Open Access
Issue
Mechanics & Industry
Volume 18, Number 2, 2017
Article Number 216
Number of page(s) 10
DOI https://doi.org/10.1051/meca/2016034
Published online 17 February 2017
  1. W. Haaf, K. Friedrich, G. Mayr, J. Schlaich, Solar chimneys, part I: principle and construction of the pilot plant in Manzanares, Int. J. Solar Energy 2 (1983) 3–20 [CrossRef] [Google Scholar]
  2. W. Haaf, Solar chimneys, part II: preliminary test results from the Manzanares pilot plant, Int. J. Solar Energy 2 (1984) 141–161 [CrossRef] [Google Scholar]
  3. J. Schlaich, The Solar Chimney, Electricity from the Sun, Deutsche Verlags-Anstalt, Stuttgart, 1994 [Google Scholar]
  4. A.J. Gannon, T.W. Von Backstrom, Controlling and maximizing solar chimney power output, In: Proceedings of the 1st International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Kruger Park, South Africa, 2002 [Google Scholar]
  5. T.P. Fluri, T.W. Von Backstrom, Comparison of modelling approaches and layouts for solar chimney turbines, Solar Energy 82 (2008) 239–246 [CrossRef] [Google Scholar]
  6. M.A. Bernardes, D.S. Voß, G. Weinrebe, Thermal and technical analyses of solar chimneys, Solar Energy 75 (2003) 511–524 [CrossRef] [Google Scholar]
  7. E. Bilgen, J. Rheault, Solar chimney power plants for high latitudes, Solar Energy 79 (2005) 449–458 [CrossRef] [Google Scholar]
  8. J.P. Pretorius, D.G. Kroger, Critical evaluation of solar chimney power plant performance, Solar Energy 80 (2006) 535–544 [CrossRef] [Google Scholar]
  9. A. Koonsrisuk, T. Chitsomboon, Dynamic similarity in solar chimney modeling, Solar Energy 81 (2007) 1439–1446 [CrossRef] [Google Scholar]
  10. A. Koonsrisuk, T. Chitsomboon, A single dimensionless variable for solar chimney power plant modeling, Solar Energy 83 (2009) 2136–2143 [CrossRef] [Google Scholar]
  11. X. Zhou, J. Yang, B. Xiao, G. Hou, Experimental study of temperature field in a solar chimney power setup, Appl. Thermal Eng. 27 (2007) 2044–2050 [CrossRef] [Google Scholar]
  12. T.P. Fluri, J.P. Pretorius, C. Van Dyk, T.W. Von Backstrom, D.G. Kroger, G. Van Zijl, Cost analysis of solar chimney power plants, Solar Energy 83 (2009) 246–256 [CrossRef] [Google Scholar]
  13. R. Petela, Thermodynamic study of a simplified model of the solar chimney power plant, Solar Energy 83 (2009) 94–107 [CrossRef] [Google Scholar]
  14. X. Zhou, J. Yang, B. Xiao, G. Hou, F. Xing, Analysis of chimney height for solar chimney power plant, Appl. Thermal Eng. 29 (2009) 178–185 [CrossRef] [Google Scholar]
  15. A. Koonsrisuk, S. Lorente, A. Bejan, Constructal solar chimney configuration, Int. J. Heat Mass Transfer 53 (2010) 327–333 [Google Scholar]
  16. M. Bernardes, W. Theodor, T.W. Von Backstrom, Evaluation of operational control strategies applicable to solar chimney power plants, Solar Energy 84 (2010) 277–288 [CrossRef] [Google Scholar]
  17. T. Chergui, S. Larbi, A. Bouhdjar, Thermo-hydrodynamic aspect analysis of flows in solar chimney power plants-A case study, Renew. Sustain. Energy Rev. 14 (2010) 1410–1418 [CrossRef] [Google Scholar]
  18. G. Xu, T. Ming, Y. Pan, F. Meng, C. Zhou, Numerical analysis on the performance of solar chimney power plant system, Energy Convers. Manage. 52 (2011) 876–883 [CrossRef] [Google Scholar]
  19. J.K. Afriyie, M.A. Nazha, H. Rajakaruna, F.K. Forson, Experimental investigations of a chimney-dependent solar crop dryer, Renew. Energy 34 (2009) 217–222 [CrossRef] [Google Scholar]
  20. J.K. Afriyie, H. Rajakaruna, M.A. Nazha, F.K. Forson, Simulation and optimization of the ventilation in a chimney-dependent solar crop dryer, Solar Energy 85 (2011) 1560–1573 [CrossRef] [Google Scholar]
  21. F. Cao, L. Zhao, L. Guo, Simulation of a sloped solar chimney power plant in Lanzhou, Energy Convers. Manage. 52 (2011) 2360–2366 [CrossRef] [Google Scholar]
  22. A. Koonsrisuk, Mathematical modeling of sloped solar chimney power plants, Energy 47 (2012) 582–589 [CrossRef] [Google Scholar]
  23. F. Cao, L. Zhao, H. Li, L. Guo, Performance analysis of conventional and sloped solar chimney power plants in China, Appl. Thermal Eng. 50 (2013) 582–592 [CrossRef] [Google Scholar]
  24. L. Zuo, Y. Zheng, Z. Li, Y. Sha, Solar chimneys integrated with sea water desalination, Desalination 276 (2011) 207–213 [CrossRef] [Google Scholar]
  25. J. Li, P. Guo, Y. Wang, Effects of collector radius and chimney height on power output of a solar chimney power plant with turbines, Renew. Energy 47 (2012) 2128 [Google Scholar]
  26. M. Hamdan, Analysis of solar chimney power plant utilizing chimney discrete model, Renew. Energy 56 (2013) 50–54 [CrossRef] [Google Scholar]
  27. E. Sanchez, T. Shibata, L.A. Zadeh, Genetic algorithms and fuzzy logic systems, World Scientific, River edge NJ, 1997 [Google Scholar]
