - Unique Approach. Energy Procedia 2017;109:243–52. doi:10.1016/j.egypro.2017.03.056.
  • [39] Kloppers JC, Kroger DG. Cooling Tower Performance Evaluation : Merkel , Poppe , and e -NTU Methods of Analysis. J Eng Gas Turbines Power 2005;127:1–7. doi:10.1115/1.1787504.
  • [40] Ren C. An Analytical Approach to the Heat and Mass Transfer Processes in Counterflow Cooling. Trans ASME 2006;128:1–7. doi:10.1115/1.2352780.
  • [41] Kloppers JC, Kroger DG. A Critical Investigation into the Heat and Mass Transfer Analysis of Counterflow Wet-Cooling Towers. Int J Heat Mass Transf 2005:765–77.
  • [42] ASHRAE. Fundamentals: ASHRAE Handbook. Atlanta: American Society of Heating, Refrigeration and AirConditioning Engineers; 2009.
  • [43] Nicklas S, Strehlow G, Duda SW, Simmonds P. 2016 HVAC Systems and Equipment. ASHRAE; 2016.
  • [44] Bamimore OT. Paramteric Effects on the Performance of an Industrial Cooling Tower (A Case Study of Ajaokuta Power Plant). 2016.
  • [45] Zou Z, Guan Z, Gurgenci H, Lu Y. Solar enhanced natural draft dry cooling tower for geothermal power applications. Sol Energy 2012;86:2686–94. doi:10.1016/j.solener.2012.06.003.
  • [46] Ge W, Fan J, Liu CX, Li W, Chen G, Zhao Y. Critical impact factors on the cooling performance design of natural draft dry cooling tower and relevant optimization strategies. Appl Therm Eng 2019;154:614–27. doi:10.1016/j.applthermaleng.2019.03.008.
  • [47] Dong P, Li X, Hooman K, Sun Y, Li J, Guan Z, et al. The crosswind effects on the start-up process of natural draft dry cooling towers in dispatchable power plants. Int J Heat Mass Transf 2019;135:950–61. doi:10.1016/j.ijheatmasstransfer.2019.02.039.
  • [48] Dang Z, Zhang Z, Gao M, He S. Numerical simulation of thermal performance for super large-scale wet cooling tower equipped with an axial fan. Int J Heat Mass Transf 2019;135:220–34. doi:10.1016/j.ijheatmasstransfer.2019.01.111.
  • [49] Klimanek A. Numerical Modelling of Natural Draft Wet-Cooling Towers. Arch Comput Methods Eng 2013;20:61–109. doi:10.1007/s11831-013-9081-9.
  • [50] Zhou Y, Gao M, Long G, Zhang Z, Dang Z, He S. Experimental study regarding the effects of forced ventilation on the thermal performance for super-large natural draft wet cooling towers. Appl Therm Eng 2019;155:40–8. doi:10.1016/j.applthermaleng.2019.03.149.
  • [51] Babar H, Sajid MU, Ali HM. Viscousity of Hybrid Nanofluids: a critical review. J Therm Sci 2019:1–49. [52] Sajid MU, Ali HM. Recent advances in application of nanofluids in heat transfer devices : A critical review. Renew Sustain Energy Rev 2019;103:556–92. doi:10.1016/j.rser.2018.12.057.
  • [53] Hussein AM, Bakar RA, Kadirgama K. Case Studies in Thermal Engineering Study of forced convection nanofluid heat transfer in the automotive cooling system. Case Stud Therm Eng 2014;2:50–61. doi:10.1016/j.csite.2013.12.001

PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER

Year 2021, Volume: 7 Issue: 4, 904 - 917, 01.05.2021
https://doi.org/10.18186/thermal.930791

Abstract

Sensible and latent heat rejection from heat engines is of high necessity for system efficiency and continuous
production. The cooling tower is one of the major heat-exchanging systems used for cooling industrial heat systems
by intimately mixing hot water with cooling air. Optimal operating conditions and parameters of the system are highly
essential for its effectiveness and efficiency. This study used the Poppe model to evaluate selected thermodynamic
relations of a rectangular counter-flow industrial cooling tower of a steel rolling mill using the system’s inlet and outlet
data as initial conditions. The effect of increasing the water temperature on the air moisture content, Merkel number,
and specific enthalpy was studied across the fills of the cooling tower. Air moisture content, Merkel number and
specific enthalpy of the system increase with increasing water temperature. However, while other variables reach a
stationary point at half the nodal segments, the specific enthalpy increases across the fills in the system. It was
concluded that the use of nano particles with high heat removal rate could increase the efficiency of the system. Also,
an increase in the quantity of the makeup water of a force draft system is recommended towards increasing the system
efficiency.

