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All issues Volume 17 / No 3 (2016) Mechanics & Industry, 17 3 (2016) 307 References
Open Access
Issue
Mechanics & Industry
Volume 17, Number 3, 2016
Article Number 307
Number of page(s) 8
DOI https://doi.org/10.1051/meca/2015062
Published online 15 February 2016
  1. M.C. Campos, J.V.C. Vargas, J.C. Ordonez, Thermodynamic optimization of a Stirling engine, Energy 44 (2012) 902–910 [CrossRef] [Google Scholar]
  2. S.C. Costa, H. Barrutia, J.A. Esnaola, M. Tutar, Numerical study of the pressure drop phenomena in wound woven wire matrix of a Stirling regenerator, Energy Convers. Manag. 67 (2013) 57–65 [CrossRef] [Google Scholar]
  3. D. Sanchez, R. Chacartegui, M. Torres, T. Sanchez, Stirling based fuel cell hybrid systems: An alternative for molten carbonate fuel cell, J. Power Sources 192 (2009) 84–93 [CrossRef] [Google Scholar]
  4. B. Kongtragool, S. Wongwises, A review of solar-powered Stirling engines and low temperature differential Stirling engines, Renew. Sust. Energy Rev. 7 (2003) 131–154 [Google Scholar]
  5. E. Prodesser, Electricity production in rural villages with biomass Stirling engines, Renew. Energy 16 (1999) 1049–52 [Google Scholar]
  6. A. Sripakagorn, C. Srikam, Design and performance of a moderate temperature difference Stirling engine, Renew. Energy 36 (2011) 1728–33 [CrossRef] [Google Scholar]
  7. H. Karabulut, C. Cinar, E. Ozturk, H.S. Yucesu, Torque and power characteristics of a helium charged Stirling engine with a lever controlled displacer driving mechanism, Renew. Energy 35 (2010) 138–43 [CrossRef] [Google Scholar]
  8. C.H. Cheng, Y.J. Yu, Numerical model for predicting thermodynamic cycle and thermal efficiency of a beta-type Stirling engine with rhombic-drive mechanism, Renew. Energy 35 (2010) 2590–601 [CrossRef] [Google Scholar]
  9. W.L. Chen, K.L. Wong, L.W. Po, A numerical analysis on the performance of a pressurized twin power piston gamma-type Stirling engine, Energy. Convers. Manag. 62 (2012) 84–92 [Google Scholar]
  10. F. Formosa, G. Despesse, Analytical model for Stirling cycle machine design, Energy. Convers. Manag. 51 (2010) 1855–63 [Google Scholar]
  11. M.H. Ahmadi, S. Dehghani, A.H. Mohammadi, M. Feidt, Marco A. Barranco-Jimenez, Optimal Design of a Solar Driven Heat Engine Based on Thermal and Thermo- Economic Criteria, Energy. Convers. Manag. 75 (2013) 635–642 [Google Scholar]
  12. M.H. Ahmadi, H. Hosseinzade, H. Sayyaadi, A.H. Mohammadi, F. Kimiaghalam, Application of the Multi- Objective Optimization Method for Designing a Powered Stirling Heat Engine: Design with Maximized Power, Thermal Efficiency and Minimized Pressure Loss, Renew. Energy 60 (2013) 313–322 [Google Scholar]
  13. M.H. Ahmadi, A.H. Mohammadi, S. Dehghani, Evaluation of the Maximized Power of a Regenerative Endoreversible Stirling Cycle Using the Thermodynamic Analysis, Energy Convers. Manag. 76 (2013) 561– 570 [Google Scholar]
  14. M.H. Ahmadi, M.A. Ahmadi, S.A. Sadatsakkak, M. Feidt, Connectionist intelligent model estimates output power and torque of Stirling engine, Renew. Sust. Energy Rev. 50 (2015) 871–883 [Google Scholar]
  15. 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 Computing and Applications 22 (2013) 1141–1150 [Google Scholar]
  16. M.H. Ahmadi, A.H. Mohammadi, S.M. Pourkiaei, Optimisation of the thermodynamic performance of the Stirling engine, Int. J. Ambient Energy (ahead-of-print), (2014) 1–13 [Google Scholar]
  17. Toghyani, Somayeh, Alibakhsh Kasaeian, Mohammad H. Ahmadi, Multi-objective optimization of Stirling engine using non-ideal adiabatic method, Energy Convers. Manag. 80 (2014) 54–62 [CrossRef] [Google Scholar]
