Abstract
Blade element momentum theory is extensively used to design and characterize the performance of the wind turbine. Aerodynamic characteristics of the airfoils used in the blades of the wind turbine are one of the crucial parameters of the blade element momentum theory. The aerodynamic characteristics of airfoil can be significantly influenced by Reynolds number besides the angle of attack. Thus, an inadequate consideration of Reynolds number for aerodynamic characteristics of the airfoil can result in discrepancy in the optimum design and performance evaluation capability of the blade element momentum theory. Several mathematical models, and semi-empirical relations to parameterize the aerodynamic characteristics of two-dimensional airfoil in blade element momentum theory, inherently lack dependency on Reynolds number and thus open a scope for uncertainty. An artificial neural network-based model is proposed to predict the lift and drag coefficient of airfoil as a function of not only the angle of attack but also the Reynolds number. A series of six in-house developed airfoils for small wind turbine have been considered for the present study. The computational fluid dynamic results of the airfoil with a range of Reynolds number (100,000–2,000,000) and angle of attack (0°–20°) were utilized to develop the model. A high coefficient of determination and low root-mean-square error of the developed models for a test dataset suggests the robust capabilities and effective topology of the artificial neural network-based model to predict the lift and drag coefficient of the airfoils with respect to a given angle of attack and Reynolds number. The developed model can then be used to replace the traditional analytical or semi-empirical model for mathematical representation of airfoil in the blade element momentum theory and thus reduce the uncertainty on account of inadequate consideration of Reynolds number for aerodynamic characteristics of airfoil in design and performance evaluation.
Graphic abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ansys I (2015) ICEM CFD theory guide. Ansys Inc
Ansys I (2013) ANSYS fluent theory guide (release 15.0). Canonsburg, PA
Arslan O, Yetik O (2014) ANN modeling of an orc-binary geothermal power plant: Simav case study. Energy Sources, Part A Recover Util Environ Eff 36:418–428. https://doi.org/10.1080/15567036.2010.542437
Bai C-J, Chen P-W, Wang W-C (2016) Aerodynamic design and analysis of a 10 kW horizontal-axis wind turbine for Tainan. Taiwan Clean Technol Environ Policy 18:1151–1166. https://doi.org/10.1007/s10098-016-1109-z
Bai CJ, Wang WC (2016) Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (HAWTs). Renew Sustain Energy Rev 63:506–519. https://doi.org/10.1016/j.rser.2016.05.078
Bai CJ, Wang WC, Chen PW (2017) Experimental and numerical studies on the performance and surface streamlines on the blades of a horizontal-axis wind turbine. Clean Technol Environ Policy 19:471–481. https://doi.org/10.1007/s10098-016-1232-x
Bavanish B, Thyagarajan K (2013) Optimization of power coefficient on a horizontal axis wind turbine using bem theory. Renew Sustain Energy Rev 26:169–182. https://doi.org/10.1016/j.rser.2013.05.009
Behera SK, Meher SK, Park HS (2015) Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste. Clean Technol Environ Policy 17:443–453. https://doi.org/10.1007/s10098-014-0798-4
Belamadi R, Djemili A, Ilinca A, Mdouki R (2016) Aerodynamic performance analysis of slotted airfoils for application to wind turbine blades. J Wind Eng Ind Aerodyn 151:79–99. https://doi.org/10.1016/j.jweia.2016.01.011
Ceyhan Ö (2012) Towards 20MW wind turbine: High reynolds number effects on rotor design. In: 50th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, pp 9–12. https://doi.org/10.2514/6.2012-1157
Chakraborty S, Chowdhury S, Das SP (2013) Artificial neural network (ANN) modeling of dynamic adsorption of crystal violet from aqueous solution using citric-acid-modified rice (Oryza sativa) straw as adsorbent. Clean Technol Environ Policy 15:255–264. https://doi.org/10.1007/s10098-012-0503-4