References

  • [1] Guo Y, Wang F, Jia M, Zhang S. Parallel hybrid model for mechanical draft counter flow wet-cooling tower. Appl Therm Eng 2017;125:1379–88. doi:10.1016/j.applthermaleng.2017.07.138.
  • [2] Ali HM, Azhar MD, Saleem M, Saeed QS, Saieed A. Heat transfer enhancement of car radiator using aqua based magnesium oxide nanofluids. Therm Sci 2015;19:2039–48. doi:10.2298/TSCI150526130A.
  • [3] Ali H, Liaquat H, Maqsood B. Experimental investigation of convective heat transfer augmentation for car radiator using ZnO - water nano fluids. Energy 2015;84:317–24. doi:10.1016/j.energy.2015.02.103.
  • [4] Li X, Gurgenci H, Guan Z, Wang X, Xia L. A review of the crosswind effect on the natural draft cooling towers. Appl Therm Eng 2019;150:250–70. doi:10.1016/j.applthermaleng.2018.12.147.
  • [5] Rai P, Khan AI. Performance ANalysis of Cooling Tower: A Review. Int J Innov Res Sci Eng 2016;2:96–102. [6] Dhorat A, Al-Obaidi MA, Mujtaba IM. Dynamic modelling and operational optimisation of natural draft cooling towers. Therm Sci Eng Prog 2019;9:30–43. doi:10.1016/j.tsep.2018.10.013.
  • [7] Arunkumar S, Sivakumar, D. B. Senthilkumar T. Fabrication and Performance Analysis of Cooling Tower. Int J Eng Sci Comput 2016:5359–62.
  • [8] Kumar D, Zehra T, Junejo A, Bhanbhro SA, Basit M. 4E (Energy, Exergy, Economic and Environmental) Analysis of the Novel Design of Wet Cooling Tower. J Therm Eng 2020;6:252–67. doi:10.18186/thermal.710981.
  • [9] Merkel F. Evporative cooling. Z Verein Deutsch Ingen 1925;70:123–8.
  • [10] Miao W, Jin W, Jiajin W, Cheng SHI. Contrastive Analysis of Cooling Performance between a High-level Water Collecting Cooling Tower and a Typical Cooling Tower. J Therm Sci 2018;27:39–47.
  • [11] Kl-oppers JC, Kroger DG. Cooling tower performance evaluation: Merkel, Poppe, and eNTU methods of analysis. J Eng Gas Turb Power 2005;127:1–7.
  • [12] Khan JR, Zubair SM. Performance characteristics of counter flow wet cooling towers. Energy Convers Manag 2002;44:2073–91.
  • [13] Prasad M. Economic upgradation and optimal use of multi-cell cross flow evaporative water cooling tower through modular performance appraisal. Appl Therm Eng 2003;24:579–93.
  • [14] Hajidavalloo E, Shakeri R, Mehrabian MA. Thermal performance of cross flow cooling towers in variable wet bulb temperature. Energy Convers Manag 2010;51:1298–303.
  • [15] SPX. Cooling Tower Performance: Basic Theory and Practice. Overland Park: SPX Cooling Technologies Inc..; 2013.
  • [16] SPX. Cooling Tower Fundamentals. U.S.A.: SPX Cooling Technologies Inc..; 2009.
  • [17] Olatunji O, Akinlabi S, Ajayi O, Madushele N. A survey of Artificial Neural Network-based Prediction ModelsJ for Thermal Properties of Biomass. Procedia Manuf 2019;33:184–91. doi:10.1016/j.promfg.2019.04.103.
  • [18] Adedeji PA, Akinlabi S, Ajayi O, Madushele N. Non-linear autoregressive neural network (NARNET) with SSA filtering for a university enegy consumption forecast. 16th Glob. Conf. Sustain. Manuf. Sustain. Manuf. Glob. Circ. Econ., 2019, p. 176–83. doi:.1037//0033-2909.I26.1.78.
  • [19] Adedeji P, Madushele N, Akinlabi S. Adaptive Neuro-fuzzy Inference System ( ANFIS ) for a multi-campus institution energy consumption forecast in South Africa 2018:950–8.
  • [20] Guo Q, Qi X, Wei Z, Yin Q, Sun P, Guo P, et al. Modeling and characteristic analysis of fouling in a wet cooling tower based on wavelet neural networks. Appl Therm Eng 2019;152:907–16. doi:10.1016/j.applthermaleng.2019.02.041.
  • [21] Hosoz M. Performance prediction of a cooling tower using artificial neural network. Energy Convers Manag 2007;48:1349–59. doi:10.1016/j.enconman.2006.06.024.
  • [22] Homod RZ. Review on the HVAC System Modeling Types and the Shortcomings of Their Application. J Energy 2013:1–10.
  • [23] Rashidi MM, Bég OA, Parsa AB, Nazari F. Analysis and optimization of a transcritical power cycle with regenerator using artificial neural networks and genetic algorithms. Proc Inst Mech Eng Part A J Power Energy 2011;225:701–17. doi:10.1177/0957650911407700.
  • [24] Rashidi MM, Ali M, Freidoonimehr N, Nazari F. Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm. Energy 2013;55:497–510. doi:10.1016/j.energy.2013.01.036.
  • [25] Rashidi MM, Galanis N, Nazari F, Parsa AB, Shamekhi L. Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network Clausius Rankine cycle. Energy 2011;36:5728–40. doi:10.1016/j.energy.2011.06.036.
  • [26] Zhang Y, Zhang H, Wang Y, You S, Zheng W. Optimal configuration and operating condition of counter flow cooling towers using particle swarm optimization algorithm. Appl Therm Eng 2019;151:318–27. doi:10.1016/j.applthermaleng.2019.01.097.
  • [27] Gao M, Sun F, Zhou S, Shi Y, Zhao Y, Wang N. Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions. Int J Therm Sci 2009;48:583–9. doi:10.1016/j.ijthermalsci.2008.03.012.
