Uncertainty quantification for microscale CFD simulations based on input from mesoscale codes

Journal of Wind Engineering and Industrial Aerodynamics ◽

10.1016/j.jweia.2018.03.011 ◽

2018 ◽

Vol 176 ◽

pp. 87-97 ◽

Author(s):

C. García-Sánchez ◽

C. Gorlé

Keyword(s):

Uncertainty Quantification ◽

Cfd Simulations

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Surrogate model based uncertainty quantification of CFD simulations of the viscous flow around a ship advancing in shallow water

Ocean Engineering ◽

10.1016/j.oceaneng.2021.109206 ◽

2021 ◽

Vol 234 ◽

pp. 109206

Author(s):

Li Xia ◽

Zao-Jian Zou ◽

Zi-Hao Wang ◽

Lu Zou ◽

Hang Gao

Keyword(s):

Viscous Flow ◽

Uncertainty Quantification ◽

Shallow Water ◽

Surrogate Model ◽

Cfd Simulations ◽

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Non-intrusive semi-analytical uncertainty quantification using Bayesian quadrature with application to CFD simulations

International Journal of Heat and Fluid Flow ◽

10.1016/j.ijheatfluidflow.2021.108917 ◽

2022 ◽

Vol 93 ◽

pp. 108917

Author(s):

Yu Duan ◽

Miriam North Ridao ◽

Matthew Eaton ◽

Michael Bluck

Keyword(s):

Uncertainty Quantification ◽

Cfd Simulations ◽

Analytical Uncertainty

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Uncertainty Quantification in CFD Simulations: A Stochastic Spectral Approach

Computational Fluid Dynamics 2002 ◽

10.1007/978-3-642-59334-5_5 ◽

2003 ◽

pp. 65-70 ◽

Author(s):

Lionel Mathelin ◽

M. Yousuff Hussaini

Keyword(s):

Uncertainty Quantification ◽

Spectral Approach ◽

Cfd Simulations

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A hybrid anchored-ANOVA – POD/Kriging method for uncertainty quantification in unsteady high-fidelity CFD simulations

Journal of Computational Physics ◽

10.1016/j.jcp.2016.07.036 ◽

2016 ◽

Vol 324 ◽

pp. 137-173 ◽

Author(s):

Luca Margheri ◽

Pierre Sagaut

Keyword(s):

Uncertainty Quantification ◽

High Fidelity ◽

Kriging Method ◽

Cfd Simulations

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Stochastic Approaches to Uncertainty Quantification in CFD Simulations

Numerical Algorithms ◽

10.1007/s11075-004-2866-z ◽

2005 ◽

Vol 38 (1) ◽

pp. 209-236 ◽

Author(s):

Lionel Mathelin ◽

M. Yousuff Hussaini ◽

Thomas A. Zang

Keyword(s):

Uncertainty Quantification ◽

Cfd Simulations

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UNCERTAINTY QUANTIFICATION AND MODELLING OF CFD SIMULATIONS OF A SWIRLING TURBULENT JET CREATED BY A ROTATING PIPE FOR APPLICATION TO HEAT TRANSFER FROM A HEATED SOLID FLAT PLATE

Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2015) ◽

10.7712/120215.4267.652 ◽

2015 ◽

Author(s):

Francisco-Javier Granados-Ortiz ◽

Joaquin Ortega-Casanova ◽

Choi-Hong Lai

Keyword(s):

Heat Transfer ◽

Uncertainty Quantification ◽

Turbulent Jet ◽

Cfd Simulations

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Stochastic approaches to uncertainty quantification in CFD simulations

Numerical Algorithms ◽

10.1007/bf02810624 ◽

2005 ◽

Vol 38 (1-3) ◽

pp. 209-236 ◽

Author(s):

Lionel Mathelin ◽

M. Yousuff Hussaini ◽

Thomas A. Zang

Keyword(s):

Uncertainty Quantification ◽

Cfd Simulations

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Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.107636 ◽

2021 ◽

pp. 107636

Author(s):

Yang Liu ◽

Dewei Wang ◽

Xiaodong Sun ◽

Yang Liu ◽

Nam Dinh ◽

...

Keyword(s):

Machine Learning ◽

High Resolution ◽

Uncertainty Quantification ◽

Bayesian Approach ◽

Bubbly Flows ◽

Cfd Simulations ◽

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An Uncertainty Quantification Analysis in a Simplified Problem of Urban Pollutant Dispersion by Means of ANOVA-POD/Kriging-Based Response Surfaces

Volume 1D, Symposia: Transport Phenomena in Mixing; Turbulent Flows; Urban Fluid Mechanics; Fluid Dynamic Behavior of Complex Particles; Analysis of Elementary Processes in Dispersed Multiphase Flows; Multiphase Flow With Heat/Mass Transfer in Process Technology; Fluid Mechanics of Aircraft and Rocket Emissions and Their Environmental Impacts; High Performance CFD Computation; Performance of Multiphase Flow Systems; Wind Energy; Uncertainty Quantification in Flow Measurements and Simulations ◽

10.1115/fedsm2014-21556 ◽

2014 ◽

Author(s):

Luca Margheri ◽

Pierre Sagaut

Keyword(s):

Uncertainty Quantification ◽

Vortex Dynamics ◽

High Sensitivity ◽

Pollutant Dispersion ◽

Response Surfaces ◽

Cfd Simulations ◽

Fine Grid ◽

Quantification Analysis ◽

Computational Resources

In the last decade there has been a growing interest in urban flow CFD simulations. As RANS approaches demonstrated to be not enough accurate to predict urban flows, people focus more and more on LES simulations. Though better results could be obtained with fine grid LES, the complexity of the urban physics seems to vanish the increasing computational resources. A different approach is herein considered, proposing a first uncertainty quantification (UQ) analysis on a single building pollutant dispersion case. A hybrid method merging the anchored-ANOVA and the POD/Kriging-based response surface is proposed to reduce the costs of the UQ analysis. Moreover, simulations are performed by the Lattice Boltzman (LBM) code PowerFLOW. Sensitivity results are presented showing the importance of vortex dynamics and the high sensitivity to the wind angle.

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Parameter Estimation and Uncertainty Quantification in Water Resources Modeling

10.3389/978-2-88963-674-7 ◽

2020 ◽

Keyword(s):

Parameter Estimation ◽

Water Resources ◽

Uncertainty Quantification

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