Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Uncertainty Quantification
Импакт фактор: 3.259 5-летний Импакт фактор: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099

Свободный доступ

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2016015984
pages 109-126

INCORPORATING PRIOR KNOWLEDGE FOR QUANTIFYING AND REDUCING MODEL-FORM UNCERTAINTY IN RANS SIMULATIONS

Jianxun Wang
Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia 24060, USA
Jin-Long Wu
Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia 24060, USA
Heng Xiao
Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia 24060, USA

Краткое описание

Simulations based on Reynolds-averaged Navier-Stokes (RANS) models have been used to support high-consequence decisions related to turbulent flows. Apart from the deterministic model predictions, the decision makers are often equally concerned about the prediction confidence. Among the uncertainties in RANS simulations, the model-form uncertainty is an important or even a dominant source. Therefore, quantifying and reducing the model-form uncertainties in RANS simulations are of critical importance to make risk-informed decisions. Researchers in statistics communities have made efforts on this issue by considering numerical models as black boxes. However, this physics-neutral approach is not a most efficient use of data, and is not practical for most engineering problems. Recently, we proposed an open-box, Bayesian framework for quantifying and reducing model-form uncertainties in RANS simulations based on observation data and physics-prior knowledge. It can incorporate the information from the vast body of existing empirical knowledge with mathematical rigor, which enables a more efficient usage of data. In this work, we examine the merits of incorporating various types of prior knowledge in the uncertainties quantification and reduction in RANS simulations. The result demonstrates that informative physics-based prior knowledge plays an important role in improving the performance of model-form uncertainty reduction, particularly when the observation data are limited. Moreover, it suggests that the proposed Bayesian framework is an effective way to incorporate empirical knowledge from various sources of turbulence modeling.


Articles with similar content:

BUILDING ENERGY MODELING
Annual Review of Heat Transfer, Vol.21, 2018, issue 1
Jelena Srebric, Mohammad Heidarinejad
A SEMI-ANALYTICAL MODEL OF BUBBLE GROWTH AND DETACHMENT DURING NUCLEATE BOILING
International Heat Transfer Conference 16, Vol.4, 2018, issue
S. P. Walker, K H Ardron, Giovanni Giustini
PARAMETER SENSITIVITY OF AN EDDY VISCOSITY MODEL: ANALYSIS, COMPUTATION AND ITS APPLICATION TO QUANTIFYING MODEL RELIABILITY
International Journal for Uncertainty Quantification, Vol.3, 2013, issue 5
Lisa Davis, Faranak Pahlevani
UNCERTAINTY QUANTIFICATION FOR INCIDENT HELIUM FLUX IN PLASMA-EXPOSED TUNGSTEN
International Journal for Uncertainty Quantification, Vol.8, 2018, issue 5
Habib N. Najm, Sophie Blondel, Brian D. Wirth, Ozgur Cekmer, David E. Bernholdt, Khachik Sargsyan
IMPROVED CONTROL OF MIMO PROCESSES USING GENETIC ALGORITHMS
Flexible Automation and Intelligent Manufacturing, 1997:
Proceedings of the Seventh International FAIM Conference, Vol.0, 1997, issue
C. S. Cox, LG. French, C. K. S. Ho, K. C. S. Ng