Abstract
Aim of this paper is to present a methodology useful to optimize the geometry of the blades of a small-size wind turbine which are obtained from a circular pipe: an optimal chord distribution and airfoil sweep can be obtained with a proper cutting path. A strong reduction in manufacturing costs and time can be achieved for blades which are a critical element in wind turbine systems, especially in case of renewable plants in developing countries. An algorithm has been developed to obtain the shape of the blades and wind turbine performances are computed by the Blade-Element Method, due to its low computational simplicity; the XFoil tool has been used to compute the aerodynamic of the blades. Heuristic algorithms have been applied to obtain a feasible design solution assuring the best efficiency of the wind turbine. Also structural considerations are kept into account to provide a feasible configuration able to withstand the forces acting on the rotating blades. Results obtained suggest that an optimal design of such a kind of blades can be obtained thanks to this methodology. The mathematical framework developed for the optimization is efficient and the heuristics algorithms allow the convergence to feasible configurations. The computing time is compatible with a practical application of the method also in industries.
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Abbreviations
- a:
-
Axial induction factor (–)
- a c :
-
Critical axial induction factor (–)
- a′:
-
Angular induction factor (–)
- A 1, A 2 :
-
Coefficients for the lift Viterna model (–)
- AR:
-
Blade aspect ratio (–)
- B 1, B 2 :
-
Coefficients for the drag Viterna model (–)
- C D :
-
Local drag coefficient (–)
- C Dmax :
-
Maximum lift coefficient (–)
- C L :
-
Local lift coefficient (–)
- C Lstall :
-
Local stall lift coefficient (–)
- ch:
-
Chord of the airfoil (m)
- ch_a :
-
Chord of the tip section (m)
- ch_b :
-
Chord of the intermediate section (m)
- ch_c :
-
Chord of the root section (m)
- D :
-
Pipe diameter (m)
- dis_d :
-
Position of the tip section chord (m)
- dis_e :
-
Position of the intermediate section chord (m)
- dis_f :
-
Position of the root section chord (m)
- F x :
-
Axial force (N)
- FS:
-
Airfoil scale factor (–)
- K :
-
Correction factor in Glauert equation (–)
- len_l :
-
Blade span (m)
- len_g :
-
Distance tip/intermediate section (m)
- len_h :
-
Distance tip/rotation centre (m)
- N :
-
Number of blades (–)
- r :
-
Radius measured from wind turbine hub (m)
- R :
-
Radius of the wind turbine from rotation axis (m)
- Q :
-
Factor for tip loss (–)
- s :
-
Pipe thickness (m)
- T :
-
Torque (Nm)
- t :
-
Parameter along Bezier curve (0 ÷ 1)
- V :
-
Wind speed (m/s)
- W :
-
Speed of the flow impacting the airfoil (m/s)
- X LE, Z LE :
-
Coordinates of the airfoil leading edge (m)
- X TE, Z TE :
-
Coordinates of the airfoil trailing edge (m)
- x i, y i :
-
Chord points’ coordinates in the plane (m)
- X i, Y i, Z i :
-
Chord points’ coordinates respect to blade longitudinal axis (m)
- X i_up, Y i_up, Z i_up :
-
Coordinates of the airfoil upper surface (m)
- X i_down, Y i_down, Z i_down :
-
Coordinates of the airfoil lower surface (m)
- x i_rot, y i_rot :
-
Rotated chord points’ coordinates in the plane (m)
- x i_lean, y i_lean, z i_lean :
-
Coordinates of the projection chord points on the pipe (m)
- α :
-
Blade angle of attack (rad)
- α stall :
-
Blade stall angle of attack (rad)
- β :
-
Relative flow angle on the blades (rad)
- λ r :
-
Tip–speed ratio at radius r (–)
- γ 0 :
-
Airfoil settling angle (rad)
- γ rot :
-
Blade geometry rotation angle (rad)
- Ω :
-
Blade rotational speed (rad/s)
- ω :
-
Wake rotational speed (rad/s)
- ρ :
-
Air density (kg/m3)
- σ′ :
-
Local solidity (–)
- τ :
-
Airfoil inclination angle on the pipe (rad)
References
Manwell JF, McGowan JG, Rogers AL, (2009) Wind energy explained: theory, design and application, 2nd edn. Wiley, Hoboken, NJ. ISBN: 978-0-470-01500-1
Walker JF, Jenkins N (1997) Wind energy technology. UNESCO energy engineering series. Wiley, Hoboken, NJ. ISBN-13: 978-0471960447
Scheubel PJ, Crossley RJ (2012) Wind turbine blade design. Energies 5(9):3425–3434
Burton T, Jenkins N, Sharpe D, Bossanyi E (2011). Wind energy handbook. Wiley, Hoboken, NJ
