Open Access
Issue
Mechanics & Industry
Volume 18, Number 4, 2017
Article Number 408
Number of page(s) 8
DOI https://doi.org/10.1051/meca/2017016
Published online 28 August 2017
  1. S.A. Kalogirou, Solar thermal collectors and applications, Prog. Energy Combust. 30 (2004) 231–295 [CrossRef] [Google Scholar]
  2. J.A. Harris, T.G. Lenz, Thermal performance of solar concentrator/cavity receiver systems, Solar Energy 34 (1985) 135–142 [CrossRef] [Google Scholar]
  3. A.M. Clausing, An analysis of convective losses from cavity solar central receiver, Solar Energy 27 (1981) 295–300 [CrossRef] [Google Scholar]
  4. P. Le Quere, F. Penot, M. Mirenayat, Experimental study of heat loss through natural convection from an isothermal cubic open cavity, Sandia Laboratory Report, 1981, SAN D81-8014 [Google Scholar]
  5. W. Huang, F. Huang, P. Hu, Z. Chen, Prediction and optimization of the performance of parabolic solar dish concentrator with sphere receiver using analytical function, Renew. Energy 53 (2013) 18–26 [CrossRef] [Google Scholar]
  6. W.G. Le Roux, T. Bello-Ochende, J.P. Meyer, The efficiency of an open-cavity tubular solar receiver for a small-scale solar thermal Brayton cycle, Energy Convers. Manag. 84 (2014) 457–470 [CrossRef] [Google Scholar]
  7. S.A. Kalogirou, C.C. Neocleous, C.N. Schizas, Artificial neural networks in modeling the heat-up response of a solar steam generation plant, in: Proceedings of the International Conference EANN'96, 1996, pp. 1–4 [Google Scholar]
  8. S.A. Kalogirou, S. Panteliou, A. Dentsoras, Modeling of solar domestic water heating systems using artificial neural networks, Solar Energy 65 (1999) 335–342 [CrossRef] [Google Scholar]
  9. S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review, Renew. Sustain. Energy Rev. 5 (2001) 373–401 [CrossRef] [Google Scholar]
  10. A. Sözen, T. Menlik, S. Ünvar, Determination of efficiency of flat-plate solar collectors using neural network approach, Exp. Syst. Appl. 35 (2008) 1533–1539 [CrossRef] [Google Scholar]
  11. S.A. Kalogirou, Artificial neural networks and genetic algorithms for the modeling, simulation, and performance prediction of solar energy systems, in: Assessment and simulation tools for sustainable energy systems, Green Energy and Technology, Vol. 129, Chapter 7, 2013, pp. 225–245 [CrossRef] [Google Scholar]
  12. W. Yaïci, E. Entchev, Performance prediction of a solar thermal energy system using artificial neural networks, Appl. Therm. Eng. 73 (2014) 1348–1359 [CrossRef] [Google Scholar]
  13. S.A. Kalogirou, E. Mathioulakis, V. Belessiotis, Artificial neural networks for the performance prediction of large solar systems, Renew. Energy 63 (2014) 90–97 [CrossRef] [Google Scholar]
  14. S. Amirkhani, Sh. Nasirivatan, A.B. Kasaeian, A. Hajinezhad, ANN and ANFIS models to predict the performance of solar chimney power plants, Renew. Energy 83 (2015) 597–607 [CrossRef] [Google Scholar]
  15. Y.A. Cengel, Heat and mass transfer, 3rd ed., McGraw-Hill, Nevada, 2006 [Google Scholar]
  16. A. Baghernejad, M. Yaghoubi, Thermoeconomic methodology of analysis and optimization of a hybrid solar thermal power plant, Int. J. Green Energy 10 (2013) 588–609 [CrossRef] [Google Scholar]
