Computational Models for Nonlinear Aeroelastic Systems

Clear Science Corp. and Duke University propose to develop and demonstrate new and efficient computational methods of modeling nonlinear aeroelastic systems. The methods will extend the scope of multi-disciplinary computational tools like NASA Dryden's STARS by augmenting linear eigenmode stability algorithms and coupled time-marching techniques. The objective is low-dimensional models that accurately reflect nonlinearity in both structure and fluid and that are efficient enough to permit full exploration of parameter space. In Phase I, our team has demonstrated the technical merit and feasibility of two types of model order reduction: proper orthogonal decomposition (POD) of the coupled-system variables and the method of harmonic balancing (HB). The HB and POD methods represent complementary and synergistic tracks toward meeting the objective of modeling fully nonlinear aeroelastic systems, providing the capability of efficiently analyzing important nonlinear aeroelastic behavior like limit cycle oscillations, hysteresis, higher harmonic and sub-harmonic resonances, jump resonances, entrainment, beating, and period doubling. The innovation offers the possibility of engineering software for predicting nonlinear aeroelastic behavior that is as accurate and efficient as the current methods of analyzing linear systems and linear behavior like flutter. In Phase II, we propose to integrate the POD and HB methods into a single package that facilitates engineering trade-studies early in the design process.