  • [28] Qi X, Liu Z, Li D. Numerical simulation of shower cooling tower based on artificial neural network. Energy Convers Manag 2008;49:724–32. doi:10.1016/j.enconman.2007.07.032.
  • [29] Kalatuzov VA. A procedure for constructing the normative characteristics of chimney-type evaporative cooling towers from the results of field measurements. Therm Eng 2007;54:901–5. doi:10.1134/s0040601507110092.
  • [30] Nasrabadi M, Finn DP. Mathematical modeling of a low temperature low approach direct cooling tower for the provision of high temperature chilled water for conditioning of building spaces. Appl Therm Eng 2014;64:273–82.
  • [31] Adetula Y, Bamidele EA. Timely Completion of Nigerian Iron and Steel Projects: the imperative of technical manpower development. 29th Annu. Conf. AGM Niger. Metall. Soc. Ajaokuta Steel Co., 2013.
  • [32] Ajaokuta Steel Company Limited. Company Overview : Ajaokuta Steel Company. Ajaokuta Steel Co 2019. http://www.ajaokutasteel.com/site/pagef.php?cnt=Company Overview (accessed May 11, 2019).
  • [33] Kroger DG. Air-Cooled Heat Exchangers and Cooling Towers. Tulsa, Oklahoma: PennWell Corp..; 2004.
  • [34] Kashani MMH, Dobrego K V. Heat and mass transfer in natural draft cooling towers. J Eng Phys Thermophys 2013;86:1072–82. doi:10.1007/s10891-013-0930-z.
  • [35] Chen X, Sun F, Chen Y, Gao M. Novel method for improving the cooling performance of natural draft wet cooling towers. Appl Therm Eng 2019;147:562–70. doi:10.1016/j.applthermaleng.2018.10.076.
  • [36] Akkaya A V. Performance analyzing of an organic rankine cycle under different ambient conditions. J Therm Eng 2017;3:1498–504. doi:10.18186/journal-of-thermal-engineering.338897.
  • [37] Naik BK, Muthukumar P. A novel approach for performance assessment of mechanical draft wet cooling towers. Appl Therm Eng 2017;121:14–26. doi:10.1016/j.applthermaleng.2017.04.042.
  • [38] Naik BK, Choudhary V, Muthukumar P, Somayaji C. Performance Assessment of a Counter Flow Cooling Tower - Unique Approach. Energy Procedia 2017;109:243–52. doi:10.1016/j.egypro.2017.03.056.
  • [39] Kloppers JC, Kroger DG. Cooling Tower Performance Evaluation : Merkel , Poppe , and e -NTU Methods of Analysis. J Eng Gas Turbines Power 2005;127:1–7. doi:10.1115/1.1787504.
  • [40] Ren C. An Analytical Approach to the Heat and Mass Transfer Processes in Counterflow Cooling. Trans ASME 2006;128:1–7. doi:10.1115/1.2352780.
  • [41] Kloppers JC, Kroger DG. A Critical Investigation into the Heat and Mass Transfer Analysis of Counterflow Wet-Cooling Towers. Int J Heat Mass Transf 2005:765–77.
  • [42] ASHRAE. Fundamentals: ASHRAE Handbook. Atlanta: American Society of Heating, Refrigeration and AirConditioning Engineers; 2009.
  • [43] Nicklas S, Strehlow G, Duda SW, Simmonds P. 2016 HVAC Systems and Equipment. ASHRAE; 2016.
  • [44] Bamimore OT. Paramteric Effects on the Performance of an Industrial Cooling Tower (A Case Study of Ajaokuta Power Plant). 2016.
  • [45] Zou Z, Guan Z, Gurgenci H, Lu Y. Solar enhanced natural draft dry cooling tower for geothermal power applications. Sol Energy 2012;86:2686–94. doi:10.1016/j.solener.2012.06.003.
  • [46] Ge W, Fan J, Liu CX, Li W, Chen G, Zhao Y. Critical impact factors on the cooling performance design of natural draft dry cooling tower and relevant optimization strategies. Appl Therm Eng 2019;154:614–27. doi:10.1016/j.applthermaleng.2019.03.008.
  • [47] Dong P, Li X, Hooman K, Sun Y, Li J, Guan Z, et al. The crosswind effects on the start-up process of natural draft dry cooling towers in dispatchable power plants. Int J Heat Mass Transf 2019;135:950–61. doi:10.1016/j.ijheatmasstransfer.2019.02.039.
  • [48] Dang Z, Zhang Z, Gao M, He S. Numerical simulation of thermal performance for super large-scale wet cooling tower equipped with an axial fan. Int J Heat Mass Transf 2019;135:220–34. doi:10.1016/j.ijheatmasstransfer.2019.01.111.
  • [49] Klimanek A. Numerical Modelling of Natural Draft Wet-Cooling Towers. Arch Comput Methods Eng 2013;20:61–109. doi:10.1007/s11831-013-9081-9.
  • [50] Zhou Y, Gao M, Long G, Zhang Z, Dang Z, He S. Experimental study regarding the effects of forced ventilation on the thermal performance for super-large natural draft wet cooling towers. Appl Therm Eng 2019;155:40–8. doi:10.1016/j.applthermaleng.2019.03.149.
  • [51] Babar H, Sajid MU, Ali HM. Viscousity of Hybrid Nanofluids: a critical review. J Therm Sci 2019:1–49. [52] Sajid MU, Ali HM. Recent advances in application of nanofluids in heat transfer devices : A critical review. Renew Sustain Energy Rev 2019;103:556–92. doi:10.1016/j.rser.2018.12.057.
  • [53] Hussein AM, Bakar RA, Kadirgama K. Case Studies in Thermal Engineering Study of forced convection nanofluid heat transfer in the automotive cooling system. Case Stud Therm Eng 2014;2:50–61. doi:10.1016/j.csite.2013.12.001
There are 51 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

