A Data-Driven Body Vortex Modeling for Engineering-Level Missile Aerodynamic Prediction

Abstract Modeling body vortex is essential for accurate missile aerodynamic prediction at high angles of attack, where separated flow dominates. This study investigates the impacts of body vortex on engineering-level missile aerodynamic prediction and compares the results with those of a semi-empirical vortex model. A vortex-tracking model based on two-dimensional potential flow is implemented in Missile DATCOM, with vortex strength and separation locations provided by an artificial neural network trained on body-alone CFD databases. The implemented method improves the prediction of local angle-of-attack changes near the body and yields more accurate nonlinear aerodynamic forces across body–fin and body–canard–tail configurations. Compared to the semi-empirical model, it better captures vortex interference, particularly for fins with low semispan. Notable improvements are observed in high angle-of-attack regimes and under roll control conditions.