Missile aerodynamic shape optimization and sensitivity analysis using evolutionary algorithms

This article presents a simulation and optimization framework designed to identify the optimal external geometry of candidate launchers for space during conceptual design phase. The framework employs statistical analysis on an archive generated through the optimization process to identify and rank the most influential control variables. This is accomplished by storing and post-processing the chromosomes produced by the genetic algorithm, using quantitative methods such as eta-squared and correlation techniques, to reveal the relationships and effects of each control variable on aerodynamic efficiency. The resulting rankings are compared with a rapid sensitivity analysis to assess the practicality and effectiveness of these methods. This approach improves the understanding of how design variables influence overall performance, offering valuable insights for refining and optimizing aerospace conceptual designs. A semi-analytical method is used for aerodynamic prediction, enabling the calculation of aerodynamic coefficients, which supports trajectory simulation through the integration of equations of motion.