Error Quantification and Confidence Assessment of Aerothermal Model Predictions for Hypersonic Aircraft

Abstract : Assessing prediction confidence and enabling its use as a decision-making metric for autonomous model fidelity selection is essential to the USAF's vision of a Digital Twin as a viable approach for condition-based fleet management by tail number. 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 quantifying errors and assessing the confidence in aerodynamic pressure and heating predictions for a spherical dome protruding from a flat ramp. Well-characterized aerothermal test data from hypersonic wind tunnel experiments are used to calibrate uncertain model parameters and quantify errors through Bayesian techniques. A Bayesian hypothesis testing-based confidence metric is employed to compare the accuracy in various model predictions. A model selection study is performed for 1st-, 2nd-, and 3rd-order piston theories. The results showed that the greatest confidence in model predictions does not necessarily correspond to the highest-order model.