The objective of this paper is to describe a method for selecting optimal engine technology solution sets while simultaneously accounting for the presence of technology risk. This method uses a genetic algorithm in conjunction with technology identification, evaluation, and selection methods to find optimal combinations of technologies. The unique feature of this method is that the technology evaluation itself is probabilistic in nature. This allows the performance impact and associated risk of each technology to be quantified in terms of a distribution on key engine technology metrics. The resulting method can best be characterized as a concurrent genetic algorithm/Monte Carlo analysis that yields a performance and risk-optimal technology solution set. This solution set is inherently a robust solution because the method will naturally strive to find those technologies representing the best compromise between performance improvement and technology risk. Finally, a practical demonstration of the method and accompanying results is given for a typical commercial aircraft engine technology selection problem.

Issue Section:
Technical Papers
Keywords:
aerospace engines, aircraft, genetic algorithms, Monte Carlo methods, risk analysis, gas turbines, Weibull distribution
Topics:
Aircraft engines, Risk, Engines, Genetic algorithms
1.
Roth, B. A., German, B. J., and Mavris, D. N., 2001, “Adaptive Selection of Engine Technology Solution Sets From a Large Combinatorial Space,” 2001 AIAA/ASME/SAE Joint Propulsion Conference, Salt Lake City, UT, July 8–11, Paper No. AIAA2001-3208.
2.
Roth, B., and Mavris, D. N., 2001, “Commercial Engine Architecture Selection in the Presence of Uncertainty and Evolving Requirements,” 15th International Society of Air Breathing Engines, Bangalore, India, Sept. 2–7, Paper No. ISABE2001-1169.
3.
Kirby, M. R., and Mavris, D. N., 2000, “A Method for Technology Selection Based on Benefit, Available Schedule and Budget Resources,” 5th World Aviation Congress and Exposition, San Diego, Oct., Paper No. AIAA2000-01-5563.
4.
Goldberg, D., 1989, Genetic Algorithms in Search, Optimization, & Machine Learning, Addison-Wesley, Reading, MA, p. 27.
5.
Kirby, M. K., and Mavris, D. N., 1999, “Forecasting Technology Uncertainty in Preliminary Aircraft Design,” 4th World Aviation Congress and Exposition, San Francisco, CA, Oct. 19–21, Paper No. SAE1999-01-5631.
Copyright © 2004
 by ASME
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