Abstract
It is well known that a modern fighter should have high aerodynamic performance, i.e., high lift (C L) and low drag (C D), high C L/C D at subsonic speed and low drag at supersonic speed, etc. However, stealthy performance has also become one of the basic requirements to a modern fighter. So the task of a designer today is to shape the aircraft with not only the maximum aerodynamic efficiency but also a low observability. Up to now, reducing radar cross section (RCS) is the most important part of low observable technique for a flight vehicle. These requirements derive the development of multiobjective (MO)/multidisciplinary (MD) optimization. The goal of MO/MD optimization is to obtain one of the needed pareto solution at a minimum computing expense. A computational study of biobjective (BO) /bidisciplinary (BD) optimization of airfoils and wings is given in the present paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pulliam, T.H.; Steger, J.L. Implicit finite difference simulation of three dimensional compressible flow. AIAA. J. 18(2): 159–167, 1980
Van Leer, B.: Flux-vector splitting for the Euler equations. Lect. Notes in Physics, 170: 507–512, 1982
Obayashi, S.; Kawahara, K.: LU factorization of an implicit scheme for the compressible N-S equations. AIAA 84-1670
Zhu, Z.Q.; Islam, Z.; Zhu, Y.K.; Li, H.M.: The numerical optimization computation of fluid/ electromagnetic fields. Comp. Fluid Dyn. J. 7(2): 229–244, 1998
Umashankar, K.; Taflove, A.: A noval method to analyze electromagnetic scattering of complex objects. IEEE Trans. Elect. Comp. EMC-24(4): 405–410, 1982
Stadler, W.: Multicriteria optimization in mechanics (A survey). Applied mechanics reviews, 37(3): 277–286, 1984
Rosenbrock, H.H.: An automatic method for finding the greatest or least value of a function. Computer Journal, 3: 175–184, 1960
Powell, M.J.D: An efficient method for finding the minimum of a function of several variables without calculation derivatives. Computer Journal, 7: 155–162, 1964
Holland, J.H. Adaption in Natural And Artificial Systems. The University Michigan Press, 1975
Goldberg, D.E. Genetic Algorithms in Search, Optimization, And Machine Learning. Addison-Wesley, 1989
Yu, R.X. A study of multiobjective/multidisciplinary optimization method. MS thesis of Bijing University of Aeronautics and Astronautics, 2002
Hager, J.O., Eyi, S., Lee, K.D. Multi-point Design of Transonic Airfoils, Using Optimization. AIAA 92-4225
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Zhu, Z.Q., Fu, H.Y., Yu, R.X., Li, H.M. (2003). Computation of Biobjective/Bidisciplinary Optimization. In: Sobieczky, H. (eds) IUTAM Symposium Transsonicum IV. Fluid Mechanics and its Applications, vol 73. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0017-8_41
Download citation
DOI: https://doi.org/10.1007/978-94-010-0017-8_41
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-3998-7
Online ISBN: 978-94-010-0017-8
eBook Packages: Springer Book Archive