Ranking Based Uncertainty Quantification for a Multifidelity Design Approach

Abstract

Computer simulation based design processes are being extensively used in complex systems like scramjet powered hypersonic vehicles. The computational demands associated with the high-fidelity analysis tools for predicting the system performance restrict the number of simulations that are possible within the design cycle time. Hence, analysis tools of lower fidelity are generally used for design studies. To enable the designer to make better design decisions in such situations, the lower fidelity analysis tool is complemented with an uncertainty model. An approach based on ranks is proposed in this study to aggregate high-fidelity information in a cost effective manner. Based on this information, a cumulative distribution function for the difference between high-fidelity response and low-fidelity response is constructed. The approach is explained initially for uncertainty quantification in a synthetic example. Subsequently an uncertainty model for estimating the mass flow capture of air, a typical disciplinary performance metric in hypersonic vehicle design, is presented.