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Multidisciplinary optimisation of a supersonic transport using design of experiments theory and response surface modelling

Published online by Cambridge University Press:  04 July 2016

A. A. Giunta ,
V. Balabanov ,
D. Haim ,
B. Grossman ,
W. H. Mason ,
L. T. Watson  and
R. T. Haftka
A. A. Giunta
Affiliation:
Multidisciplinary Analysis and Design (MAD) Center for Advanced VehiclesVirginia Polytechnic Institute and State UniversityBlacksburg, Virginia, USA
V. Balabanov
Affiliation:
Multidisciplinary Analysis and Design (MAD) Center for Advanced VehiclesVirginia Polytechnic Institute and State UniversityBlacksburg, Virginia, USA
D. Haim
Affiliation:
Multidisciplinary Analysis and Design (MAD) Center for Advanced VehiclesVirginia Polytechnic Institute and State UniversityBlacksburg, Virginia, USA
B. Grossman
Affiliation:
Multidisciplinary Analysis and Design (MAD) Center for Advanced VehiclesVirginia Polytechnic Institute and State UniversityBlacksburg, Virginia, USA
W. H. Mason
Affiliation:
Multidisciplinary Analysis and Design (MAD) Center for Advanced VehiclesVirginia Polytechnic Institute and State UniversityBlacksburg, Virginia, USA
L. T. Watson
Affiliation:
Multidisciplinary Analysis and Design (MAD) Center for Advanced VehiclesVirginia Polytechnic Institute and State UniversityBlacksburg, Virginia, USA
R. T. Haftka
Affiliation:
Department of Aerospace Engineering, Mechanics and Engineering ScienceUniversity of FloridaGainesville, Florida, USA
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Abstract

The presence of numerical noise in engineering design optimisation problems inhibits the use of many gradient-based optimisation methods. This numerical noise may result in the inaccurate calculation of gradients which in turn slows or prevents convergence during optimisation, or it may promote convergence to spurious local optima. The problems created by numerical noise are particularly acute in aircraft design applications where a single aerodynamic or structural analysis of a realistic aircraft configuration may require tens of CPU hours on a supercomputer. The computational expense of the analyses coupled with the convergence difficulties created by numerical noise are significant obstacles to performing aircraft multidisciplinary design optimisation. To address these issues, a procedure has been developed to create noise-free algebraic models of subsonic and supersonic aerodynamic performance quantities, for use in the optimisation of high-speed civil transport (HSCT) aircraft configurations. This procedure employs methods from statistical design of experiments theory and response surface modelling to create the noise-free algebraic models. Results from a sample HSCT design problem involving ten variables are presented to demonstrate the utility of this method.

Type
Research Article
Information
The Aeronautical Journal , Volume 101 , Issue 1008 , October 1997 , pp. 347 - 356
Copyright
Copyright © Royal Aeronautical Society 1997 