  17. S.A. Kalogirou, Applications of artificial neural networks for energy systems, Appl. Energy 67 (2000) 17–35 [CrossRef] [Google Scholar]
  18. A.K. Tripathy, S. Mohapatra, S. Beura, G. Pradhan, Weather forecasting using ANN and PSO, Int. J. Scient. Eng. Res. 2 (2011) 1–5 [EDP Sciences] [Google Scholar]
  19. Y.O. Ozgoren, S. Cetinkaya, S. Sarıdemir, A. Cicek, F. Kara, Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network, Energy Convers. Manage. 67 (2013) 357–368 [CrossRef] [Google Scholar]
  20. C. Sayin, H.M. Ertunc, M. Hosoz, I. Kilicaslan, M. Canakci, Performance and exhaust emissions of a gasoline engine using artificial neural network, Appl. Therm. Eng. 27 (2007) 46–54 [CrossRef] [Google Scholar]
  21. K. Hornick, M. Stinchcombe, H. White, Neural Network 2 (1989) 359–366 [CrossRef] [Google Scholar]
  22. M. Brown, C. Harris, Neural fuzzy adaptive modeling and control, Prentice-Hall, Englewood Cliffs, NJ, 1994 [Google Scholar]
  23. S. Zendehboudi, M.A. Ahmadi, A. Bahadori, A. Shafiei, T. Babadagli, A developed smart technique to predict minimum miscible pressure − EOR implication, Canad. J. Chem. Eng. 91 (2013) 1–13 [Google Scholar]
  24. S. Zendehboudi, M.A. Ahmadi, O. Mohammadzadeh, A. Bahadori, I. Chatzis, Thermodynamic investigation of asphaltene precipitation during primary oil production, laboratory and smart technique, Ind. Eng. Chem. Res., 2017, doi: 10.1021/ie301949c [Google Scholar]
  25. M.H. Ahmadi, S.S. Ghare Aghaj, A. Nazeri, Prediction of power in solar Stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization, Neural Comput. Appl. 22 (2013) 1141–1150 [CrossRef] [Google Scholar]
  26. M.H. Ahmadi, M.A. Ahmadi, S.A. Sadatsakkak, M. Feidt, Connectionist intelligent model estimates output power and torque of Stirling engine, Renew. Sustain. Energy Rev. 50 (2015) 871–883 [CrossRef] [Google Scholar]
  27. M.H. Ahmadi, M.A. Ahmadi, M. Mehrpooya, M.A. Rosen, Using GMDH neural networks to model the power and torque of a Stirling engine, Sustainability 7 (2015) 2243–2255 [CrossRef] [Google Scholar]
  28. S.M. Pourkiaei, M.H. Ahmadi, S.M. Hasheminejad, Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network, Mech. Indust. 17 (2016) 105 [CrossRef] [EDP Sciences] [Google Scholar]
  29. S.A. Sadatsakkak, M.H. Ahmadi, M.A. Ahmadi, Implementation of artificial neural-networks to model the performance parameters of Stirling engine, Mech. Indust. 17 (2016) 307 [CrossRef] [EDP Sciences] [Google Scholar]
  30. M.H. Ahmadi, M.A. Ahmadi, M. Ashouri, F.R. Astaraei, R. Ghasempour, F. Aloui, Prediction of performance of Stirling engine using least squares support machine technique, Mech. Indust. 17 (2016) 506 [CrossRef] [EDP Sciences] [Google Scholar]
  31. A. Kasaeian, M. Mehrpooya, M. Aghaie, M.H. Ahmadi, Solar radiation prediction based on ICA and HGAPSO for Kuhin City, Iran, Mech. Indust. 17 (2016) 509 [CrossRef] [EDP Sciences] [Google Scholar]
  32. R. Loni, A.B. Kasaeian, E.A. Asli-Ardeh, B. Ghobadian, W.G. Le Roux, Performance study of a solar-assisted organic Rankine cycle using a dish mounted rectangular-cavity tubular solar receiver, Appl. Therm. Eng. 108 (2016) 1298–1309 [CrossRef] [Google Scholar]

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