  18. Ahmadi, Mohammad H., Amir H. Mohammadi, Saeed Dehghani, Marco A. Barranco-Jiménez, Multi-objective thermodynamic-based optimization of output power of Solar Dish-Stirling engine by implementing an evolutionary algorithm, Energy Convers. Manag. 75 (2013) 438–445 [CrossRef] [Google Scholar]
  19. 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]
  20. M.H. Ahmadi, M. Mehrpooya, N. Khalilpoor, Artificial neural networks modelling of the performance parameters of the Stirling engine, Int. J. Am. Energy (2014) (ahead-of-print), 1–7 [Google Scholar]
  21. A. Kecebas, I. Yabanova, M. Yumurtacı, Artificial neural network modeling of geothermal district heating system thought exergy analysis, Energy Convers. Manag. 64 (2012) 206–12 [CrossRef] [Google Scholar]
  22. M. KianiDehKiani, B. Ghobadian, T. Tavakoli, A.M. Nikbakht, G. Najafi, Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends, Energy 35 (2010) 65–9 [CrossRef] [Google Scholar]
  23. R.M. Balabin, E.I. Lomakina, R.Z. Safieva, Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy, Fuel 90 (2011) 2007–15 [CrossRef] [Google Scholar]
  24. T.F. Yusaf, D.R. Buttsworth, K.H. Saleh, B.F. Yousif, CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network, Appl. Energy 87 (2010) 1661–9 [CrossRef] [Google Scholar]
  25. B. Ghobadian, H. Rahimi, A.M. Nikbakht, G. Najafi, T.F. Yusaf, Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network, Renew. Energy 34 (2009) 976–82 [CrossRef] [Google Scholar]
  26. Y. Cay, A. Cicek, F. Kara, S. Sagiroglu, Prediction of engine performance for an alternative fuel using artificial neural network, Appl. Therm. Eng. 37 (2012) 217–25 [CrossRef] [Google Scholar]
  27. M. Golcu, Y. Sekmen, P. Erduranli, S. Salman, Artificial neural network based modeling of variable valve timing in a spark ignition engine, Appl. Energy 81 (2005) 187–97 [CrossRef] [Google Scholar]
  28. C. Sayin, H. 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]
  29. K. Atashkari, N Nariman-Zadeh, M. Golcu, A. Khalkhali, A. Jamali, Modeling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms, Energy Convers. Manag. 48 (2007) 1029–41 [Google Scholar]
  30. I.P. Koronaki, E. Rogdakis, T. Kakatsiou, Thermodynamic analysis of an open cycle solid desiccant cooling system using Artificial Neural Network, Energy Convers. Manag. 60 (2012) 152–60 [CrossRef] [Google Scholar]
  31. B. ZareNezhad, A. Aminian, Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model, Energy Convers. Manag. 52 (2011) 911–6 [CrossRef] [Google Scholar]
  32. G. Jahedi, M.M. Ardehali, Wavelet based artificial neural network applied for energy efficiency enhancement of decoupled HVAC system, Energy Convers. Manag. 54 (2012) 47–56 [CrossRef] [Google Scholar]
  33. H.R. AmeriSiahoui, A.R. Dehghani, M. Razavi, M.R. Khani, Investigation of thermal stratification in cisterns using analytical and Artificial Neural Networks methods, Energy Convers. Manag. 52 (2011) 505–11 [CrossRef] [Google Scholar]
  34. S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review, Renew. Sustain. Energy Rev. 5 (2001) 373–401 [Google Scholar]
  35. G.L. Ward, Performance characteristics of the Stirling engine. MSc thesis, University of Bath, 1972 [Google Scholar]
  36. J.I. Prieto, M.A. Gonzalez, C. Gonzalez, J Fano., A new equation representing the performance of kinematic Stirling engines, Proc. Instn. Mech. Eng. Part C 214 (2000) 449–464 [CrossRef] [Google Scholar]
  37. I.A . Ozkan, I. Saritas, S. Yaldiz, Prediction of cutting forces and tool tip temperature in turning using Artificial Neural Network, In: 5th Int. Adv. Tech. Symp., Karabuk, Turkey, 13–15 May, 2009 [Google Scholar]
  38. Y. Oguz, M. Dede, Speed estimation of vector controlled squirrel cage a synchronous motor with artificial neural networks, Energy Convers. Manag. 52 (2011) 675–86 [CrossRef] [Google Scholar]
  39. D. Anderson, G. McNeill, Artificial neural networks technology, NewYork: Kaman Sciences Corporation, 1992 [Google Scholar]
