×

zbMATH — the first resource for mathematics

Wind turbine design through evolutionary algorithms based on surrogate CFD methods. (English) Zbl 1294.76215
Summary: An automatic design environment using evolutionary techniques has been developed for the design of Horizontal Axis Wind Turbine (HAWT) blades taking into account both the aerodynamics and the structural constraints of the blades. This design environment is based on a simple, fast, and robust aerodynamic simulator intended for the prediction of the performance of any turbine blade produced as an intermediate individual of the evolutionary search process. In order to reduce the computational costs, the results obtained by this simple simulator have been corrected through the application of Artificial Neural Network (ANN) based approximations. An additional stress analysis stage has been introduced in order to take into account the structural resistance of the blades as a constraint throughout the optimization process. Results of the simulations obtained using this technique, of the application of the automatic design procedure and of the performance of the wind turbines thus produced are presented. In a first test case the rotor designed through the proposed method is compared to the one obtained analytically for a given air speed. This comparison to an analytical optimum provides a good index in order to evaluate the performance of the method. In a second case the rotor is optimized for a typical wind speed distribution, which is a more realistic scenario where the cost performance of the resulting turbines needs to be evaluated.
MSC:
76N25 Flow control and optimization for compressible fluids and gas dynamics
92B20 Neural networks for/in biological studies, artificial life and related topics
Software:
AESOP
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Abbot H, Von Doenho AE (1949) Theory of wing sections. Dover, New York
[2] Amoignon, O, AESOP—a numerical platform for aerodynamic shape optimization, Optim Eng, 11, 555-581, (2010) · Zbl 1428.76085
[3] Benini, E; Toffolo, A, Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation, J Sol Energy Eng, 124, 359-363, (2002)
[4] Betz A (1920) Eine erweiterung der schraubenstrahl-theorie. Z Flugtech Mot.luftschiffahrt (ZFM) 11:105-110
[5] Burger, C; Hartfield, R, Wind turbine airfoil performance optimization using the vortex lattice method and a genetic algorithm, San Diego, CA
[6] Caamaño P (2011) Caracterización de espacios de calidad y algoritmos evolutivos en problemas de optimización con codificación real. PhD thesis, Universidade da Coruña, Spain
[7] Díaz Casás, V; Lopez Peña, F; Duro, RJ; Lamas, A, Automatic aerodynamic design of a wind turbine through evolutionary techniques, Sofia, Bulgaria
[8] Glauert H (1922) An aerodynamic theory of the airscrew. Aeronautical Research Committee Reports and Memoranda 786, ARCRM (Aeronautical Research Committee, Reports and Memoranda), London, United Kingdom
[9] Glauert H (1926) The elements of airfoil and airscrew theory. Cambridge University Press, Cambridge · JFM 52.0880.03
[10] Goldstein, S, On the vortex theory of screw propellers, Proc Roy Soc Lond Math Phys Sci, 123, 440-465, (1929) · JFM 55.1133.06
[11] Hansen MOL (2008) Aerodynamics of wind turbines. Earthscan/James & James, London
[12] Head MR (AERONAUTICAL RESEARCH COUNCIL LONDON (England)) (1958) Entrainment in the turbulent boundary layer. Defense Technical Information Center · JFM 55.1133.06
[13] Hess, JL, Panel methods in computational fluid dynamics, Annu Rev Fluid Mech, 22, 255-274, (1990)
[14] Hess, JL; Smith, AMO, Calculation of potencial flow about arbitrary bodies, Prog Aerosp Sci, 8, 1-138, (1967) · Zbl 0204.25602
[15] Huyse L (2001) Free-form airfoil shape optimization under uncertainty using maximum expected value and second-order second-moment strategies. Institute for Computer Applications in Science and Engineering Report N 2001-18. CASE NASA Langley Research Center Hampton, Virginia · JFM 55.1133.06
[16] Jameson, A, Aerodynamic design via control theory, J Sci Comput, 3, 233-260, (1988) · Zbl 0676.76055
[17] Marín, J; Solé, RV, Macroevolutionary algorithms: a new optimization method on fitness landscapes, IEEE Trans Evol Comput, 4, 272-286, (1999)
[18] Mengistu, T; Ghaly, W, Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models, Optim Eng, 9, 239-255, (2008) · Zbl 1300.76025
[19] Michel R (1951) Etude de la transition sur les profiles d’Aile. ONERA Report 1/1578A, Onera (Office National d’Etudes et Recherches Aérospatiales), France
[20] Mohammadi B, Pironneau O (2001) Applied shape optimization for fluids. Oxford University Press, New York · Zbl 0970.76003
[21] Montgomerie B (2004) Methods for root effects, tip effects and extending the angle of attack range ±180 deg, with application to aerodynamics for blades on wind turbines and propellers. Technical report, Swedish Defence Research Agency, Stockholm, Tech Rep FOI
[22] Oyama, A; Obayashi, S; Nakahashi, K; Nakamura, T; Sivasundaram, S (ed.), Aerodynamic optimization of transonic wing design based on evolutionary algorithm, Daytona Beach, FL, USA, May 10-12
[23] Ozgener, O, A small wind turbine system (SWTS) application and its performance analysis, Energy Convers Manag, 47, 11-12, (2006)
[24] Peri, D; Campana, EF, Multidisciplinary design optimization of a naval surface combatant, J Ship Res, 47, 1-12, (2003)
[25] Prandtl L (1919) Tragflügeltheorie. Götinger Nachrichten, matematisch-physikalische Klasse. Translated to English (1921) as: Application of modern hydrodynamics to aeronautics. National Advisory Committee for Aeronautics Report N 116. US Government Printing Office
[26] Squire HB, Young AD (1938) The calculation of the profile drag of aerofoils. His Majesty’s Stationery Office
[27] Theodorsen T (1944) The theory of propellers. National Advisory Committee for Aeronautics Reports Ns 775, 776, 777 and 778. US Government Printing Office
[28] Thwaites B (1952) On the momentum equation in laminar boundary layer flow. Aeronautical Research Committee Reports and Memoranda 2587, London
[29] Weisser, D, A wind energy analysis of grenada: an estimation using the Weibull density function, Renew Energy, 28, 1803-1812, (2003)
[30] Zhang, G; Patuwo, BE; Hu, MY, Forecasting with artificial neural networks: the state of the art, Int J Forecast, 14, 35-62, (1998)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.