BibTex RIS Kaynak Göster

An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network

Yıl 2013, Cilt: 3 Sayı: 4, 993 - 1003, 01.12.2013

Öz

In this research, an optimization of a wind turbine airfoil is performed by Genetic Algorithm (GA) as optimization method, coupled with CFD (Computational Fluid Dynamics) and Artificial Neural Network (ANN). A pressure-based implicit procedure is applied to solve the Navier-Stokes equations on a nonorthogonal mesh with collocated finite volume formulation to calculate the aerodynamic coefficients. The boundedness criteria for the numerical procedure are determined by Normalized Variable Diagram (NVD) scheme and the k-ε eddy-viscosity turbulence model is utilized. ANN has been used as surrogate model to reduce computational cost and time.  Single objective and multi objective optimization of wind turbine airfoil have been performed and the results of optimization are presented. To decrease the number of design variables and producing a smooth shaped airfoil, modified Hicks-Henne functions are applied. In this process, the Eppler E387 airfoil has been applied as the base airfoil. The angle of attack varies from 0 to 20 degrees and Reynolds number of the flow is 460000. The presented technique decreases the time of optimization by 99.5%. Moreover, the results manifest the good agreement of trained ANN outputs and CFD simulation. In addition, the Multi-objective optimization can attain the better solutions than single objective to design a wind turbine airfoil with good stall characteristics.

Kaynakça

  • W. Li, L. Huyse, and S. Padula, "Robust airfoil optimization to achieve drag reduction over a range of Mach numbers," Structural and Multidisciplinary Optimization, vol. 24, pp. 38-50, 2002/08/01 2002.
  • B. Howard, Z. Beckett, and Z. David, "Airfoil Optimization Using Practical Aerodynamic Design Requirements," in 27th AIAA Applied Aerodynamics Conference, ed: American Institute of Aeronautics and Astronautics, 2009.
  • U. K. Wickramasinghe, R. Carrese, and L. Xiaodong, "Designing airfoils using a reference point based evolutionary many-objective particle swarm optimization algorithm," in Evolutionary Computation (CEC), 2010 IEEE Congress on, 2010, pp. 1-8.
  • E. S. Tashnizi, A. A. Taheri, and M. H. Hekmat, "Investigation of the adjoint method in aerodynamic optimization using various shape parameterization techniques," Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 32, pp. 176- , 2010.
  • H. Rui, J. Antony, and W. Qiqi, "Adjoint based aerodynamic optimization of supersonic biplane airfoils," in 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, ed: American Institute of Aeronautics and Astronautics, M. H. Mohamed, G. Janiga, E. Pap, and D. Thévenin, "Multi-objective optimization of the airfoil shape of Wells turbine used for wave energy conversion," Energy, vol. 36, pp. 438-446, 2011.
  • S. Paul and B. Ruxandra, "Two-dimensional airfoil shape optimization for airfoils at low speeds," in AIAA Modeling and Simulation Technologies Conference, ed: American Institute of Aeronautics and Astronautics, T. Winnemöller and C. P. Van Dam, "Design and Numerical Optimization of Thick Airfoils Including Blunt Trailing Edges," Journal of Aircraft, vol. 44, pp. 240, 2007/01/01 2007.
  • X. Mauclère, "Automatic 2D Airfoil Generation, Evaluation and Optimisation using MATLAB and XFOIL," Master of science, Mechanical Engineering Department section of fluid mechanics, Technical University of Denemark, 2009.
  • C. Thumthae and T. Chitsomboon, "Optimal angle of attack for untwisted blade wind turbine," Renewable Energy, vol. 34, pp. 1279-1284, 5// 2009.
  • M. Endo, "Wind Turbine Airfoil Optimization by Particle Swarm Method," Case Western Reserve University, 2011.
  • N. Bizzarrini, F. Grasso, and D. P. Coiro, "Genetic algorithms in wind turbine airfoil design," presented at the EWEA, Brussels, Belgium, 2011.
  • R. K. Singh, M. R. Ahmed, M. A. Zullah, and Y.-H. Lee, "Design of a low Reynolds number airfoil for small horizontal axis wind turbines," Renewable Energy, vol. , pp. 66-76, 6// 2012.
  • R. W. V. Jr, "Aero-Structural Optimization of a 5 MW Wind Turbine Rotor," Degree Master of Science, Mechanical and Aerospace Engineering, The Ohio State University, 2012.
  • R. K. Singh and M. R. Ahmed, "Blade design and performance testing of a small wind turbine rotor for low wind speed applications," Renewable Energy, vol. 50, pp. 819, 2// 2013.
  • N. Alexandrov, R. Lewis, C. Gumbert, L. Green, and Management Variable-Fidelity Models," Journal of Aircraft, vol. 38, pp. 1093-1101, 11/01/ 2001. and Model with Aerodynamic Optimization A. Kusiak, Z. Zijun,
  • "Optimization of Wind Turbine Performance With Data- Driven Models," Sustainable Energy, IEEE Transactions on, vol. 1, pp. 66-76, 2010. and L. Mingyang,
  • A. Kusiak, H. Zheng, and Z. Song, "Power optimization of wind turbines with data mining and evolutionary computation," Renewable Energy, vol. 35, pp. 695-702, 3// 2010.
  • A. Kusiak and H. Zheng, "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, vol. 35, pp. 1324-1332, // 2010.
  • A. F. P. Ribeiro, A. M. Awruch, and H. M. Gomes, "An airfoil optimization technique for wind turbines," Applied Mathematical Modelling, vol. 36, pp. 4898- , 10// 2012.
  • M. H. Djavareshkian and A. Esmaeili, "Neuro-fuzzy based performance," Ocean Engineering, vol. 59, pp. 1-8, 2013. estimation of Hydrofoil
  • M. Djavareshkian, "A new NVD scheme in pressure-based finite-volume methods," 2001, pp. 10-14.
  • B. P. Leonard, "A survey of finite differences with upwinding for numerical modeling of the incompressible convection diffusion equation in C. Taylor and K. Morgan leds " Technices in Transient and Turbulent Flow, Pineridgequess, Swansea, U.K., vol. 2, pp. 1-35, R. L. Haupt and S. E. Haupt, "Introduction to Optimization," in Practical Genetic Algorithms, ed: John Wiley & Sons, Inc., 2004, pp. 1-25.
  • R. M. Hicks and P. A. Henne, "Wing Design by Numerical Optimization," Journal of Aircraft, vol. 15, pp. 412, 1978/07/01 1978.
  • M. T. Hagan, H. B. Demuth, and M. Beale, Neural network design: PWS Publishing Co., 1996.
  • Y. Hao and M. W. Bogdan, "Levenberg?Marquardt Training," in Intelligent Systems, ed: CRC Press, 2011, pp. 1-16. E. Hau, Wind Turbines: Fundamentals,
  • Technologies, Application, Economics: Springer, 2006.
  • M. O. L. Hansen, Aerodynamics of wind turbines electronic resource]: Earthscan, 2008.
  • G. Francesco, "Hybrid Optimization for Wind Airfoils," Turbine AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, ed: American Institute of Aeronautics and Astronautics, 2012. rd
  • "Experimental data base for computer program assessment," tech.rep.,AGARD Advisory Report No.
Yıl 2013, Cilt: 3 Sayı: 4, 993 - 1003, 01.12.2013