Ragheb M, Ragheb AM (2011) Wind turbines theory—The Betz equation and optimal rotor tip speed ratio. In: Carriveau R (ed) Fundamental and advanced topics in wind power, chap 2. ISBN 978-953-307-508-2
Grasso F (2011) Usage of numerical optimization in wind turbine airfoil design. J Aircr 48(1):248–255
Drela M (2017) XFOIL user guide, MIT, Boston, USA. http://web.mit.edu/drela/Public/web/xfoil/. Accessed on Jan 2017
Li JY, Li R, Gao Y, Huang J (2010) Aerodynamic optimization of wind turbine airfoils using response surface techniques. Proc Inst Mech Eng Part A J Power Energy 224(6):827–838
Méndez J, Greiner D (2006) Wind blade chord and twist angle optimization using genetic algorithms. In: Fifth international conference on engineering computational technology, Las Palmas de Gran Canaria, Spain, pp 12–15
Hampsey (2012) Multiobjective evolutionary optimisation of small wind turbine blades, PhD thesis at the Discipline of Mechanical Engineering-University of Newcastle (N.S.W.), Australia, August 2002, pp 1–468
Tangler J, Kocurek D (2005) Wind turbine post-stall airfoil performance characteristics guidelines for blade-element momentum methods. In: 43rd AIAA Aerospace sciences meeting and exhibit. January 10–13, Reno, Nevada (USA)
Viterna LA, Corrigan RD (1981) Fixed pitch rotor performance of large horizontal axis wind turbines, DOE/NASA workshop on large horizontal axis wind turbines. Cleveland, Ohio, July 1981
Kenway G, Martins JRRA (2008) Aerostructural shape optimization of wind turbine blades considering site-specific winds. In: Proceedings of 12th AIAA/ISSMO multidisciplinary analysis and optimization conference. University of Toronto Institute for Aerospace Studies, Victoria, British Columbia, Canada. Toronto, Ontario, Canada
Sorensen JN, Shen WZ (2002) Numerical modelling of wind turbine wakes. J Fluids Eng 124(2):393–399
Hansen AC, Butterfield CP (1993) Aerodynamics of horizontal-axis wind turbines. Annu Rev Fluid Mech 25(1):115–149
Kusiak A, Zheng H (2010) Optimization of wind turbine energy and power factor with an evolutionary computation algorithm. Energy 35(3):1324–1332. ISSN 0360-5442
Lee KH, Kim KH, Lee KT, Park JP, Lee DH (2010) Two-step optimization for wind turbine blade with probability approach. J Sol Energy Eng 132(3):034503-1, 034503-5
Jureczko MEZYK, Pawlak M, Mężyk A (2005) Optimisation of wind turbine blades. J Mater Process Technol 167(2):463–471
Song F, Ni Y, Tan Z (2011) Optimization design, modeling and dynamic analysis for composite wind turbine blade. Proc Eng 16:369–375
Ingram G (2005) Wind turbine blade analysis using the blade element momentum method. Version 1.0. School of Engineering. Durham University, Durham
Wilkes CE, Summers JW, Daniels CA, Berard MT (2005) PVC handbook, Hanser, Munich. ISBN 9781569903797
Fishman GS (1995) Monte Carlo: concepts, algorithms, and applications. Springer, New York. ISBN 0-387-94527-X
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Hoos HH, Stützle T (2005) Stochastic local search: foundations and applications. Elsevier, Amsterdam
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Pub. Co, Reading
Kennedy J, Eberhart R, Shi Y (2001) Swarm intelligence, 1st edn. Morgan Kaufmann, Burlington, MA. ISBN: 9780080518268
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
Yang XS (2005) Biology-derived algorithms in engineering optimizaton (Chap. 32). In: Olarius S, Zomaya AY (eds) Handbook of bioinspired algorithms and applications. Chapman & Hall/CRC, London
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm for optimization inspired by imperialistic competition. IEEE Congress in Evolutionary Computation, Singapore
Duan H, Liu S, Lei X (2008) Air robot path planning based on Intelligent Water Drops optimization. In: IEEE IJCNN 2008, pp 1397–1401
Shah-Hosseini H (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J BioInspir Comput 1(1–2):71–79
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Timmis J, Neal M, Hunt J (2000) An artificial immune system for data analysis. BioSystems 55(1):143–150
Hsiao Y, Chuang CL, Jiang JA, Chien CC (2005) A novel optimization algorithm: space gravitational optimization. In: IEEE Xplore conference: IEEE international conference on systems, man and cybernetics, vol 3
Ahrari A, Atai AA (2010) Grenade explosion method—a novel tool for optimization of multimodal functions. Appl Soft Comput 10(4):1132–1140
