THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

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Influence of selected turbulence model on the optimization of a CST parameterized airfoil

ABSTRACT
An airfoil was parameterized using the Class-Shape Transformation technique and then optimized via Genetic Algorithm. The aerodynamic characteristics of the airfoil were obtained with the use of a computational fluid dynamics software. The automated numerical technique was validated using available experimental data and then the optimization procedure was repeated for few different turbulence models. The obtained optimized airfoils were then compared in order to gain some insight on the influence of the different turbulence models on the optimization result. [Projekat Ministarstva nauke Republike Srbije, br. P35035]
KEYWORDS
PAPER SUBMITTED: 2016-02-09
PAPER REVISED: 2016-05-10
PAPER ACCEPTED: 2016-07-13
PUBLISHED ONLINE: 2016-08-07
DOI REFERENCE: https://doi.org/10.2298/TSCI160209194I
REFERENCES
  1. Mukesh, R., et al., Airfoil Shape Optimization Using Non-traditional Optimization Technique and its Validation, Journal of King Saud University - Engineering Sciences, 26 (2014), pp. 191-197
  2. Shahrokhi, A., Jahangirian, A., Airfoil Shape Parameterization for Optimum Navier-Stokes Design with Genetic Algorithm, Aerospace Science and Technology, 11 (2007), pp. 443-450
  3. Ribeiro, A.,F.,P., et al., An airfoil Optimization Technique for Wind Turbines, Applied Mathematical Modelling, 36 (2012), pp. 4898-4907
  4. Derksen, R.,W., Rogalsky, T., Bezier-PARSEC: An Optimized Aerofoil Parameterization for Design, Advances in Engineering Software, 41 (2010), pp. 923-930
  5. Lane, A., K., Marshall, D., D., Inverse Airfoil Design Utilizing CST Parameterization, (AIAA 2010-1228), 48th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, 2010, pp. 1-14
  6. Entz, R., M., U., et al. Methods for Preliminary Airfoil Optimization, (AIAA 2009-3774), 27th AIAA Applied Aerodynamics Conference, San Antonio, Texas,2009, pp. 1-15
  7. Kulfan, B., M., Bussoletti, J., E., "Fundamental" Parametric Geometry Representations for Aircraft Component Shapes, 11th AIAA/ISSMO Multidisciplinary analysis and optimization conference: the modelling and simulation frontier for multidisciplinary design optimization, Portsmouth, Virginia, 2006, pp. 1-45 or Paper 6948
  8. Kulfan, B., M., A Universal Parametric Geometry Representation Method - "CST", 45th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, 2007, pp. 1-36 or Paper 62
  9. Kulfan, B., M., Recent Extensions and Applications of the ‘CST' Universal Parametric Geometry representation method, The Aeronautical Journal, 114 (2010), 1153, pp. 156-176
  10. Beasley, D., et al., An Overview of Genetic Algorithm: Part 1, Fundamentals, University Computing, 15 (1993), pp. 58-69
  11. Beasley, D., et al., An Overview of Genetic Algorithm: Part 2, Research topics, University Computing, 15 (1993), pp. 170-181
  12. Cao, Y., J., Wu, Q., H., Teaching Genetic Algorithm Using MATLAB, International Journal of Electrical Engineering Education, 36 (1999), pp. 139-159
  13. ***, ANSYS FLUENT Theory Guide, ANSYS, Inc., Canonsburg, PA, 2015
  14. ***, ANSYS FLUENT User Guide, ANSYS, Inc., Canonsburg, PA, 2015
  15. Graham, J., D., A Systematic Investigation of Pressure Distributions at High Speeds Over Five Representative NACA Low-Drag and Conventional Airfoil Sections, NACA Report No. 832, 1945
  16. Nitzberg E., G. and Crandal, S., A Study of Flow Changes Associated with Airfoil Section Drag Rise at Supercritical Speeds, NACA Technical Note No. 1813, 1949