  28. K. Kristinson, G. Dumont, System identification and control using genetic algorithms, J. IEEE Trans. Syst. Man. Cybern 22 (1992) 1033–46 [CrossRef] [Google Scholar]
  29. J. Koza, Genetic programming, on the programming of computers by means of natural selection, MIT Press, MA, Cambridge, 1992 [Google Scholar]
  30. H. Iba, T. Kuita, deH. Garis, T. Sator, System identification using structured genetic algorithms, In: Proceedings of the 5th international conference on genetic algorithms, ICGA’93, USA, 1993 [Google Scholar]
  31. K. Rodríguez-Vázquez, Multi-objective evolutionary algorithms in non-linear system identification, Ph.D. Thesis, University of Sheffield, Sheffield, UK, 1999 [Google Scholar]
  32. A.G. Ivakhnenko, Polynomial Theory of Complex Systems, IEEE Trans. Syst. Man Cybern SMC-1 (1971) 364–378 [Google Scholar]
  33. S.J. Farlow, Self-organizing method in modelling, GMDH type algorithm, Marcel Dekker Inc., 1984 [Google Scholar]
  34. J.A. Mueller, F. Lemke, Self-organizing data mining: an intelligent approach to extract knowledge from data, Pub. Libri, Hamburg, 2000 [Google Scholar]
  35. N. Nariman-zadeh, A. Darvizeh, M.E. Felezi, H. Gharababei, Polynomial modeling of explosive compaction process of metallic powders using GMDH-type neural networks and singular value decomposition, J. Model Simul. Mater. Eng. 10 (2002) 727–44 [CrossRef] [Google Scholar]
  36. C.M. Fonseca, P.J. Fleming, Nonlinear system identification with multi-objective genetic algorithm proceedings of the 13th World congress of the international federation of automatic control, Pergamon Press, San Francisco, California, 1996, pp. 187–92 [Google Scholar]
  37. G.P. Liu, V. Kadirkamanathan, Multi-objective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms, IEEE Proc. Control Theory Appl. 146 (1999) 373–82 [CrossRef] [Google Scholar]
  38. N. Nariman-Zadeh, A. Darvizeh, R. Ahmad-Zadeh, Hybrid Genetic Design of GMDH-Type Neural Networks Using Singular Value Decomposition for Modelling and Prediction of the Explosive Cutting Process, Proc. I MECH E Part B. J. Eng Manuf. 217 (2003) 779–790 [CrossRef] [Google Scholar]
  39. V.W. Porto, Evolutionary computation approaches to solving problems in neural computation, In: Handbook of evolutionary computation, edited by Back D.B. Fogel, Michalewicz Z, Oxford University Press, Institute of Physics Publishing and New York, 1997, pp. D1.2, 1–6 [Google Scholar]
  40. X. Yao, Evolving artificial neural networks, Proc. IEEE 87 (1999) 1423–47 [Google Scholar]
  41. E.F. Vasechkina, V.D. Yarin, Evolving polynomial neural network by means of genetic algorithm: some application examples, Complex Int. 9 (2001) [Google Scholar]
  42. X. Yao, Evolving Artificial Neural Networks, Proc. IEEE 87 (1999) 1423–1447 [Google Scholar]
  43. N. Nariman-zadeh, K. Atashkari, A. Jamali, A. Pilechi, X. Yao, Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms, J. Eng. Optim. 37 (2005) 43–62 [CrossRef] [Google Scholar]
  44. B.R. Munson, D.F. Young, T.O. Okiishi, Fundamentals of fluid mechanics, 2nd edition, John Wiley & Sons, Inc, 1994, Chap. 2 [Google Scholar]
  45. J. Schlaich, R. Bergermann, W. Schiel, G. Weinrebe, Design of commercial solar updraft tower systems utilization of solar induced convective flows for power generation, J. Solar Energy Eng. 127 (2005) 117–124 [Google Scholar]
  46. K. Atashkari, Nariman-Zadeh, N, Jamali, A, Pilechi, A, Thermodynamic Pareto optimization of turbojet using multi-objective genetic algorithm, Int. J. Thermal Sci. 44 (2005) 1061–1071 [CrossRef] [Google Scholar]
  47. K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali, A. Jamali, Modelling and multi-objective optimization of a variable valve-timing spark- ignition engine using polynomial neural networks and evolutionary algorithms, Energy Convers. Manage. 48 (2007) 1029–1041 [CrossRef] [Google Scholar]
  48. A. Jamali, N. Nariman-zadeh, A. Darvizeh, A. Masoumi, S. Hamrang, Multi- objective evolutionary optimization of polynomial neural networks for model- ling and prediction of explosive cutting process, Eng. Appl. Artif. Intell. 22 (2009) 67–687 [CrossRef] [Google Scholar]
  49. J.I.E. Lin, C.T. Cheng, K.W. Chau, Using support vector machines for long-term discharge prediction, Hydrolo. Sci. J. 51 (2006) 599–612 [Google Scholar]
  50. 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]
  51. R. Shirmohammadi, B. Ghorbani, M. Hamedi, M.-H. Hamedi, L.M. Romeo, Optimization of mixed refrigerant systems in low temperature applications by means of group method of data handling (GMDH), J. Natural Gas Sci. Eng. 26 (2015) 303–312 [CrossRef] [Google Scholar]
  52. S.M. Pourkiaei, H.A. Mohammad, S. Mahmoud Hasheminejad, Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network, Mech. Ind. 17 (2016) 105 [CrossRef] [EDP Sciences] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.