Quantification of the uncertainty within a SAS-SST simulation caused by the unknown high-wavenumber damping factor
Author(s)
Duan, Yu
Ahn, Ji Soo
Eaton, Matthew D
Bluck, Michael J
Type
Journal Article
Abstract
This paper aims to quantify the uncertainty in the SAS-SST simulation of a prism bluff-body flow due to varying
the higher-wavenumber damping factor (Cs). Instead of performing the uncertainty quantification on the CFD
simulation directly, a surrogate modelling approach is adopted. The mesh sensitivity is first studied and the
numerical error due to the mesh is approximated accordingly. The Gaussian processes/Kriging method is used to
generate surrogate models for quantities of interest (QoIs). The suitability of the surrogate models is assessed
using the leave-one-out cross-validation tests (LOO-CV). The stochastic tests are then performed using the crossvalidated surrogate models to quantify the uncertainty of QoIs by varying Cs. Four prior probability density
functions (such as U(0, 1), N
(
0.5, 0.12)
, Beta(2, 2) and Beta(5, 1.5)) of Cs are considered.
It is demonstrated in this study that the uncertainty of a predicted QoI due to varying Cs is regionally
dependent. The flow statistics in the near wake of the prism body are subject to larger variance due to the
uncertainty in Cs. The influence of Cs rapidly decays as the location moves downstream. The response of different
QoIs to the changing Cs varies greatly. Therefore, the calibration of Cs only using observations of one variable
may bias the results. Last but not least, it is important to consider different sources of uncertainties within the
numerical model when scrutinising a turbulence model, as ignoring the contributions to the total error may lead
to biased conclusions.
the higher-wavenumber damping factor (Cs). Instead of performing the uncertainty quantification on the CFD
simulation directly, a surrogate modelling approach is adopted. The mesh sensitivity is first studied and the
numerical error due to the mesh is approximated accordingly. The Gaussian processes/Kriging method is used to
generate surrogate models for quantities of interest (QoIs). The suitability of the surrogate models is assessed
using the leave-one-out cross-validation tests (LOO-CV). The stochastic tests are then performed using the crossvalidated surrogate models to quantify the uncertainty of QoIs by varying Cs. Four prior probability density
functions (such as U(0, 1), N
(
0.5, 0.12)
, Beta(2, 2) and Beta(5, 1.5)) of Cs are considered.
It is demonstrated in this study that the uncertainty of a predicted QoI due to varying Cs is regionally
dependent. The flow statistics in the near wake of the prism body are subject to larger variance due to the
uncertainty in Cs. The influence of Cs rapidly decays as the location moves downstream. The response of different
QoIs to the changing Cs varies greatly. Therefore, the calibration of Cs only using observations of one variable
may bias the results. Last but not least, it is important to consider different sources of uncertainties within the
numerical model when scrutinising a turbulence model, as ignoring the contributions to the total error may lead
to biased conclusions.
Date Issued
2021-09
Date Acceptance
2021-05-21
Citation
Nuclear Engineering and Design, 2021, 381, pp.1-12
ISSN
0029-5493
Publisher
Elsevier BV
Start Page
1
End Page
12
Journal / Book Title
Nuclear Engineering and Design
Volume
381
Copyright Statement
© 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Rolls-Royce
Identifier
https://www.sciencedirect.com/science/article/pii/S0029549321002594?via%3Dihub
Subjects
Energy
0915 Interdisciplinary Engineering
Publication Status
Published
Article Number
111307
Date Publish Online
2021-06-21