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This work proposes a novel approach for non-deterministic extension ofexperimental data that considers structural model inadequacy for conditionsother than the calibration scenario while simultaneously resolving anysignificant prior-data discrepancy with information extracted from flightmeasurements. This functionality is achieved through methodical utilization ofmodel error emulators and Bayesian model averaging studies with availableresponse data. The outlined approach does not require prior flight dataavailability and introduces straightforward mechanisms for their assimilationin future predictions. Application of the methodology is demonstrated herein byextending material performance data captured at the HyMETS facility to the MSLscenario, where the described process yields results that exhibit significantlyimproved capacity for predictive uncertainty quantification studies. This workalso investigates limitations associated with straightforward uncertaintypropagation procedures onto calibrated model predictions for the flightscenario and manages computational requirements with sensitivity analysis andsurrogate modeling techniques.

Non-Deterministic Extension of Plasma Wind Tunnel Data Calibrated Model Predictions to Flight Conditions