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Multivariate Search and Its Application to Aircraft Design Optimisation

Published online by Cambridge University Press:  04 July 2016

W. Z. Stepniewski  and
C. F. Kalmbach
W. Z. Stepniewski
Affiliation:
The Boeing Company, Vertol Division and Princeton University
C. F. Kalmbach
Affiliation:
The Boeing Company, Vertol Division and Princeton University
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Extract

Long before the legendary Queen Dido cleverly maximised the area of future Carthage by choosing the optimum perimeter shape, man probably consciously or subconsciously tried to optimise his designs as well as his actions.

In a more modern time, the post Renaissance period witnessed development of the principles of optimisation on a rigorous mathematical basis, as exemplified in works of Newton, Leibniz, Euler and many others. Eventually, those optimisation ideas were even transplanted by Leibniz and his school to the field of general philosophy, providing an attractive target for the merciless satire of Voltaire in Candide.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 74 , Issue 713 , May 1970 , pp. 419 - 432
Copyright
Copyright © Royal Aeronautical Society 1970 

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References

1. Wilde, D. J. and Beightler, C. S. Foundations of Optimisation. Prentice-Hall, Inc, 1967.Google Scholar
2. Wilde, D. J. Optimum Seeking Methods. Prentice-Hall, Inc, 1964.Google Scholar
3. Textbooks on Optimisation:Google Scholar
(a) Bellman, R. E. Dynamic Programming. Princeton Univ Press, 1957.Google Scholar
(b) Pontryagin, L. S., et al. The Mathematical Theory of Optimal Processes. Interscience Publishers, 1962.Google Scholar
(c) Bellman, R. and Dreyfus, S. E.. Applied Dynamic Programming. Princeton Univ Press, 1962.Google Scholar
(d) Leitman, G. Optimization Techniques. Academic Press, 1962.Google Scholar
(e) Dantzig, G. B. Linear Programming and Extensions. Princeton Univ Press, 1963.Google Scholar
(j) Fan, Liang-Tseng and Wang, Chiu-Sen. The Discrete Maximum Principle. John Wiley & Sons, 1964.Google Scholar
(g) Fan, Liang-Tseng. The Continuous Maximum Principle. John Wiley & Sons, 1964.Google Scholar
(h) Duffin, R. J., Peterson, E. L. and Zener, C. Geometric Programming—Theory and Application. John Wiley & Sons, 1967.Google Scholar
(i) Bryson, A. E. and Ho, Y. C. Applied Optimal Control. Blaisdell, 1969.Google Scholar
(j) Luenberger, David G. Optimisation by Vector Space Methods. Wiley-Interscience, 1969.Google Scholar
4. Krzywoblocki, M. Z. Laws of Dynamic Systems and Optimization. Revue Roumaine des Sciences Techniques; Editions de L'Academie de la Republique Socialiste de Roumanie; Tome 13, 1968.Google Scholar
5. AIAA Conference. New Orleans. 16th April 1969:Google Scholar
(a) Turner, M. J. Optimization of Structures to Satisfy Flutter Requirements.Google Scholar
(b) Rubin, C. P. Dynamic Optimization of Complex Structures.Google Scholar
(c) Fox, R. L. and Kapour, M. P. Structural Optimization in the Dynamics Response Regime; A Computational Approach.Google Scholar
(d) McCart, B. R., Hauo, E. J. and Streeter, T. D. Optimal Design of Structures with Constraints on Natural Frequency.Google Scholar
(e) JrMcIntosh, S. C., Weisshaar, T. A. and Ashley, H. Progress in Aeroelastic Optimization—Analytical versus Numerical Approaches.Google Scholar
6. General: Princeton Iterator.Google Scholar
(a) Campbell, J., Moore, W. E. and Wol, H. Minimax— A General Purpose Adaptive Iterator for Nonlinear Problems. Analytical Mechanics Associates, Inc. June, 1964.Google Scholar
(b) Lion, P. M., Campbell, J. H. and Shulzycki, A. M. Trajectory Optimization Program. Princeton Univ Report 7175, March 1968.Google Scholar
7. Hague, D. S. and Glatt, C. R. An Introduction to Multivariate Search Techniques for Parameter Optimization. NASA CR-73200, April 1968.Google Scholar
8. A Guide to the Automated Engineering and Scientific Optimization Program. NASA CR-73201.Google Scholar
9. Application of Multivariable Search Techniques to the Optimal Design of a Hypersonic Cruise Vehicle. NASA CR-73202, April 1968.Google Scholar
10. Application of Multivariable Search Techniques to the Shaping of Minimum Total Heat Re-entry Bodies at Hyperbolic Velocity. NASA CR-73203, April 1968.Google Scholar
11. Lasdon, L. S. and Waren, A. Mathematical Programming for Optimal Design. Electro-Technology, November 1967.Google Scholar
12. Fletches, R. and Powell, M. J. D. A Rapidly Convergent Descent Method for Minimisation. British Computer Journal, Vol. 6, 1963.Google Scholar
13. Krzywoblocki, M. Z. and Stepniewski, W. Z. Application of Optimization Techniques to the Design and Operation of V/STOL Aircraft. Proceedings of the International Congress of Subsonic Aeronautics. The New York Academy of Sciences, NY, April 1967.Google Scholar
14. Davenport, F., Magee, J. and Austin, W. Analysis of Propeller and Rotor Performance in Static and Axial Flight by an Explicit Vortex Influence Technique. Boeing Report R-372, Rev. B.Google Scholar
15. Magee, J., Maisel, M. and Davenport, F. The Design and Performance Prediction of Propeller/ Rotors for VTOL Applications. AHS No. 325, May 1969.Google Scholar
16. Jones, R. T. A Guide to the Use of SEAL. A. Special Engineering Analysis Language. Boeing Report D2-139592-1.Google Scholar