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References

1. Giunta, A.A., Dudley, J.M., Narducci, R., Grossman, B., Haftka, R.T., Mason, W.H. and Watson, L.T. Noisy aerodynamic response and smooth approximations in HSCT design, Proceedings of the 5th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Panama City Beach, FL, 1994, pp 1117–1128.Google Scholar
2. Dudley, J., Huang, X., MacMillin, P.E., Grossman, B., Haftka, R.T. and Mason, W.H. Multidisciplinary optimization of the high-speed civil transport, AIAA Paper 95-0124, 1995.Google Scholar
3. Kaufman, M., Balabanov, V., Burgee, S., Giunta, A.A., Grossman, B., Mason, W.H., Watson, L.T. and Haftka, R.T. Variable-complexity response surface approximations for wing structural weight in HSCT design, AIAA Paper 96-0089, 1996.Google Scholar
4. Venter, G., Haftka, R.T. and Starnes, J.H. Construction of response surfaces for design optimization applications, Proceedings of the 6th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Bellevue, WA, 1996, pp 548–564.Google Scholar
5. Narducci, R., Grossman, B., Valorani, M., Dadone, A. and Haftka, R.T. Optimization methods for non-smoodi or noisy objective functions in fluid design problems, Proceedings of the AIAA 12th Computational Fluid Dynamics Conference, San Diego, CA, 1995, pp 21-32.Google Scholar
6. Gill, P.E., Murray, W. and Wright, M.H. Practical Optimization, Academic Press, San Diego, CA, 1981.Google Scholar
7. Nelder, J.A. and Mead, R. A Simplex method for function minimization, Comput J, 1965, 7, pp 308–313.Google Scholar
8. Jayaram, S., Myklebust, A. and Gelhausen, P. ACSYNT — a standards-based system for parametric computer aided conceptual design of aircraft, AIAA Paper 92-1268, 1992.Google Scholar
9. McCullers, L.A. Aircraft configuration optimization including optimized flight profiles, Proceedings of the Symposium on Recent Experiences in Multidisciplinary Analysis and Optimization, Sobieski, J. (ed), NASA CP-2327, 1984, pp 396–412.Google Scholar
10. Frank, P.D., Booker, A.J., Caudell, T.P. and Healy, M.J. A comparison of optimization and search methods for multidisciplinary design, Proceedings of the 4th AIAA/USAF/NASA/OAI Symposium on Multidisciplinary Analysis and Optimization, Cleveland, OH, 1992, pp 1040–1057.Google Scholar
11. Sobieszczanski-Sobieski, J. and Haftka, R.T. Multidisciplinary aerospace design optimization: survey of recent developments, AIAA Paper 96-0711, 1996.Google Scholar
12. Gilmore, P. and Kelley, C.T. An implicit filtering algorithm for optimization of functions with many local minima, SIAM J Opt, 1995, 5, (2), pp 269–285.Google Scholar
13. Giunta, A.A., Balabanov, V., Haim, D., Grossman, B., Mason, W.H., Watson, L.T. and Haftka, R.T. Wing design for a High-Speed Civil Transport using a design of experiments methodology, Proceedings of the 6th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Bellevue, WA, 1996, pp 168–183.Google Scholar
14. Toomer, C.A., Topliss, M.E. and Hills, D.P. Aerodynamic optimisation using analytic descriptions of the design space, AIAA Paper 96-4141, 1996.Google Scholar
15. Chen, W., Allen, J.K., Schrage, D.P. and Mistree, F. Statistical experimentation methods for achieving affordable concurrent systems design, AIAA J, 1997, 35, (5), pp 893–900.Google Scholar
16. Montgomery, D.C. Design and Analysis of Experiments, John Wiley & Sons, New York, NY, 1976.Google Scholar
17. Myers, R.H. and Montgomery, D.C. Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, New York, NY, 1995.Google Scholar
18. Roux, W.J., Stander, N. and Haftka, R.T. Response surface approximations for structural optimization. Proceedings of the 6th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Bellevue, WA, 1996, pp 565–578.Google Scholar
19. Knill, D.L., Balabanov, V., Grossman, B., Mason, W.H. and Haftka, R.T. Certification of a CFD code for high-speed civil transport design optimization, AIAA Paper 96-0330, 1996.Google Scholar
20. Burgee, S., Giunta, A.A., Balabanov, V., Grossman, B., Mason, W.H., Narducci, R., Haftka, R.T. and Watson, L.T A coarse grained parallel variable-complexity multidisciplinary optimization paradigm, Int J Supercomp App and High Perf Comp, 1996, 10, (4), pp 269–299.Google Scholar
21. DOT Users Manual Version 4.20, Vanderplaats Research & Development, Colorado Springs, CO, 1995.Google Scholar
22. Harris, R.V. An analysis and correlation of aircraft wave drag, NASA TM X-947, 1964.Google Scholar
23. Carlson, H.W., and Walkley, K.B. Numerical methods and a computer program for subsonic and supersonic design and analysis of wings with attainable thrust considerations, NASA CP 3808, 1984.Google Scholar
24. Hogg, R.V., and Ledolter, J. Applied Statistics for Engineers and Physical Scientists, Macmillin, New York, 1992.Google Scholar
25. JMP Users Guide Version 3.1, SAS Institute, Cary, NC, 1995.Google Scholar
26. Craig, J.A. D-optimal design method: final report and user's manual, General Dynamics Report FZM-6777 for Air Force Contract F33615-78-C-3011, 1978.Google Scholar
27. Giunta, A.A., Narducci, R., Burgee, S., Grossman, B., Mason, W.H., Watson, L. T. and Haftka, R.T. Variable-complexity response surface aerodynamic design of an HSCT wing, Proceedings of the 13th AIAA Applied Aerodynamics Conference, San Diego, CA, 1995, pp 994-1002.Google Scholar
28. Box, M.J. and Draper, N.R. Factorial designs, the criterion, and some related matters, Technometrics, 1971, 13, (4), pp 731–742.Google Scholar
29. Mitchell, T.J. An algorithm for the construction of D-optimal experimental designs, Technometrics, 1974, 16, (2), pp 203–210.Google Scholar
30. Hutchison, M.G. Multidisciplinary Optimization of High-Speed Civil Transport Configurations Using Variable-Complexity Modeling, PhD Dissertation, Virginia Polytechnic Institute and State University, 1993.Google Scholar