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Uncertainty based aircraft derivative design for requirement changes

Published online by Cambridge University Press:  29 February 2016

H.-U. Park ,
J. Chung  and
D. Neufeld
H.-U. Park*
Affiliation:
Department of Aerospace Engineering, Ryerson University, Toronto, Ontario, Canada
J. Chung
Affiliation:
Department of Aerospace Engineering, Ryerson University, Toronto, Ontario, Canada
D. Neufeld
Affiliation:
Aircraft Design & Certification, Reichensteinstrasse, Neckargemund, Germany
*
ainasul@gmail.com
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Abstract

Aircraft manufacturers often consider producing multiple derivatives of aircraft to satisfy various market demands and technical changes while keeping development costs and time to a minimum. Many approaches have been proposed for carrying out derivative design. However, these approaches consider both the baseline design and derivatives together at the conceptual design stage using the entire set of design variables with an assumed set of expected requirements. These frozen requirements on derivative design cannot consider new demands from market changes. In this paper, a method is proposed that uses design optimisation for conceptual design of derivatives for existing aircraft that consider requirement changes. Furthermore, the Possibility-Based Design Optimisation (PBDO) method was implemented to consider uncertainty in the aircraft operation phase. The altitude range of aircraft operation was defined as an uncertain parameter to prevent violation of constraints in the entire operating envelope of the aircraft. The PBDO method yields a more conservative design than those obtained with deterministic design optimisation.

In this paper, the proposed derivative design process was applied to the Expedition 350, a small piston engine powered aircraft produced by Found Aircraft, Canada. A derivative that changes the normally aspirated engine to a turbocharged engine for high-altitude operation was considered. An optimum configuration with the new engine was obtained while enhancing performance and stability characteristics. The proposed derivative design process can be implemented on the derivative design of other aircraft.

Keywords

Possibility-Based Design OptimisationUncertaintyAircraft Conceptual DesignDerivative Design
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1224 , February 2016 , pp. 375 - 389
Copyright
Copyright © Royal Aeronautical Society 2016 