  40. A. Sencan, II Kose, R. Selbas, Prediction of thermo physical properties of mixed refrigerants using artificial neural network, Energy Convers. Manag. 52 (2011) 958–74 [CrossRef] [Google Scholar]
  41. Y.L. Tu, T.J. Chang, C.L. Chen, Y.J. Chang, Estimation of monthly wind power outputs of WECS with limited record period using artificial neural networks, Energy Convers. Manag. 59 (2012) 114–21 [CrossRef] [Google Scholar]
  42. G. Landeras, J.J. Lopez, O. Kisi, J. Shiri, Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain), Energy Convers. Manag. 62 (2012) 1–13 [CrossRef] [Google Scholar]
  43. H.M. Hasanien, FPGA implementation of adaptive ANN controller for speed regulation of permanent magnet stepper motor drives, Energy Convers. Manag. 52 (2011) 1252–7 [CrossRef] [Google Scholar]
  44. B. Krose, P. van der Smagt, An introduction neural networks, 8th edition, Amsterdam: Oberpfaffenhofen, 1996 [Google Scholar]
  45. M.T. Hagan, H.B. Demuth, M. Beale, Neural network design, United States of America: PWS Publishing Company, 1996 [Google Scholar]
  46. M. Al-Assadi, H.A. El Kadi, I.M. Deiab, Using artificial neural networks to predict the fatigue life of different composite materials including the stress ratio effect, Appl. Compos. Mater. 18 (2011) 297–309 [CrossRef] [Google Scholar]
  47. Yasar Onder Ozgoren Selim Cetinkaya Suat Sarıdemir Adem Cicek Fuat Kara, Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network, Energy Convers. Manag. 67 (2013) 357–368 [Google Scholar]
  48. 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]
  49. K. Hornick, M. Stinchcombe, H. White, Neural Network 2 (1989) 359–66 [Google Scholar]
  50. M. Brown, C. Harris, Neural fuzzy adaptive modeling and control, EnglewoodCliffs (NJ), Prentice-Hall, 1994 [Google Scholar]
  51. M.A. Ahmadi, S.R. Shadizadeh, New Approach for Prediction of Asphaltene Precipitation due to Natural Depletion by Using Evolutionary Algorithm Concept, J. Fuel 102 (2012) 716–723 [CrossRef] [Google Scholar]
  52. S. Zendehboudi, M.A. Ahmadi, A. Bahadori, A. Shafiei, T. Babadagli, A Developed Smart Technique to Predict Minimum Miscible Pressure-EOR Implication, Canadian J. Chem. Eng. (2013) 1–13 [Google Scholar]
  53. 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. DOI: 10.1021/ie301949c [Google Scholar]
  54. M.A. Ahmadi, M. Golshadi, Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion, J. Petroleum Sci. Eng. 98–99 (2012) 40–49 [CrossRef] [Google Scholar]
  55. M.A. Ahmadi, M. Ebadi, A. Shokrollahi, S.M.J. Majidi, Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir, Appl. Soft Comput. 13 (2013) 1085–1098 [Google Scholar]
  56. M.A. Ahmadi, Neural Network Based Unified Particle Swarm Optimization for Prediction of Asphaltene Precipitation, Fluid Phase Equilibria 314 (2012) 46–51 [CrossRef] [Google Scholar]
  57. S. Zendehboudi, M.A. Ahmadi, L. James, I. Chatzis, Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization, Energy & Fuels 26 (2012) 3432–3447 [CrossRef] [Google Scholar]
  58. M.A. Ahmadi, S. Zendehboudi, A. Lohi, A. Elkamel, I. Chatzis, Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization, Geophys. Prospect. 61 (2013) 582–598 [CrossRef] [Google Scholar]
  59. M.A. Ahmadi, Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm, J. Petroleum Exploration Prod. Technol. 1 (2011) 99–106 [CrossRef] [Google Scholar]
  60. M.A. Ahmadi, M. Ebadi, Evolving Smart Approach for Determination Dew Point Pressure through Condensate Gas Reservoirs, Fuel 117 (2014) 1074–1084 [Google Scholar]
  61. M.A. Ahmadi, M. Ebadi, S.M. Hosseini, Prediction Breakthrough Time of Water Coning in the Fractured Reservoirs by Implementing Low Parameter Support Vector Machine Approach, Fuel 117 (2014) 579–589 [Google Scholar]

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