AERODYNAMIC AND AEROACOUSTIC OPTIMIZATION OF AIRFOI LS VIA A PARALLEL GENETIC ALGORITHM

A parallel genetic algorithm (GA) was used to generate, in a single run, a family of aerodynamically efficient, low-noise rotor blade designs representing th e Pareto optimal set. The n-branch tournament, uniform crossover genetic algorithm operates on twenty design variables, which constitute the control points for a spline representing the airfoil surface. The GA takes advantage of available computer resources by operating in either serial mode or “manager/worker” parallel mode. The multiple objectives of this work were to maximize lift-to-drag of a rotor airfoil shape and to minimize an overall noise measure including effects of loading and thickness noise of the airfoil. Constraint s are placed on minimum lift coefficient, pitching moment and boundary layer convergence. The program XFOIL provides the aerodynamic analysis, and the code WOPWOP provides the aeroacoustic analysis. The Pareto-optimal airfoil set has been generated and is compared to the performance of a typical rotorcraft airfoil under identical flight conditions.

[1]  T. L. Vincent,et al.  Optimality in parametric systems , 1981 .

[2]  G. P. Succi,et al.  The prediction of helicopter rotor discrete frequency noise , 1982 .

[3]  Garret N. Vanderplaats,et al.  Numerical Optimization Techniques for Engineering Design: With Applications , 1984 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[6]  M. Drela.  XFOIL: An Analysis and Design System for Low Reynolds Number Airfoils , 1989 .

[7]  Kenneth S. Brentner,et al.  Helicopter noise prediction - The current status and future direction , 1994 .

[8]  D. Quagliarella,et al.  Genetic algorithms applied to the aerodynamic design of transonic airfoils , 1994 .

[9]  V. L. Wells,et al.  Acoustic design of rotor blades using a genetic algorithm , 1995 .

[10]  William Crossley,et al.  Genetic Algorithm approaches for multiobjective design of rotor systems , 1996 .

[11]  S. Obayashi,et al.  Genetic optimization of target pressure distributions for inverse design methods , 1996 .

[12]  Denis J. Doorly,et al.  Supervised Parallel Genetic Algorithms in Aerodynamic Optimisation , 1997, ICANNGA.

[13]  William A. Crossley,et al.  Empirically-Derived Population Size and Mutation Rate Guidelines for a Genetic Algorithm with Uniform Crossover , 1998 .

[14]  William A. Crossley,et al.  Aerodynamic Shape Design for Rotor Airfoils via Genetic Algorithm , 1998 .

[15]  Stephen Norris,et al.  Pareto-optimal controller gains generated by a genetic algorithm , 1998 .

[16]  William A. Crossley,et al.  Using the Two-Branch Tournament Genetic Algorithm for Multiobjective Design , 1999 .