Chattot JJ (2003) Optimization of wind turbines using helicoidal vortex model. J Sol Energy Eng Trans ASME 125:418–424. https://doi.org/10.1115/1.1621675
El-Okda YM (2015) Design methods of horizontal axis wind turbine rotor blades. Int J Ind Electron Drives 2:135. https://doi.org/10.1504/ijied.2015.072789
Ge M, Fang L, Tian D (2015) Influence of reynolds number on multi-objective aerodynamic design of a wind turbine blade. PLoS ONE 10:1–25. https://doi.org/10.1371/journal.pone.0141848
Ge M, Tian D, Deng Y (2016) Reynolds number effect on the optimization of a wind turbine blade for maximum aerodynamic efficiency. J Energy Eng. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000254
Gue IHV, Ubando AT, Tseng ML, Tan RR (2020) Artificial neural networks for sustainable development: a critical review. Clean Technol Environ Policy 22:1449–1465. https://doi.org/10.1007/s10098-020-01883-2
Hand MM et al (2001) Unsteady aerodynamics experiment phase VI: wind tunnel test configurations and available data campaigns. Golden, CO (US)
Hassanzadeh A, Hassanzadeh Hassanabad A, Dadvand A (2016) Aerodynamic shape optimization and analysis of small wind turbine blades employing the Viterna approach for post-stall region. Alex Eng J 55:2035–2043. https://doi.org/10.1016/j.aej.2016.07.008
Howard D, Mark B (2004) Neural network toolbox documentation. Neural Netw Tool 2004:846
Lanzafame R, Messina M (2007) Fluid dynamics wind turbine design: critical analysis, optimization and application of BEM theory. Renew Energy 32:2291–2305. https://doi.org/10.1016/j.renene.2006.12.010
Leishman JG (2006) Principles of helicopter aerodynamics
Maleki H, Sorooshian A, Goudarzi G et al (2019) Air pollution prediction by using an artificial neural network model. Clean Technol Environ Policy 21:1341–1352. https://doi.org/10.1007/s10098-019-01709-w
MATLAB (2015) R2015a. The MathWorks Inc., Natick, Massachusetts
McGhee R, Walker B, Millard B (1988) Experimental results for the eppler 387 airfoil at low reynolds numbers in langley low - turbulence pressure tunnel. Nasa 4062:238
McTavish S, Feszty D, Nitzsche F (2013) Evaluating Reynolds number effects in small-scale wind turbine experiments. J Wind Eng Ind Aerodyn 120:81–90. https://doi.org/10.1016/j.jweia.2013.07.006
Miller MA, Kiefer J, Westergaard C et al (2019) Horizontal axis wind turbine testing at high Reynolds numbers. Phys Rev Fluids 4:1–22. https://doi.org/10.1103/PhysRevFluids.4.110504
Pinkerton RM (1936) Calculated and measured pressure distributions over the midspan section of the NACA 4412 airfoil. 563
Reggio M, Villalpando F, Ilinca A (2011) Assessment of turbulence models for flow simulation around a wind turbine airfoil. Model Simul Eng. https://doi.org/10.1155/2011/714146
Tangler JL, Somers DM (1995) NREL Airfoil Families for HAWTs. Golden, CO.(US)
Tugcu A, Arslan O (2017) Optimization of geothermal energy aided absorption refrigeration system—GAARS: a novel ANN-based approach. Geothermics 65:210–221. https://doi.org/10.1016/j.geothermics.2016.10.004
Wallach R, de Mattos BS, da Mota Girardi R (2006) Aerodynamic coefficient prediction of a general transport aircraft using neural network. In: ICAS Secretariat 25th congress of the international council of the aeronautical science, vol 2, pp 1199–1214. https://doi.org/10.2514/6.2006-658
Wang L, Tang X, Liu X (2012) Optimized chord and twist angle distributions of wind turbine blade considering Reynolds number effects
Zhu WJ, Shen WZ, Sørensen JN (2014) Integrated airfoil and blade design method for large wind turbines. Renew Energy 70:172–183. https://doi.org/10.1016/j.renene.2014.02.057
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Verma, N., Baloni, B.D. Artificial neural network-based meta-models for predicting the aerodynamic characteristics of two-dimensional airfoils for small horizontal axis wind turbine. Clean Techn Environ Policy 24, 563–577 (2022). https://doi.org/10.1007/s10098-021-02059-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10098-021-02059-2