Open Access
Mechanics & Industry
Volume 17, Number 6, 2016
Article Number 607
Number of page(s) 13
Published online 07 July 2016
  1. D. Saeidi, A. Sedaghat, P. Alamdari, A. Akbar Alemrajabi, Aerodynamic design and economical evaluation of site specific small vertical axis wind turbines, Appl. Energy 101 (2013) 765–775 [CrossRef] [Google Scholar]
  2. A. Sharifi, M.R.H. Nobari, Prediction of optimum section pitch angle distribution along wind turbine blades, Energy Convers. Manage. 67 (2013) 342–350 [CrossRef] [Google Scholar]
  3. B. Kim, W. Kim, S. Lee, S. Bae, Y. Lee, Developement and verification of a performance based optimal design software for wind turbine blades, Renew. Energy 54 (2013) 166–172 [CrossRef] [Google Scholar]
  4. R.W. Vesel Jr., J.J. McNamara, Performance enhancement and load reduction of a 5 MW wind turbine blade, Renew. Energy 66 (2014) 391–401 [CrossRef] [Google Scholar]
  5. R.K. Singh, M. Rafiuddin Ahmed, Blade design and performance testing of a small wind turbine rotor for low wind speed applications, Renew. Energy 50 (2013) 812–819 [CrossRef] [Google Scholar]
  6. A. Pourrajabian, M. Mirzaei, R. Ebrahimi, D. Wood, Effect of air density on the performance of a small wind turbine blade: A case study in Iran, J. Wind Eng. Ind. Aerodyn. 126 (2014) 1–10 [Google Scholar]
  7. S.M. Mortazavi, M.R. Soltani, H. Motieyan, A Pareto optimal multi-objective optimization for a horizontal axis wind turbine blade airfoil sections utilizing exergy analysis and neural networks, J. Wind Eng. Ind. Aerodyn. 136 (2015) 62–72 [CrossRef] [Google Scholar]
  8. A. Joodaki, A. Ashrafizadeh, Surface shape design in fluid flow problems via hybrid optimization algorithms, Aerospace Sci. Technol. 39 (2014) 639–651 [CrossRef] [Google Scholar]
  9. A. Vicini, D. Quagliarella, Airfoil and Wing Design Through Hybrid Optimization Strategies, 16th AIAA Applied Aerodynamic conference, New Mexico, 1998 [Google Scholar]
  10. R. Duvigneau, M. Visonneau, Hybrid genetic algorithm and artificial neural network for complex design optimization in CFD, Int. J. Numer. Methods Fluids 44 (2000) 1255–1278 [Google Scholar]
  11. M. Sessaregoa, K.R. Dixonb, D.E. Rivala, D.H. Wood, A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization, Eng. Optim. (2014) available at: http://dx. doi. org/10. 1080/0305215X. 2014. 941532 [Google Scholar]
  12. J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind energy explained, New York, USA, John Wiley & Sons Ltd, 2002 [Google Scholar]
  13. D.M. Eggleston, F.S. Stoddard, Wind turbine engineering design, New York, USA, Van Nostrand Reinhoid, 1987 [Google Scholar]
  14. D.J. Malcolm, A.C. Hansen, WindPACT turbine rotor design study, Subcontractor report, Golden: National Renewable Energy Laboratory, NREL/SR-500-32495, 2002, 82 p. [Google Scholar]
  15. D.J. Malcolm, A.C. Hansen, WindPACT turbine rotor design, specificrating study, Subcontractor report, Golden: National Renewable Energy Laboratory, NREL/SR-500-34794, 2003, 42 p. [Google Scholar]
  16. J.F. Manwell, J.G. McGowan, A.L. Rogers, Wind energy explained, Wiley Online Library, 2002 [Google Scholar]
  17. E. Bossanyi, GH bladed theory manual, GH & Partners Ltd., 2003 [Google Scholar]
  18. J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, Michigan, 1975 [Google Scholar]
  19. D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, Application of the Bees Algorithm to the Training of Radial Basis Function Networks for Control Chart Pattern Recognition, 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’06). Ischia, Italy, July 25–28, 2006, pp. 711–716. [Google Scholar]
  20. A. Joodaki, A. Ashrafizadeh, A. Shadaram, Comparison of Continuous and Binary Genetic Algorithms in the Numerical Solution of Internal/External Shape Design Problems, An ECCOMAS Thematic Conference, CFD and Optimization, Turkey, 2011 [Google Scholar]
  21. A. Lipowski, D. Lipowska, Roulette-wheel selection via stochastic acceptance, Physica A 391 (2012) 2193–2196 [CrossRef] [Google Scholar]
  22. D.E. Goldberg, K. Deb, A comprehensive analysis of selection schemes used in Genetic Algorithm, Foundation of Genetic Algorithms 1 (1991) 69–93 [Google Scholar]
  23. P.C. Pendharkar, J.A. Rodger, An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based nueral approaches for classification, Comput. Oper. Res. 31 (2004) 481–498 [CrossRef] [Google Scholar]
  24. D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, Technical Note: Bees Algorithm, Manufacturing Engineering Centre, Cardiff University, Cardiff, Wales, 2005 [Google Scholar]
  25. A. Muthiah, R. Ajkumar, A Comparison of Artificial Bee Colony algorithm and Genetic Algorithm to Minimize the Makespan for Job Shop Scheduling, Proc. Eng. 97 (2014) 1745–1754 [CrossRef] [Google Scholar]
  26. R. Forsatia, A. Keikhab, M. Shamsfard, An improved bee colony optimization algorithm with an application to document clustering, Neurocomputing 159 (2015) 9–26 [CrossRef] [Google Scholar]
  27. H. Zarea, F. Moradi Kashkooli, A. Mansuri Mehryan, M.R. Saffarian, E. Namvar Beherghani, Optimal design of plate-fin heat exchangers by a Bees Algorithm, Appl. Thermal Eng. 69 (2014) 267–277 [CrossRef] [Google Scholar]
  28. M. Lozano, F. Herrera, J.R. Cano, Replacement strategies to preserve useful diversity in steady-state genetic algorithms, Inf. Sci. 178 (2008) 4421–4433 [CrossRef] [Google Scholar]
  29. G. Wang, L. Guo, A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization, Hindawi Publishing Corporation, J. Appl. Math. 2013, 21 p. Article ID 696491 [Google Scholar]
  30. R. Poore, T. Lettenmaier, Alternative design study report: WindPACT advanced wind turbine drive train design study, Subcontractor report. Golden:National Renewable Energy Laboratory, NREL/SR-500-33196, 2003, 556 p. [Google Scholar]
  31. D.M. Somers, The S827 and S828 Airfoils. Subcontractor report. National Renewable Energy Laboratory, Golden, NREL/SR-500-36343, 2005, 54 p. [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.