Stochastic analysis of transport in tubes with rough walls
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9100 Gilman Drive, La Jolla, CA 92093 (United States) and Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States)
- Department of Mathematics, Purdue University, IN 47907 (United States)
Flow and transport in tubes with rough surfaces play an important role in a variety of applications. Often the topology of such surfaces cannot be accurately described in all of its relevant details due to either insufficient data or measurement errors or both. In such cases, this topological uncertainty can be efficiently handled by treating rough boundaries as random fields, so that an underlying physical phenomenon is described by deterministic or stochastic differential equations in random domains. To deal with this class of problems, we use a computational framework, which is based on stochastic mappings to transform the original deterministic/stochastic problem in a random domain into a stochastic problem in a deterministic domain. The latter problem has been studied more extensively and existing analytical/numerical techniques can be readily applied. In this paper, we employ both a generalized polynomial chaos and Monte Carlo simulations to solve the transformed stochastic problem. We use our approach to describe transport of a passive scalar in Stokes' flow and to quantify the corresponding predictive uncertainty.
- OSTI ID:
- 20840345
- Journal Information:
- Journal of Computational Physics, Vol. 217, Issue 1; Other Information: DOI: 10.1016/j.jcp.2006.02.029; PII: S0021-9991(06)00121-5; Copyright (c) 2006 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9991
- Country of Publication:
- United States
- Language:
- English
Similar Records
A stochastic particle-mesh scheme for uncertainty propagation in vortical flows
Predictability and reduced order modeling in stochastic reaction networks.