Uncertainty Quantification in Viscous Hypersonic Flows using Gradient Information and Surrogate Modeling

Abstract

In this paper, the use of gradient observations in conjunction with surrogate models for the purposes of uncertainty quantification within the context of viscous hypersonic flows is examined. The approach is presented within the context of both perfect gas and nonequilibrium real gas simulations and can be used to quantify the uncertainty in an output of interest due to the uncertainty associated with various model and design parameters. The gradient of an objective is calculated via a discrete adjoint approach. By using an adjoint based approach, the sensitivity of an objective to a large number of input parameters can be calculated in an efficient and timely manner. With these sensitivity derivatives, the uncertainty in an objective due to input parameters is calculated in various ways. Initially, first-order methods, such as the method of moments and linear extrapolation, are used to represent the design space and calculate relevant output statistics. In order to improve upon these first-order approaches, Kriging and gradient enhanced Kriging models are created for the function space and serve as a basis for additional inexpensive Monte Carlo sampling. The probability distribution functions and statistics generated by these linear and Kriging based methods compare favorably with the results of nonlinear Monte Carlo sampling and can serve as a basis for further exploration of uncertainty quantification in hypersonic flows.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 18 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.