Hypersonic Vehicle Design Based on Mode Decomposition

An inverse aerodynamic design method for a hypersonic vehicle is presented by applying modal decomposition to both the dataset of vehicle geometry and aerodynamic coefficients obtained from computational fluid dynamics (CFD) analysis. Surrogate models are constructed by decomposing the dataset of the level set function representing vehicle shape and the aerodynamic coefficients (lift, drag, and pitching moment coefficients) across a wide range of Mach numbers and angles of attack. Regression analysis links the mode coefficients of geometry and aerodynamic coefficients, enabling the estimation of aerodynamic performance from geometry and vice versa. The approach is demonstrated using wedge-shaped and cone waverider configurations, with the design goal of combining their favorable aerodynamic characteristics. Numerical results show that the estimated lift and drag coefficients under supersonic conditions agree well with CFD results, while discrepancies remain in transonic and subsonic regimes, attributed to limited training data and nonlinear effects. The proposed framework offers a promising direction for efficient inverse design and optimization of hypersonic vehicles based on limited training data, with future work focusing on improving estimation accuracy through clustering analysis and expanded training datasets.