Airfoil shape optimization using genetic algorithms and parallel CFD

This paper applies a real-valued genetic algorithm (GA) and computational fluid dynamics (CFD) to the task of airfoil shape optimization. The airfoil profiles are modeled by a combination of Bezier curves, whose control points are used as real-valued genes. The fitness function for maximization is an appropriate combination of the lift and drag coefficients. The required lift and drag coefficients are obtained by parallel incompressible Navier-Stokes computations on a 8-node P-II PC cluster using MPI calls. A master node is responsible for executing the GA procedure that generates new chromosomes (airfoil shapes), distributing chromosomes evenly to computational nodes in the cluster and collecting the computed lift and drag coefficients for the GA procedure. On each individual computational node, a grid generator and a flow solver is called to calculate the lift and drag coefficient for each chromosome received. Optimization attempt on the performance of a general aviation airfoil LS0417 is tried as an example case.