Enhancement of Grid and Face Element Amendment in Aeroelasticity Analysis by Advanced Self-Adaptive Computing Method

Abstract

This research presents a novel approach for enhancing the accuracy and efficiency of aeroelasticity analysis through the combination of grid optimization and face element amendment techniques. The study focuses on improving the precision of a low order panel method by performing piecewise linear amendment based on Computational Fluid Dynamics (CFD) aerodynamic loads data under varying angles of attack. Additionally, the distribution of face element calculating grid is optimized using visual evoked potential estimation. The proposed method addresses the limitations of traditional panel methods that heavily rely on grid distribution and offers a more robust and accurate solution. The optimized grid not only maintains the computational efficiency of the panel method but also ensures closer approximation of wing stresses to CFD data, thus enhancing the precision and efficiency of aeroelasticity optimized iterative design processes. The results obtained from this research demonstrate effective compensation for the shortcomings of the panel method and a significant improvement in the computational efficiency of aerodynamic loading in the presence of changing structural stiff parameters.