Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments

Liu, Yang; Sun, Xiaodong; Dinh, Truc-Nam

doi:10.1016/j.nucengdes.2019.110200

Cited by 30 publications

(8 citation statements)

References 98 publications

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“…• Bayesian Inference: Within the Bayesian inference framework [30] the posterior distribution. These properties can be directly sampled from the distribution [31] and propagated through the simulation. To support the development of ANNs, Bayesian inference is used by assigning prior uncertainty on network weights; thus, it provides uncertainty about the functional mean through posterior predictive distributions [32].…”

Section: Uncertainty Quantification Methodsmentioning

confidence: 99%

Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review

Lin

Bao

Dinh

2021

Annals of Nuclear Energy

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Section: Uncertainty Quantification Methodsmentioning

confidence: 99%

Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review

Lin

Bao

Dinh

2021

Annals of Nuclear Energy

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“…Automatic machine learning algorithms [ 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ] have been widely used in analysis and optimization in physical and chemical engineering. Liu et al [ 51 , 52 , 54 ] used Bayesian optimization [ 51 , 52 ] and deep neural network algorithms [ 54 ] in the modelling and analysis of multiphase flow and boiling heat transfer, respectively. They effectively reduced the uncertainty of empirical correlations in complex processes and verified the effectiveness of the algorithms through experiments.…”

Section: Optimization Methodsmentioning

confidence: 99%

Multi-Objective Optimization of Braun-Type Exothermic Reactor for Ammonia Synthesis

Xie

Xia

Wang

2021

Entropy

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The exothermic reactor for ammonia synthesis is a primary device determining the performance of the energy storage system. The Braun-type ammonia synthesis reactor is used as the exothermic reactor to improve the heat release rate. Due to the entirely different usage scenarios and design objectives, its parameters need to be redesigned and optimized. Based on finite-time thermodynamics, a one-dimensional model is established to analyze the effects of inlet gas molar flow rate, hydrogen–nitrogen ratio, reactor length and inlet temperature on the total entropy generation rate and the total exothermic rate of the reactor. It’s found that the total exothermic rate mainly depends on the inlet molar flow rate. Furthermore, considering the minimum total entropy generation rate and maximum total exothermic rate, the NSGA-II algorithm is applied to optimize seven reactor parameters including the inlet molar flow rate, lengths and temperatures of the three reactors. Lastly, the optimized reactor is obtained from the Pareto front using three fuzzy decision methods and deviation index. Compared with the reference reactor, the total exothermic rate of the optimized reactor is improved by 12.6% while the total entropy generation rate is reduced by 3.4%. The results in this paper can provide some guidance for the optimal design and application of exothermic reactors in practical engineering.

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“…As risk-informed safety analysis methodology starts to be adopted by nuclear regulatory authorities, uncertainty quantification (UQ) and risk analysis become increasingly important in the nuclear engineering community [23,24,25,26]. In this sense, the modeler should not only provide the prediction of a certain problem, but also provide the uncertainty associated with that prediction.…”

Section: Uncertainty Quantification and Model Implementationmentioning

confidence: 99%

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

Liu¹,

Hu²,

Dai³

et al. 2021

Self Cite

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is a U.S. Department of Energy laboratory managed by UChicago Argonne, LLC under contract DE-AC02-06CH11357. The Laboratory's main facility is outside Chicago, at 9700 South Cass Avenue, Argonne, Illinois 60439. For information about Argonne and its pioneering science and technology programs, see www.anl.gov. DOCUMENT AVAILABLITYOnline Access: U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free at OSTI.GOV (http://www.osti.gov/), a service of the US Dept. of Energy's Office of Scientific and Technical Information.

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Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments

Cited by 30 publications

References 98 publications

Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review

Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review

Multi-Objective Optimization of Braun-Type Exothermic Reactor for Ammonia Synthesis

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

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