Abstract
We present the formulation and implementation of a stochas-
tic Computational Fluid Dynamics (CFD) solver based on the widely
used finite volume library - OpenFOAM. The solver employs General-
ized Polynomial Chaos (gPC) expansion to (a) quantify the uncertainties
associated with the fluid flow simulations, and (b) study the non-linear
propagation of these uncertainties. The aim is to accurately estimate the
uncertainty in the result of a CFD simulation at a lower computational
cost than the standard Monte Carlo (MC) method. The gPC approach
is based on the spectral decomposition of the random variables in terms
of basis polynomials containing randomness and the unknown determin-
istic expansion coefficients. As opposed to the mostly used non-intrusive
approach, in this work, we use the intrusive variant of the gPC method in
the sense that the deterministic equations are modified to directly solve
for the (coupled) expansion coefficients. To this end, we have tested the
intrusive gPC implementation for both the laminar and the turbulent
flow problems in CFD. The results are in accordance with the analytical
and the non-intrusive approaches. The stochastic solver thus developed,
can serve as an alternative to perform uncertainty quantification, espe-
cially when the non-intrusive methods are significantly expensive, which
is mostly true for a lot of stochastic CFD problems.
tic Computational Fluid Dynamics (CFD) solver based on the widely
used finite volume library - OpenFOAM. The solver employs General-
ized Polynomial Chaos (gPC) expansion to (a) quantify the uncertainties
associated with the fluid flow simulations, and (b) study the non-linear
propagation of these uncertainties. The aim is to accurately estimate the
uncertainty in the result of a CFD simulation at a lower computational
cost than the standard Monte Carlo (MC) method. The gPC approach
is based on the spectral decomposition of the random variables in terms
of basis polynomials containing randomness and the unknown determin-
istic expansion coefficients. As opposed to the mostly used non-intrusive
approach, in this work, we use the intrusive variant of the gPC method in
the sense that the deterministic equations are modified to directly solve
for the (coupled) expansion coefficients. To this end, we have tested the
intrusive gPC implementation for both the laminar and the turbulent
flow problems in CFD. The results are in accordance with the analytical
and the non-intrusive approaches. The stochastic solver thus developed,
can serve as an alternative to perform uncertainty quantification, espe-
cially when the non-intrusive methods are significantly expensive, which
is mostly true for a lot of stochastic CFD problems.
| Original language | English |
|---|---|
| Title of host publication | Computational Science – ICCS 2020 |
| Subtitle of host publication | 20th International Conference Amsterdam, The Netherlands, June 3–5, 2020 Proceedings, Part VII |
| Editors | Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, João Teixeira |
| Place of Publication | Cham |
| Publisher | Springer |
| Pages | 677-691 |
| ISBN (Electronic) | 978-3-030-50436-6 |
| ISBN (Print) | 978-3-030-50435-9 |
| DOIs | |
| Publication status | Published - 15-Jun-2020 |
| Event | 20th International Conference Computational Science : ICCS 2020 - Amsterdam, Netherlands Duration: 3-Jun-2020 → 5-Jun-2020 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 12143 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 20th International Conference Computational Science |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 03/06/2020 → 05/06/2020 |