Öz

Kaynakça

  • W. Li, L. Huyse, and S. Padula, "Robust airfoil optimization to achieve drag reduction over a range of Mach numbers," Structural and Multidisciplinary Optimization, vol. 24, pp. 38-50, 2002/08/01 2002.
  • B. Howard, Z. Beckett, and Z. David, "Airfoil Optimization Using Practical Aerodynamic Design Requirements," in 27th AIAA Applied Aerodynamics Conference, ed: American Institute of Aeronautics and Astronautics, 2009.
  • U. K. Wickramasinghe, R. Carrese, and L. Xiaodong, "Designing airfoils using a reference point based evolutionary many-objective particle swarm optimization algorithm," in Evolutionary Computation (CEC), 2010 IEEE Congress on, 2010, pp. 1-8.
  • E. S. Tashnizi, A. A. Taheri, and M. H. Hekmat, "Investigation of the adjoint method in aerodynamic optimization using various shape parameterization techniques," Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 32, pp. 176- , 2010.
  • H. Rui, J. Antony, and W. Qiqi, "Adjoint based aerodynamic optimization of supersonic biplane airfoils," in 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, ed: American Institute of Aeronautics and Astronautics, M. H. Mohamed, G. Janiga, E. Pap, and D. Thévenin, "Multi-objective optimization of the airfoil shape of Wells turbine used for wave energy conversion," Energy, vol. 36, pp. 438-446, 2011.
  • S. Paul and B. Ruxandra, "Two-dimensional airfoil shape optimization for airfoils at low speeds," in AIAA Modeling and Simulation Technologies Conference, ed: American Institute of Aeronautics and Astronautics, T. Winnemöller and C. P. Van Dam, "Design and Numerical Optimization of Thick Airfoils Including Blunt Trailing Edges," Journal of Aircraft, vol. 44, pp. 240, 2007/01/01 2007.
  • X. Mauclère, "Automatic 2D Airfoil Generation, Evaluation and Optimisation using MATLAB and XFOIL," Master of science, Mechanical Engineering Department section of fluid mechanics, Technical University of Denemark, 2009.
  • C. Thumthae and T. Chitsomboon, "Optimal angle of attack for untwisted blade wind turbine," Renewable Energy, vol. 34, pp. 1279-1284, 5// 2009.
  • M. Endo, "Wind Turbine Airfoil Optimization by Particle Swarm Method," Case Western Reserve University, 2011.
  • N. Bizzarrini, F. Grasso, and D. P. Coiro, "Genetic algorithms in wind turbine airfoil design," presented at the EWEA, Brussels, Belgium, 2011.
  • R. K. Singh, M. R. Ahmed, M. A. Zullah, and Y.-H. Lee, "Design of a low Reynolds number airfoil for small horizontal axis wind turbines," Renewable Energy, vol. , pp. 66-76, 6// 2012.
  • R. W. V. Jr, "Aero-Structural Optimization of a 5 MW Wind Turbine Rotor," Degree Master of Science, Mechanical and Aerospace Engineering, The Ohio State University, 2012.
  • R. K. Singh and M. R. Ahmed, "Blade design and performance testing of a small wind turbine rotor for low wind speed applications," Renewable Energy, vol. 50, pp. 819, 2// 2013.
  • N. Alexandrov, R. Lewis, C. Gumbert, L. Green, and Management Variable-Fidelity Models," Journal of Aircraft, vol. 38, pp. 1093-1101, 11/01/ 2001. and Model with Aerodynamic Optimization A. Kusiak, Z. Zijun,
  • "Optimization of Wind Turbine Performance With Data- Driven Models," Sustainable Energy, IEEE Transactions on, vol. 1, pp. 66-76, 2010. and L. Mingyang,
  • A. Kusiak, H. Zheng, and Z. Song, "Power optimization of wind turbines with data mining and evolutionary computation," Renewable Energy, vol. 35, pp. 695-702, 3// 2010.
  • A. Kusiak and H. Zheng, "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, vol. 35, pp. 1324-1332, // 2010.
