Improved vortex lattice method for drag prediction of supersonic wings using shock cone modelling

Abstract In the realm of supersonic design, obtaining data for numerous supersonic configurations amidst intricate flow conditions proves time-consuming due to the excessive costs associated with high-fidelity computational demands. Running iterative simulations over an extended period is often impractical or entails substantial expenses. This inherent challenge necessitates the adoption of low-order potential solvers with reasonable accuracy to generate datasets. In support of this objective, This study addresses the high computational costs of obtaining data for supersonic configurations by developing a low-order solver that combines the Taylor-Maccoll hypervelocity method (TMHM) with the supersonic vortex lattice method. This approach aims to provide accurate drag predictions in supersonic flows while minimizing computational demands. By integrating TMHM to calculate wave drag and skin friction drag and enhancing the vortex lattice method to handle shockwave impacts through panel matching, the solver achieves improved accuracy in lift and drag computations. Validation against experimental data shows a 20% reduction in drag prediction error compared to traditional vortex lattice methods, with a 2.01% error for low-shock angles. The method achieves accuracy rates between 90% and 95% across various configurations, including a 90% accuracy for delta wings, 85% for positive dihedral wings, and 95% for large sweptback angle designs, as confirmed by comparisons with high-fidelity CFD data.