Experimental Verification of an Optimized Design of Air Intake of a Sub-Sonic Cruise Missile

Abstract

Aerodynamic design and optimization of intake of a subsonic cruise missile have been carried out for a specific cruise condition. A hybrid optimization technique by combining both the Artificial Neural Network (ANN) method and the Genetic Algorithm (GA) has been used for intake design studies. Subsonic intake geometric parameterization is conducted and a generic intake for a cruise missile is constructed by the CATIA V5 generative shape design module. The developed hybrid optimization algorithm is validated using the experimental method in a subsonic wind tunnel and by using the random initial intake geometry by all design parameters free. Intake geometries are compared to each other which are obtained from optimization iterations. CFD analyses are conducted at an engine-corrected mass flow rate of about 10 kg/s. Optimized geometry reached 0.9 Pressure Recovery (PR) coefficient and 0.0262 distortion coefficient. Experimental verification of the result obtained from the optimized intake produced a satisfactory match with a difference of less than 5%. The circumferential distortion is less than 0.05 and radial distortion is less than 0.4 for both numerical and experimental analysis. Three key milestones such as CFD simulation using Fluent® solver, hybrid optimization using GA-ANN combination and experimental validation using subsonic wind tunnel were achieved in the study.