  • A. F. P. Ribeiro, A. M. Awruch, and H. M. Gomes, "An airfoil optimization technique for wind turbines," Applied Mathematical Modelling, vol. 36, pp. 4898- , 10// 2012.
  • M. H. Djavareshkian and A. Esmaeili, "Neuro-fuzzy based performance," Ocean Engineering, vol. 59, pp. 1-8, 2013. estimation of Hydrofoil
  • M. Djavareshkian, "A new NVD scheme in pressure-based finite-volume methods," 2001, pp. 10-14.
  • B. P. Leonard, "A survey of finite differences with upwinding for numerical modeling of the incompressible convection diffusion equation in C. Taylor and K. Morgan leds " Technices in Transient and Turbulent Flow, Pineridgequess, Swansea, U.K., vol. 2, pp. 1-35, R. L. Haupt and S. E. Haupt, "Introduction to Optimization," in Practical Genetic Algorithms, ed: John Wiley & Sons, Inc., 2004, pp. 1-25.
  • R. M. Hicks and P. A. Henne, "Wing Design by Numerical Optimization," Journal of Aircraft, vol. 15, pp. 412, 1978/07/01 1978.
  • M. T. Hagan, H. B. Demuth, and M. Beale, Neural network design: PWS Publishing Co., 1996.
  • Y. Hao and M. W. Bogdan, "Levenberg?Marquardt Training," in Intelligent Systems, ed: CRC Press, 2011, pp. 1-16. E. Hau, Wind Turbines: Fundamentals,
  • Technologies, Application, Economics: Springer, 2006.
  • M. O. L. Hansen, Aerodynamics of wind turbines electronic resource]: Earthscan, 2008.
  • G. Francesco, "Hybrid Optimization for Wind Airfoils," Turbine AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, ed: American Institute of Aeronautics and Astronautics, 2012. rd
  • "Experimental data base for computer program assessment," tech.rep.,AGARD Advisory Report No.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Amir Latifi Bidarouni Bu kişi benim

Mohammad Hasan Djavareshkian Bu kişi benim

Yayımlanma Tarihi 1 Aralık 2013
Yayımlandığı Sayı Yıl 2013 Cilt: 3 Sayı: 4

Kaynak Göster

APA Bidarouni, A. L., & Djavareshkian, M. H. (2013). An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network. International Journal Of Renewable Energy Research, 3(4), 993-1003.
AMA Bidarouni AL, Djavareshkian MH. An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network. International Journal Of Renewable Energy Research. Aralık 2013;3(4):993-1003.
Chicago Bidarouni, Amir Latifi, ve Mohammad Hasan Djavareshkian. “An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network”. International Journal Of Renewable Energy Research 3, sy. 4 (Aralık 2013): 993-1003.
EndNote Bidarouni AL, Djavareshkian MH (01 Aralık 2013) An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network. International Journal Of Renewable Energy Research 3 4 993–1003.
IEEE A. L. Bidarouni ve M. H. Djavareshkian, “An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network”, International Journal Of Renewable Energy Research, c. 3, sy. 4, ss. 993–1003, 2013.
ISNAD Bidarouni, Amir Latifi - Djavareshkian, Mohammad Hasan. “An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network”. International Journal Of Renewable Energy Research 3/4 (Aralık 2013), 993-1003.
JAMA Bidarouni AL, Djavareshkian MH. An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network. International Journal Of Renewable Energy Research. 2013;3:993–1003.
MLA Bidarouni, Amir Latifi ve Mohammad Hasan Djavareshkian. “An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network”. International Journal Of Renewable Energy Research, c. 3, sy. 4, 2013, ss. 993-1003.
Vancouver Bidarouni AL, Djavareshkian MH. An Optimization of Wind Turbine Airfoil Possessing Good Stall Characteristics by Genetic Algorithm Utilizing CFD and Neural Network. International Journal Of Renewable Energy Research. 2013;3(4):993-1003.