Scientific and Technical Aerospace Reports Volume 38 July 28, 2000

sonic and transonic cases will be presented. Flexibility in the use of the design variables allows many different tests to be performed
before the best solution is achieved. Lastly, the computational cost is reduced by the use of a low level code for computing the
aerodynamic coefficients.
Author
Wings; Design Analysis; Optimization; Subsonic Flow; Transonic Flow
20000061439 Italian Aerospace Research Center, Capua, Italy
A Multiobjective Approach to Transonic Wing Design by Means of Genetic Algorithms
Vicini, A., Italian Aerospace Research Center, Italy; Quagliarella, D., Italian Aerospace Research Center, Italy; Aerodynamic
Design and Optimisation of Flight Vehicles in a Concurrent Multi-Disciplinary Environment; June 2000, pp. 22-1 - 22-12; In
English; See also 20000061419; Copyright Waived; Avail: CASI; A03, Hardcopy
In this work a transonic wing design problem is faced by means of a multiobjective genetic algorithm, and using a full potential
flow model. The applications here presented regard both planform and wing section optimization. It is shown how both geometric
and aerodynamic constraints can be taken into account, and how the multiobjective approach to optimization can be an effective
way to handle conflicting design criteria. An interpolation technique allowing a better approximation of Pareto fronts is described.
Two possible ways of improving the computational efficiency of the genetic algorithm, namely a parallel implementation of the
code and a hybrid optimization approach, are presented.
Author
Aircraft Design; Wings; Design Analysis; Transonic Flow; Genetic Algorithms
20000061440 National Research Council of Canada, Aerodynamics Lab., Ottawa, Ontario Canada
Application of Micro Genetic Algorithms and Neural Networks for Airfoil Design Optimization
Tse, Daniel C. M., National Research Council of Canada, Canada; Chan, Louis Y. Y., National Research Council of Canada, Canada;
Aerodynamic Design and Optimisation of Flight Vehicles in a Concurrent Multi-Disciplinary Environment; June 2000, pp.
23-1 - 23-11; In English; See also 20000061419; Copyright Waived; Avail: CASI; A03, Hardcopy
Genetic algorithms are versatile optimization tools suitable for solving multi-disciplinary optimization problems in aerodynamics
where the design parameters may exhibit multi-modal or non-smooth variations. However, the fitness evaluation phase
of the algorithms casts a large overhead on the computational requirement and is particularly acute in aerodynamic problems
where time-consuming CFD methods are needed for evaluating performance. Methods and strategies to improve the performance
of basic genetic algorithms are important to enable the method to be useful for complicated three-dimensional or multi-disciplinary
problems. Two such methods are studied in the present work: micro genetic algorithms and artificial neural networks. Both
methods are applied to inverse and direct airfoil design problems and the resulting improvement in efficiency is noted and discussed.
Author
Genetic Algorithms; Neural Nets; Airfoils; Design Analysis; Optimization
20000061441 Daimler-Benz Aerospace A.G., Munich, Germany
Multi-Objective Aeroelastic Optimization
Stettner, M., Daimler-Benz Aerospace A.G., Germany; Haase, W., Daimler-Benz Aerospace A.G., Germany; Aerodynamic
Design and Optimisation of Flight Vehicles in a Concurrent Multi-Disciplinary Environment; June 2000, pp. 24-1 - 24-8; In
English; See also 20000061419
Contract(s)/Grant(s): ESPRDIT Proj. 20082; Copyright Waived; Avail: CASI; A02, Hardcopy
The present work is aiming at an aeroelastic analysis of the X31 delta wing and particularly at the aeroelastic optimization
problem of maximizing the aerodynamic roll rate and minimizing the structural weight at supersonic flow speeds. Results are
achieved by means of a multi-objective genetic algorithm (GA) utilizing a GUI-supported software being developed in the European-Union
funded ESPRIT project FRONTIER.
Author
Aeroelasticity; Optimization; Delta Wings; Genetic Algorithms
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