Uncertainty Quantification and Error Estimation in Scramjet Simulation

The numerical prediction of scramjet in-flight performance is a landmark example in which current simulation capability is overwhelmed by abundant uncertainty and error. The aim of this work is to develop a decision-making tool for balancing the available computational resources in order to equally reduce the effects of all sources of uncertainty and error below a confidence threshold. To that end, a nested uncertainty quantification and error estimation loop is proposed that balances aleatoric uncertainty, epistemic uncertainty, and numerical error in an efficient way. Application to a nozzle flow problem shows a reduction of the confidence interval by three orders of magnitude. The framework applied to the HyShot II scramjet flight experiment validation simulation indicates that the epistemic uncertainty in the RANS turbulence model is the dominating contribution to the confidence interval.