Application of Response Surface Methodology to Predict the Optimized Input Quantities of Parabolic Trough Concentrator

*Vijayan Gopalsamy orcid scopus  -  KSK College of Engineering and Technology, India
Ramalingam Senthil orcid scopus  -  Department of Mechanical Engineering, SRM Institute of Science and Technology,Kattankulathur,Chennai, India
Muthukrishnan Varatharajulu orcid scopus  -  Department of Mechanical Engineering, National Institute of Technology-Trichy, Tamilnadu, India
Rajasekaran Karunakaran  -  Department of Mechanical Engineering, Anna University, Chennai, Tamilnadu, India
Received: 14 May 2020; Revised: 29 Jun 2020; Accepted: 5 Jul 2020; Published: 15 Oct 2020; Available online: 8 Jul 2020.
Open Access License URL:

Citation Format:
Article Info
Section: Original Research Article
Language: EN
Statistics: 797 330
This work carries out a numerical investigation on aluminum oxide/de-ionized water nanofluid based shield-free parabolic trough solar collector (PTSC) system to evaluate, validate, and optimize the experimental output data. A numerical model is developed using response surface methodology (RSM) for evaluation (identifying influencing parameters and its level) and single objective approach (SOA) technique of desirability function analysis (DFA) for optimization. The experimental data ensured that global efficiency was enhanced from 61.8% to 67.0% for an increased mass flow rate from 0.02 kg/s to 0.06 kg/s, respectively. The overall deviation between experimental and numerical is only 0.352%. The energy and exergy error is varied from 3.0% to 6.0%, and the uncertainty of the experiment is 3.1%. Based on the desirability function analysis, the maximum and minimum efficiencies are 49.7% and 84.9%, as per the SOA technique. This numerical model explores the way to enhance global efficiency by 26.72%.©2020. CBIORE-IJRED. All rights reserved
Keywords: parabolic trough solar collector; nanofluid; optimization; response surface methodology; shield-free receiver

Article Metrics:

  1. Alireza, A. (2016). Optimization of solar and wind energy systems: A survey. International Journal of Ambient Energy, doi: 10.1080/01430750.2016.1155493
  2. Amin, R., &Mehran, A. (2020). A comparative study of different optimized mirrors layouts of linear fresnel concentrators on annual energy and exergy efficiencies. International Journal of Ambient Energy, doi: 10.1080/01430750.2020.1758780
  3. Anissa, G., Hatem, M., &Philippe, B. (2015). Numerical study and optimization of parabolic trough solar collector receiver tube. Journal of Solar Energy Engineering-ASME, 137, A051003-1; doi: 10.1115/1.4030849
  4. ASHRAE Standard 93-2010, ASHRAE (2010), Atlanta (GA).
  5. Bruno, D., Antonio, J., &Joao F. (2014). Optimization of a seasonal storage solar system using Genetic Algorithms. Solar Energy, 101, 160-166; doi: 10.1016/j.solener.2013.12.031
  6. Cabello, JM., Cejudo, JM., Luque, M., Ruiz, F., Deb, K., &Tewari, R. (2011). Optimization of the size of a solar thermal electricity plant by means of genetic algorithms. Renewable Energy, 36 (11), 3146-3153; doi: 10.1016/j.renene.2011.03.018
  7. Cheng, ZD., He, YL., Cui, FQ., Du, BC., Zheng, ZJ., &Xu, Y. (2014). Comparative and sensitive analysis for parabolic trough solar collectors with a detailed Monte Carlo ray tracing optical model. Applied Energy, 115, 559-572; doi: 10.1016/j.apenergy.2013.11.001
  8. Cheng, ZD., He, YL., Xiao, J., Tao, YB., & Xu, J. (2010). Three dimensional numerical study of heat transfer characteristics in the receiver tube of parabolic trough solar collector. International Communications in Heat Transfer, 37, 782-787; doi: 10.1016/j.icheatmasstransfer.2010.05.002
  9. Dudley, VE., Kolb, GL., Mahoney, AR., Mancini, TR., Matthews, CW., Sloan, M., &Kearney, D. (1994). Test results: SEGS LS-2 Solar collector, Sandia National Laboratories, Albuquerque, USA, 139; 10.2172/70756.
  10. Farshad, SA., &Sheikholeslami, M. (2019). Simulation of nanoparticles second law treatment inside a solar collector considering turbulent flow. Physica A:Statistical Mechanics and its Applications, 525, 1-12; doi: 10.1016/j.physa.2019.03.089
  11. Hatami, M., & Jing, D. (2017). Optimization of wavy direct absorber solar collector (WDASC) using Al2O3-water nanofluid and RSM analysis. Applied Thermal Engineering, 121, 1040-1050. doi: 10.1016/j.applthermaleng.2017.04.137
  12. Jiangfeng, G., &Xiulan, H., (2016). Multi-parameter optimization design of parabolic trough solar receiver. Applied Energy, 98, 73-79; doi: 10.1016/j.applthermaleng.2015.12.041
  13. Kline, S., &Mcclintock, F. (1953). Describing uncertainties in single-sample experiments. Mechanical Engineering, 75(1), 3-8.
  14. Majedul, I., Azharul, K., Suvash., Saha., Sarah, M., Prasad, KD., &Yarlagadda, V. (2012). Three dimensional simulation of a parabolic trough concentrator thermal collector.In Proceeding of 50th Annual Conference-Australian Solar Energy Society, Melbourne, 1-12.
  15. Moffat, RJ. (1988). Describing the uncertainties in experimental results. Experimental Thermal and Fluid science, 1(1), 3-17; doi: 10.1016/0894-1777(88)90043-X
  16. Mohamed, A. (2014). Two dimension numerical modelling of receiver tube performance for concentrated solar power plant. Energy Procedia, 57, 551-560; doi: 10.1016/j.egypro.2014.10.209
  17. Mohsen, M., &Mostafa, ZM. (2018). Neural network modeling for accurate prediction of thermal efficiency of a flat plate solar collector working with nanofluids. International Journal of Ambient Energy, doi: 10.1080/01430750.2018.1525576
  18. Moradikazerouni, A., Hajizadeh, A., Safaei, MR., Afrand, M., Yarmand, H., &Zulkifli, NWBM. (2019). Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting. Physica A: Statistical Mechanics and its Applications, 521, 138-145; doi: 10.1016/j.physa.2019.01.051
  19. Petela, R. (2004). Exergy of undiluted thermal radiation, Solar Energy 74, 469-488; doi: 10.1016/S0038-092X(03)00226-3
  20. Rahmati, A., &Niazi, S. (2015). Application and comparison of different lattice Boltzmann methods on non-uniform meshes for simulation of micro cavity and micro channel flow. Journal of Computational Methods in Engineering, 34(1), 97-118.
  21. Reza, A., Ehsanolah, A., Rahim, M., Martin, O., Mojtaba, N., & Farzad, P. (2019). Optimization of combined cooling, heating and power (CCHP) systems incorporating the solar and geothermal energy: a review study. International Journal of Ambient Energy, doi: 10.1080/01430750.2019.1630299
  22. Risi., MM., &Laforgia, D. (2013). Modelling and optimization of transparent parabolic trough collector based on gas-phase nanofluids. Renewable Energy, 58, 134-139; doi: 10.1016/j.physa.2019.122146
  23. Saman, R., Masoud, B., Nader, R., &Javad AE. (2017). Steps optimization and productivity enhancement in a nanofluid cascade solar still. Renewable Energy, 118, 536-545; doi: 10.1016/j.renene.2017.11.048
  24. Sami, S. (2018). Impact of magnetic field on the enhancement of performance of thermal solar collectors using nanofluids. International Journal of Ambient Energy, doi: 10.1080/01430750.2018.1437561
  25. Sarafraz, MM., Tlili, I., Zhe, T., Mohsen, B., & Mohammad, RS. (2019). Smart optimization of a thermosyphon heat pipe for an evacuated tube solar collector using response surface methodology (RSM). Physica A : Statistical Mechanics and its Applications, 534, 122-146; doi: 10.1016/j.physa.2019.122146
  26. Senthil, R. (2019). Thermal performance of aluminum oxide based nanofluids in flat plate solar collector. International Journal of Engineering and Advanced Technology, 8(3), 445-448.
  27. Senthil, R., &Cheralathan, M. (2016). Enhancement of heat absorption rate of direct absorption solar collector using graphite nanofluid, International Journal of Chemtech Research, 9(9), 303-308.
  28. Senthil, R., & Cheralathan, M. (2019). Enhancement of the thermal energy storage capacity of a parabolic dish concentrated solar receiver using phase change materials. Journal of Energy Storage, 25(100841); doi: 10.1016/j.est.2019.100841
  29. Shrikant, C., &Guniram, R. (2018). Thermodynamic investigation of nano-phase change materials as heat transfer fluid- heat exchanger for thermal-energy storage in concentrating solar thermo-electric generation systems. Journal of Ambient Energy, doi: 10.1080/01430750.2018.1517691
  30. Tahereh, BG., & Ranjbar, AA. (2015). Geometric optimization of a nanofluid-based direct absorption solar collector using response surface methodology. Solar Energy, 122, 314-325; doi: 10.1016/j.solener.2015.09.007
  31. Tahereh, BG., &Ranjbar, AA. (2017). Thermal and exergy optimization of a nanofluid-based direct absorption solar collector. Renewable Energy,106, 274-287; doi: 10.1016/j.renene.2017.01.031
  32. Venkata Rao, R., &Hameer Singh, Keesari. (2019). Solar assisted heat engine systems: multi objective optimization and decision making. International Journal of Ambient Energy, doi: 10.1080/01430750.2019.1636870
  33. Vijayan, G., &Karunakaran, R. (2019). Performance evaluation of nanofluid on parabolic trough solar collector. Thermal Science, 24(2A), 853-864; doi: 10.2298/TSCI180509059G
  34. Vijayan, G., Karunakaran, R., Logesh, K., Sivasaravanan, S., &Metin, Kok. (2019). Influence of dimensionless parameter on deionized water-alumina nanofluid based parabolic trough solar collector. Recent Patents on Nanotechnology, 13(3), 206-221; doi: 10.2174/1872210513666190410123503
  35. Ze-Dong, C., Ya-Ling, H., Bao-Cun, D., Kun, W., &Qi, L. (2015). Geometric optimization on optical performance of parabolic trough solar collector systems using particle swarm optimization algorithm. Applied Energy, 148, 282-293; doi: 10.1016/j.apenergy.2015.03.079

No citation recorded.