Abstract
A robust optimization technique is developed for the aerodynamic shape optimization of a helicopter rotor airfoil considering uncertain operating conditions. Both a CFD model and a coupled panel/integral boundary layer model of the aerodynamics are coupled with an optimization code based on Genetic Algorithms. In order to reduce the computational cost of the robust optimization, a multi-fidelity strategy is developed which employs both aerodynamic models inside the optimization loop.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beyer HG, Sendhoff B (2007) Robust optimization—a comprehensive survey. Comput Methods Appl Mech Eng 196:3190–3218
Biava M (2007) RANS computations of rotor/fuselage unsteady interactional aerodynamics. PhD Thesis, Politecnico di Milano
Celi R (1999) Recent applications of design optimization to rotorcraft—a survey. J Aircr 36:176–189
Choi S, Alonso JA, Kroo I (2005) Two-level multi-fidelity design optimization studies for supersonic jets. In: 43rd AIAA aerospace sciences meeting and exhibit
Congedo PM, Corre C, Martinez JM (2011) Shape optimization of an airfoil in a BZT flow with multiple-source uncertainties. Comput Methods Appl Mech Eng 200:216–232
Jones BR, Crossley WA, Lyrintzis AS (2000) Aerodynamic and aeroacoustic optimization of rotorcraft airfoils via a parallel genetic algorithm. J Aircr 37:1088–1096
Srinivas N, Deb K (1995) Multiobjective function optimization using nondominated sorting genetic algorithms. Evol Comput 2:221–248
Drela M (1989) XFOIL: An analysis and design system for low reynolds number airfoils. In: Conference on low Reynolds number airfoil aerodynamics, University of Notre Dame
Drela M (1998) Pros and cons of airfoil optimization. In: Caughey DA, Hafez MM (eds) Frontiers of computational fluid dynamics. World Scientific, Singapore, pp 363–382
Jin Y, Olhofer M, Sendhoff B (2002) A framework for evolutionary optimization with approximate fitness functions. IEEE Trans Evol Comput 5:481–494
Kulfan BM, Bussoletti JE (2006) Fundamental parametric geometry representation for aircraft component shapes. In: 11th AIAA/ISSMO multidisciplinary analysis and optimization conference
Leishman JG (2002) Principles of helicopter aerodynamics. Cambridge University Press, Cambridge
Massaro A, Bennini E (2012) Multi-objective optimization of helicopter airfoils using surrogate-assisted memetic algorithm. J Aircr 49:375–383
Minisci E, Vasile M (2013) Robust design of a re-entry unmanned space vehicle by multi-fidelity evolution control. AIAA J 51:1284–1295
Robinson TD, Willcox KE, Eldred MS, Haimes R (2006) Multifidelity optimization for variable-complexity design. In: 11th AIAA/ISSMO multidisciplinary analysis and optimization conference
Sun H, Lee S (2005) Response surface approach to aerodynamic optimization design of helicopter rotor blade. Int J Numer Meth Eng 64:125–142
Venkatakrishnan V, Mavriplis DJ (1996) Implicit method for the computation of unsteady flows on unstructured grids. J Comput Phys 127:380–397
Vu NA, Lee JW, Byun YH, Kim S (2010) Aerodynamic design optimization of helicopter rotor blades including airfoil shapes. In: 66th annual forum of the American helicopter society
van der Wall BG (2003) 2nd HHC aeroacoustic rotor test (HART II)—Part I: test documentation. Braunschweig, Germany
Yao W, Chen X, Luo W, van Tooren M, Guo J (2011) Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles. Prog Aerosp Sci 47:450–479
Witteveen J (2009) Explicit mesh deformation using inverse distance weighting interpolation. In: 47th AIAA aerospace sciences meeting
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Fusi, F., Congedo, P.M., Guardone, A., Quaranta, G. (2015). Robust Optimization of a Helicopter Rotor Airfoil Using Multi-fidelity Approach. In: Greiner, D., Galván, B., Périaux, J., Gauger, N., Giannakoglou, K., Winter, G. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-319-11541-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-11541-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11540-5
Online ISBN: 978-3-319-11541-2
eBook Packages: EngineeringEngineering (R0)