O. T. Bamimore This is me

S.o. Enibe This is me

Paul. A. Adedeji This is me 0000-0002-2212-0623

Publication Date May 1, 2021
Submission Date April 15, 2019
Published in Issue Year 2021 Volume: 7 Issue: 4

Cite

APA Bamimore, O. T., Enibe, S., & Adedeji, P. A. (2021). PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER. Journal of Thermal Engineering, 7(4), 904-917. https://doi.org/10.18186/thermal.930791
AMA Bamimore OT, Enibe S, Adedeji PA. PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER. Journal of Thermal Engineering. May 2021;7(4):904-917. doi:10.18186/thermal.930791
Chicago Bamimore, O. T., S.o. Enibe, and Paul. A. Adedeji. “PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER”. Journal of Thermal Engineering 7, no. 4 (May 2021): 904-17. https://doi.org/10.18186/thermal.930791.
EndNote Bamimore OT, Enibe S, Adedeji PA (May 1, 2021) PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER. Journal of Thermal Engineering 7 4 904–917.
IEEE O. T. Bamimore, S. Enibe, and P. A. Adedeji, “PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER”, Journal of Thermal Engineering, vol. 7, no. 4, pp. 904–917, 2021, doi: 10.18186/thermal.930791.
ISNAD Bamimore, O. T. et al. “PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER”. Journal of Thermal Engineering 7/4 (May 2021), 904-917. https://doi.org/10.18186/thermal.930791.
JAMA Bamimore OT, Enibe S, Adedeji PA. PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER. Journal of Thermal Engineering. 2021;7:904–917.
MLA Bamimore, O. T. et al. “PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER”. Journal of Thermal Engineering, vol. 7, no. 4, 2021, pp. 904-17, doi:10.18186/thermal.930791.
Vancouver Bamimore OT, Enibe S, Adedeji PA. PARAMETRIC EFFECTS ON THE PERFORMANCE OF AN INDUSTRIAL COOLING TOWER. Journal of Thermal Engineering. 2021;7(4):904-17.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK http://eds.yildiz.edu.tr/journal-of-thermal-engineering