We address the problem of propagating input uncertainties through a computational fluid dynamics model. Methods such as Monte Carlo simulation can require many thousands (or more) of computational fluid dynamics solves, rendering them prohibitively expensive for practical applications. This expense can be overcome with reduced-order models that preserve the essential flow dynamics. The specific...

Because the number of state variables in a reduced-order observer is less than the order n of S by the number m of (independent) observations, the reduced-order observer is parsimonious, often a desirable engineering quality. But, in addition, a reduced-order observer may have better properties than a full-order observer, especially with regard to robustness of a control system which uses an ob...

In this paper, we present first, second, and third order implicit finite-volume solvers for advectiondiffusion problems based on the first-order hyperbolic system method. In particular, we demonstrate that the construction of an uniformly accurate third-order advection-diffusion scheme is made trivial by the hyperbolic method while a naive construction of adding a third-order diffusion scheme t...