Missile Aerodynamics Optimization Using Genetic Algorithm

Abstract

This study presents a numerical optimization framework that integrates a Genetic Algorithm (GA) with the Missile Aerodynamic Prediction Program (MAPP) to enhance the aerodynamic performance of wing-body configurations at supersonic speeds. MAPP, based on USAF DATCOM methods, predicts longitudinal aerodynamic coefficients including lift, and drag. The GA shell was coupled with MAPP via a FORTRAN subroutine to maximize the lift-to-drag ratio (L/D) for wing-alone, body-alone, and wing-body configurations under geometric constraints.

Validation against experimental data at Mach 1.99 showed excellent agreement. Optimization results demonstrated significant performance gains: wing-alone configurations achieved L/D improvements of 9.5–21.2%; body-alone designs showed dramatic gains up to 81.7% at Mach 2.1; and wing-body combinations attained a 34.1% improvement. This integrated GA-MAPP tool offers an efficient, reliable method for supersonic aerodynamic shape optimization, with strong potential for application in advanced aircraft design.