Comparison of local and global sensitivity analysis methods and application to thermal hydraulic phenomena

Liu, Dong; Jiang, Penghao; Chen, Hu; Yan, Ting

doi:10.1016/j.pnucene.2023.104612

Cited by 19 publications

(3 citation statements)

References 28 publications

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“…In the field of nuclear TH, a variety of SA techniques have been effectively employed. These include Subset Simulation, Line Sampling [44], Sobol indices [45,46], Pearson correlation coefficient, elementary effects method [47], adjoint method [48] etc. 3.…”

Section: Bayesian Iuq Framework Overviewmentioning

confidence: 99%

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Wang,

Wu,

Xie

et al. 2023

Energies

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Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach leverages a hierarchical model to encapsulate group-level behaviors inherent to the PMPs, thereby mitigating existing challenges posed by the high variability of PMPs under diverse experimental conditions and the potential overfitting issues due to unknown model discrepancies or outliers. To accommodate computational scalability and efficiency, we utilize VI to enable the framework to be used in applications with a large number of variables or datasets. The efficacy of the proposed method is evaluated against a previous study where a No-U-Turn-Sampler was used in a Bayesian hierarchical model. We illustrate the performance comparisons of the proposed framework through a synthetic data example and an applied case in nuclear TH. Our findings reveal that the presented approach not only delivers accurate and efficient IUQ without the need for manual tuning, but also offers a promising way for scaling to larger, more complex nuclear TH experimental datasets.

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Section: Bayesian Iuq Framework Overviewmentioning

confidence: 99%

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Wang,

Wu,

Xie

et al. 2023

Energies

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show abstract

“…LSA is a method that examines the impact of a single input parameter on the model output while keeping other input parameters fixed. 34 In short, it calculates the partial derivative of the output response with respect to a specific input variable. GSA method allows for quantifying the influence of the inputs on the output response considering the entire range of input random variables.…”

Section: Introductionmentioning

confidence: 99%

“…According to its functionality, SA can be divided into local sensitivity analysis (LSA) and GSA. LSA is a method that examines the impact of a single input parameter on the model output while keeping other input parameters fixed 34 . In short, it calculates the partial derivative of the output response with respect to a specific input variable.…”

Section: Introductionmentioning

confidence: 99%

Fatigue reliability and sensitivity analysis of aero‐disk considering correlation

Di,

Li,

Nan

et al. 2024

Quality & Reliability Eng

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Aero‐disk is a key component of aero‐engine. Due to its complex working conditions, aero‐disk is prone to structural failures. Therefore, it is essential to analyze the reliability and importance input variables of aero‐disk. In this paper, a framework of aero‐disk reliability analysis and global sensitivity analysis (GSA) was established. First, the hazardous regions of aero‐disk were determined by finite element analysis, and the limit state function of aero‐disk was defined by the life interference model. Then D‐Vine model was utilized to establish the correlation model for aero‐disk hazardous regions to evaluate the reliability of aero‐disk. In addition, two GSA methods based on Copula and space partition were proposed to identify important input random variables considering underlying correlation, and the accuracy of the proposed method was verified by two examples. Finally, the proposed method was applied to the GSA of aero‐disk. The results show that the established framework fills in the gap of uncertainty analysis of aero‐disk, which can be extended to other engineering fields. The proposed GSA methods have both high efficiency and accuracy and can realize multi‐dimensional GSA when the correlation of input variables is considered.

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Application of MGGP in Predicting Bearing Capacity of a Strip Footing Resting on the Crest of a Marginal Soil Hillslope

Acharyya,

Dey

2024

KSCE J Civ Eng

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Comparison of local and global sensitivity analysis methods and application to thermal hydraulic phenomena

Cited by 19 publications

References 28 publications

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Fatigue reliability and sensitivity analysis of aero‐disk considering correlation

Application of MGGP in Predicting Bearing Capacity of a Strip Footing Resting on the Crest of a Marginal Soil Hillslope

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