Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments-Reference-Cited by-同舟云学术

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

Published:2021-08 Issue: Volume:212 Page:107636
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Liu Yang,Wang Dewei,Sun Xiaodong,Liu Yang,Dinh Nam,Hu Rui

Funder

Office of Nuclear Energy

Argonne National Laboratory

U.S. Department of Energy

Nuclear Energy University Program

Laboratory Directed Research and Development

Office of Science

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

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4. Enhancement of risk informed validation framework for external hazard scenario;Bodda;Reliab. Eng. Syst. Saf.,2020

5. Interface capturing simulations of droplet interaction with spacer grids under DFFB conditions;Saini;Nucl. Eng. Des.,2020

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