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References

REFERENCES

1.Robinson, D.L. and Melary, M.F. Large airplane derivative development methodology, AIAA/AHS/ASEE Aircraft Design Systems and Operations Meeting, 14-16 October 1985, Colorado Springs, Colorado, US.CrossRefGoogle Scholar
2.Hibma, R. and Wegner, D. The evolution of a strategic bomber, AlAA 1981 Annual Meeting and Technical Display, 12-14 May 1981, Long Beach, California, US.CrossRefGoogle Scholar
3.Fulford, R.H. Airplane criteria process, World Aviation Congress & Exposition, 21-24 October 1997, Anaheim, California, US.CrossRefGoogle Scholar
4.Birrenbach, R. Regional aircraft family design, 2000 World Aviation Conference, 10-12 October, 2000, San Diego, California, US.CrossRefGoogle Scholar
5.Brown, R.B. and Swihart, J.M. A new family of passenger friendly commercial air transports, 39th AIAA Aerospace Sciences, Meeting & Exhibit, 8-11 January 2001, Reno, Nevada, US.CrossRefGoogle Scholar
6.Kumar, D., Chen, W. and Simpson, T.W. A market-driven approach to the design of platform-based product families, 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 6-8 September 2006, Portsmouth, Virginia, US.CrossRefGoogle Scholar
7.Yearsley, J.D. and Mattson, C.A. Product family design using a smart Pareto filter, 46th AIAA Aerospace Sciences Meeting and Exhibit, 7-10 January 2008, Reno, Nevada, US.CrossRefGoogle Scholar
8.Yearsley, J.D. and Mattson, C.A. Interactive design of combined scale-based and module-based product family platforms, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 10-12 September 2008, Victoria, British Columbia, Canada.Google Scholar
9.Simpson, T.W. and D'Souza, B.S. Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 4-6 September 2002, Atlanta, Georgia, US.CrossRefGoogle Scholar
10.Valliyappan, S. and Simpson, T.W. Exploring visualization strategies to support product family design optimization, 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 6-8 September 2006, Portsmouth, Virginia, US.CrossRefGoogle Scholar
11.Khajavirad, A., Michalek, J.J. and Simpson, T.W. A decomposed genetic algorithm for solving the joint product family optimization problem, 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 23-26 April 2007, Honolulu, Hawaii, US.CrossRefGoogle Scholar
12.Lim, D. and Mavris, D.N.An Approach to Evolutionary Aircraft Design for Growth Potential, 7th AIAA Aviation Technology, Integration and Operations Conference (ATIO), 18-20 September 2007, Belfast, Northern Ireland.Google Scholar
13.Allison, J., Roth, B., Kokkolaras, M., Kroo, I.M. and Papalambros, P.Y. Aircraft family design using decomposition-based methods, 11 AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 6-8 September 2006, Portsmouth, Virginia, US.CrossRefGoogle Scholar
14.Freeman, D., Lim, D., Garcia, E. and Mavris, D.N. Methodology for the design of unmanned aircraft product families, 28th International Congress of the Aeronautical Sciences, 23-28 September 2012, Brisbane, Australia.Google Scholar
15.Edi, P.Aircraft family concept for high performance transport aircrafts, Int J Mech, 2012, 6, (3), pp 195–202.Google Scholar
16.Pate, D.J., Patterson, M.D. and German, B.J., Optimizing families of reconfigurable aircraft for multiple mission, J Aircraft, 2012, 49, (6), pp 1988–2000.CrossRefGoogle Scholar
17.Moon, S.K., Park, K.J. and Simpson, T.W.Platform design variable identification for a product family using multi-objective particle swarm optimization, Res Eng Des, 2014, 25, pp 95–108.CrossRefGoogle Scholar
18.Youn, B.D., Choi, K.K. and Du, L.Enriched performance measure approach for reliability-based design optimization, 2005, AIAA J, 43, (4), pp 874–884.CrossRefGoogle Scholar
19.Agarwal, H. Reliability Based Design Optimization: Formulations and Methodologies, PhD Thesis, University of Notre Dame, 2004.Google Scholar
20.Du, L., Choi, K.K., Youn, B.D., and Gorsich, D.Possibility-based design optimization method for design problems with both statistical and fuzzy input data, J Mech Des, 2006, 128, (4), pp 928–936.CrossRefGoogle Scholar
21.Found Aircraft, http://www.foundair.com.Google Scholar
22.Neufeld, D., Nhu-Van, N., Lee, J.W. and Kim, S. A multidisciplinary possibility approach to light aircraft conceptual design, 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 23-26 April 2012, Honolulu, Hawaii, US.CrossRefGoogle Scholar
23.Choi, K.K. and Youn, B.D. Hybrid analysis method for reliability-based design optimization, 27th ASME Design Automation Conference, 9-12 September 2001, Pittsburgh, Pennsylvania, US.CrossRefGoogle Scholar
24.Savoia, M., Structural reliability analysis through fuzzy number approach, with application to stability, Comput & Struct, 2002, 80, (12), pp 1087–1102.CrossRefGoogle Scholar
25.Youn, B.D., Choi, K.K. and Park, Y.H.Hybrid analysis method for reliability-based design optimization, J Mech Des, 2003, 125, (2), pp 221–232.CrossRefGoogle Scholar
26.Choi, K.K., Du, L. and Youn, B.D. A new fuzzy analysis method for possibility-based design optimization, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 30 August-1 September 2004, Albany, New York, US.CrossRefGoogle Scholar
27.Zhao, D. and Xue, D.Parametric design with neural network relationships and fuzzy relationships considering uncertainties, Comput Ind, 2010, 61, pp 287–296.CrossRefGoogle Scholar
28.Zadeh, L.A.Fuzzy sets, Inf Control, 1965, 8, (3), pp 338–353.CrossRefGoogle Scholar
29.Flight Test Guide for Certification of Part 23 Airplanes, 2011, Advisory Circular, Federal Aviation Administration, Washington, District of Columbia, US.Google Scholar
30.AndersonJ.D., Jr. J.D., Jr.Aircraft Performance and Design, 1999, McGraw-Hill Toronto, Canada.Google Scholar
31.Nelson, R.C.Flight Stability and Automatic Control, 2nd ed, 1998, McGraw-Hill Toronto, Canada.Google Scholar
32.Jaeger, L., Gogu, C., Segonds, S. and Bes, C.Aircraft multidisciplinary design optimization under both model and design variables uncertainty, J Aircraft, 2013, 50, (2), pp 528–538.CrossRefGoogle Scholar
33.Park, H.U., Chung, J., Lee, J.W. and Behdinan, K.Uncertainty based MDO for aircraft conceptual design, Aircr Eng Aerosp Technol, 2015, 87 (4), pp 345–356.CrossRefGoogle Scholar
34.Park, H.U., Chung, J., Behdinan, K. and Lee, J.W.Multidisciplinary wing design optimization considering global sensitivity and uncertainty of approximation models, J Mech Sci Technol, 2014, 28, (6), pp 2231–2242.CrossRefGoogle Scholar
35.MIL-F-8785C, Military Specification: Flying Qualities of Piloted Airplanes, 1980, Department of Defense Military Specifications and Standards, Philadelphia, US.Google Scholar