This work investigates the modelling of epistemic input parameter uncertainties, and the numerical techniques for uncertainty quantification and uncertainty-based sensitivity analysis in the presence of both aleatory and epistemic uncertainties. Two different approaches are used, the interval valued probability (IVP) method and the Bayesian probabilistic (BP) method. In both cases, a double loop method is used to computationally separate the two different uncertainty types and propagate them within the model. These two approaches are successfully applied on a high-dimensional jet engine secondary air system model from aerospace engineering. The different outputs obtained by the two approaches are interpreted and compared. For the global sensitivity analysis of the epistemic variables, an empirical “pinching” strategy is applied when using the IVP method. With the BP method, variance-based global sensitivity analysis of the epistemic variables is performed. Novel expressions for the Sobol indices of a statistic of a response, conditional on the epistemic variables, are presented and interpreted.     «

This work investigates the modelling of epistemic input parameter uncertainties, and the numerical techniques for uncertainty quantification and uncertainty-based sensitivity analysis in the presence of both aleatory and epistemic uncertainties. Two different approaches are used, the interval valued probability (IVP) method and the Bayesian probabilistic (BP) method. In both cases, a double loop method is used to computationally separate the two different uncertainty types and propagate them wi...     »