Quantifying Confidence in Model Predictions for Hypersonic Aircraft Structures

Abstract

Lack of confidence in structural response and life predictions of a vehicle exposed to combined extreme environments has consistently prevented the USAF from fielding affordable, reliable, and reusable hypersonic space access platforms. Significant strides have been made in modeling complex interactions of the multi-physics, fluid-thermal-structural coupling applicable to hypersonic flow conditions. However, validation of these models remains a challenge due to limited experimental data for hypersonic conditions. This research addresses fundamental and critical issues in quantifying uncertainty and assessing the confidence in model predictions of hypersonic structural response through a systematic framework. The first year of this research focused on identifying and developing the components of the model uncertainty framework for aerodynamic pressure and heating predictions, including global sensitivity analysis, Bayesian model calibration, and validation metric comparison. The second year emphasized effectively integrating information into the coupled system through segmented Bayesian model calibration. The final year brought together model discrepancy in aerodynamic pressure calibrated from aerothermal experiments with nonlinear structural dynamic reduced order models to investigate the uncertainty and sensitivity in coupled aeroelastic response (i.e., flutter and limit cycle oscillation). ix Approved for public release; distribution unlimited. Approved for public release; distribution unlimited. 1.1 Uncertainty Quantification and Model Prediction Confidence Assessment Quantifying the confidence in model prediction consists of two intertwined, yet distinct, activities: uncertainty quantification (UQ) and verification and validation (V&V). The science of uncertainty quantification for numerical simulations (i.e., the quantitative characterization and reduction of uncertainties) has origins dating back to the early-1990's. However, over the past decade there has been a surge in multiple research communities towards formalizing and generalizing the process. Thus, there are now multiple descriptions and implementations of the UQ and V&V processes that are all similar in their objectives, but different in their details. Due to the nature of UQ and V&V research, it is necessary to establish terminology and research scope. Uncertainty is inherent in all computational model predictions due to imperfect knowledge and physical variability in the system, model order reduction, assumptions and approximations, and the limited experimental data available for model calibration and validation. This is especially the case for compliant structures in hypersonic environments due to the complex and poorly-understood loading and the coupled multi-physics nature of the fluidthermal-structural interaction. Physical variability is incorporated in fluid-thermal-structural models through variations in material properties, geometry, boundary conditions, and load interactions. The aerothermoelastic model prediction also has both model-for...