Uncertainty Analysis For Fluid Mechanics With Applications
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Uncertainty Analysis for Fluid Mechanics with Applications
This paper reviews uncertainty analysis methods and their application to fundamental problems in fluid dynamics. Probabilistic (Monte-Carlo, Moment methods, Polynomial Chaos) and non-probabilistic methods (Interval Analysis Propagation of error using sensitivity derivatives) are described and implemented. Results are presented for a model convection equation with a source term, a model non-linear convection-diffusion equation; supersonic flow over wedges, expansion corners, and an airfoil; and two-dimensional laminar boundary layer flow.
Uncertainty Quantification in Computational Fluid Dynamics
Author: Hester Bijl
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-09-20
Fluid flows are characterized by uncertain inputs such as random initial data, material and flux coefficients, and boundary conditions. The current volume addresses the pertinent issue of efficiently computing the flow uncertainty, given this initial randomness. It collects seven original review articles that cover improved versions of the Monte Carlo method (the so-called multi-level Monte Carlo method (MLMC)), moment-based stochastic Galerkin methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type in both physical and stochastic space. The methods are also complemented by concrete applications such as flows around aerofoils and rockets, problems of aeroelasticity (fluid-structure interactions), and shallow water flows for propagating water waves. The wealth of numerical examples provide evidence on the suitability of each proposed method as well as comparisons of different approaches.
Pade-legendre Method for Uncertainty Quantification with Fluid Dynamics Applications
Abstract: The Pade-Legendre (PL) method, a novel approach for uncertainty quantification is introduced. The proposed method uses a rational function expansion and is designed to effectively characterize uncertainties in strongly non-linear or discontinuous systems. The discontinuities can be either in the underlying functions (inherent discontinuities) or from lack of sufficient data resolution (multi-scale discontinuities). In the former case, PL method can produce an accurate response surface without spurious oscillations and does not require prior knowledge of the discontinuities. For the latter type of discontinuities, the PL method can help reduce the number of deterministic simulations required to accurately represent the response surface. If sufficient data resolution is achieved, the PL method degenerates to standard polynomial reconstruction. The present approach is illustrated in a number of applications as an uncertainty propagation technique. Moreover, the method is applied to an inference problem in which a sharp discontinuity in the system input is present. The PL method shows a considerable improvement over the traditional approach when discontinuities are present. In addition, an ongoing effort called the UQ Experiment in which we used the PL method to help design the experimental setup is discussed.