Multi-objective aerodynamic shape design optimization of canard-controlled missiles for reducing induced roll

Canard-controlled missiles are popular due to their compact size and high maneuverability. However, it is well known that these missiles suffer from adverse roll properties caused by tail-fin-induced roll during canard deflection in roll maneuvers. While auxiliary systems like a free-spinning tail section can mitigate these issues, they have concerns regarding system complexity. This research focuses on optimizing the aerodynamic design of canard-controlled missiles to minimize the adverse roll without introducing additional systems. A multi-objective genetic algorithm (MOGA) is employed as the optimizer, striking a balance between minimizing undesired roll, maintaining static stability, and ensuring maneuverability. Computational fluid dynamics (CFD), employing the Reynolds averaged Navier–Stokes solver, provides an accurate calculation of the rolling moment coefficient related to adverse roll characteristics. To offset the high computational demands of CFD, a surrogate model is used within an optimization framework that incorporates the design of experiments and the Kriging model, coupled with the MOGA optimizer, for a more efficient optimization process. This proposed framework offers a range of optimal Pareto solutions with superior aerodynamic performance, subject to multi-objective functions and constraints. Following optimization, data mining techniques are used to elucidate why these Pareto solutions performed better, identifying key geometric features influencing missile performance and the correlation between objective functions and design variables. The findings highlight that the optimal missile designs should feature a reduced exposed semi-span, a key factor in adverse roll properties, to minimize induced roll while still accounting for stability and maneuverability.