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Repinšek M, Liu SH, Mernik L (2012) A note on teaching-learning-based optimization algorithm. Inf Sci Int J 212:79–93. https://doi.org/10.1016/j.ins.2012.05.009
Tornabene F, Ceruti A (2013) Mixed static and dynamic optimization of four-parameter functionally graded completely doubly curved and degenerate shells and panels using GDQ method. Math Probl Eng 2013:1–33. https://doi.org/10.1155/2013/867079 (Article ID 867079)
Ceruti A (2013) Design and heuristic optimization of low temperature differential stirling engine for water pumping. Int J Heat Technol 31(1):9–16
Ceruti A, Voloshin V, Marzocca P (2014) Heuristic algorithms applied to multidisciplinary design optimization of unconventional airship configuration. J Aircr J Aircr 51(6):1758–1772
Ceruti A, Marzocca P, (2017) Heuristic optimization of Bezier curves based trajectories for unconventional airships docking. Aircr Eng Aerosp Technol 89(1):76–86. https://doi.org/10.1108/AEAT-11-2014-0200
Frulla G, Gili P, Visone M, D’Oriano V, Lappa M (2015) A practical engineering approach to the design and manufacturing of a mini kW bladewind turbine: definition, optimization and CFD analysis. Fluid Dyn Mater Process 11(3):257–277
Hepperle M (2007) JavaFoil User’s Guide. http://www.mh-aerotools.de/airfoils/java/JavaFoil%20Users%20Guide.pdf. Accessed Jan 2017
Pelletier A, Mueller TJ (2000) Low reynolds number aerodynamics of low-aspect-ratio, thin/flat/cambered-plate wings. J Aircr 37(5):825–832
Autodesk® AutoCAD 2012 DXF Reference. https://images.autodesk.com/adsk/files/autocad_2012_pdf_dxf-reference_enu.pdf. Accessed Dec 2016
Burton T, Jenkins N, Sharpe D, Bossanyi E, (2001) Wind energy handbook, Wiley, Hoboken. ISBN: 978-0-470-69975-1
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Appendix
Appendix
As already introduced in the Sect. 3 of the paper, the basic methodology to optimize wind turbine blades comes from the integration of Momentum Theory and Blade-Element Model. The Momentum Theory finds the axial force and torque of a wind turbine evaluating the behaviour of the air stream through the area swept by the wind turbine blades.
In Momentum Theory [4], the kinetic energy of the air flow passing through the wind turbine and the quantity of motion are equated, and the axial force on the turbine disk area (Fig. 16) can be computed with:
where V1 is the velocity of the undisturbed air flow before the wind turbine (Fig. 17). In a similar manner, the torque generated by the air flow over the blades can be evaluated with:
The losses on the blades can be accounted with the following equation, function of the radial distance from the turbine hub:
The Blade-Element Theory exploits the aerodynamics to compute the axial force and torque on the wind turbine: the integral of the lift and drag of the blade airfoils along the span is required in this approach. Once defined the rotational speed of the wind turbine blades (Ω) and the speed of the rotational wake (ω), the axial induction factor (a) and the angular induction factor (a′) can be defined as follows (see also Fig. 17):
Considering the triangle of the air relative speed over a rotating blade (see Fig. 18), the following formula applies:
The settling angle (γ) depends on the blade geometry, while the angle between wind turbine hub axis and the relative air speed acting on the blade (β) can be found with:
The local tip ratio (λr) is an index of the ratio between wind turbine rotational speed and the speed of the undisturbed axial wind flow:
Following the rules of the trigonometry, the relative speed impacting the blade sections can be found with:
The axial force acting on the infinitesimal area of the blade surface dS given by the local chord (c) multiplied by dr is:
where CL and CD are lift and drag airfoil non-dimensional coefficients. A similar formula can be derived for the torque on the infinitesimal surface:
In the previous formulas, the local solidity (σ′) has been introduced:
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Ceruti, A. Meta-heuristic multidisciplinary design optimization of wind turbine blades obtained from circular pipes. Engineering with Computers 35, 363–379 (2019). https://doi.org/10.1007/s00366-018-0604-8
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DOI: https://doi.org/10.1007/s00366-018-0604-8