An Intelligent Framework based on Parametric Reduced-Order Model Database for Uncertainty Quantification in Aeroelasticity

The goal of the proposed effort is to develop an intelligent framework based on parametric reduced-order models (ROM) for uncertainty quantification (UQ) in aeroelasticity (AE). Leveraging significant achievements by the proposing team in prior research, this Phase I effort represents a significant contribution to UQ adoption for AE analysis in NASA, and includes several emerging techniques: a data-driven, non-intrusive process for holistic UQ analysis and software organization; fully automated generation of parametric state-consistent ROMs and stability characteristics in broad stochastic domain; stochastic models to establish uncertain input-output mapping; uncertainty propagation and interval analysis for statistical characterization; and a modular software framework with seamless interface with NASA FUN3D to streamline the entire process. The feasibility of the proposed technology will be demonstrated for AE problems of NASA interest (e.g., flutter onset). Beyond Phase I, efforts will focus on AE UQ engine optimization in terms of execution efficiency, robustness, and autonomy; and refinement of the AE UQ environment and FUN3D integration; and process automation of modeling, simulation, and UQ for technology insertion and transition; and extensive software validation and demonstration for AE UQ of realistic aircrafts of current NASA interest.nbsp;