Variance reduction for global response problem based on forward Monte Carlo calculation

Tao, Shi; Ma, Ji; Huang, Hongwen; Qiu, Youheng; Zeng, Herong; Li, Zhenghong; Qian, Dazhi

doi:10.1016/j.nucengdes.2017.07.006

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…For simplicity, the comparison between frequencies f 1 and f 4 , and between frequencies f 6 and f 7 are in Figs. [28][29][30] in Section A.2 show the scatter plot profile comparison for all frequencies.…”

Section: Discussionmentioning

confidence: 99%

“…The uncertainty in the model parameters h can be accounted for by using a probability density function. By doing so, the modeller can construct a stochastic model (or a class of models [14])) to predict probabilistically the possible values of the system output D, and therefore its statistics, given the vector of uncertain model parameters h. These are the so-called forward problems which can be solved by means of analytical approaches [25][26][27] or in general by Monte Carlo simulation approaches [28][29][30]. In the latter approach, the statistics of D are obtained by first generating n realisations of the h k (for k ¼ 1; .…”

Section: Introductionmentioning

confidence: 99%

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Sampling methods for solving Bayesian model updating problems: A tutorial

Lye

Cicirello

Patelli

2021

Mechanical Systems and Signal Processing

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This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the context of Bayesian model updating for engineering applications. Markov Chain Monte Carlo, Transitional Markov Chain Monte Carlo, and Sequential Monte Carlo methods are introduced, applied to different case studies and finally their performance is compared. For each of these methods, numerical implementations and their settings are provided.Three case studies with increased complexity and challenges are presented showing the advantages and limitations of each of the sampling techniques under review. The first case study presents the parameter identification for a spring-mass system under a static load. The second case study presents a 2-dimensional bi-modal posterior distribution and the aim is to observe the performance of each of these sampling techniques in sampling from such distribution. Finally, the last case study presents the stochastic identification of the model parameters of a complex and non-linear numerical model based on experimental data.The case studies presented in this paper consider the recorded data set as a single piece of information which is used to make inferences and estimations on time-invariant model parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sampling methods for solving Bayesian model updating problems: A tutorial

Lye

Cicirello

Patelli

2021

Mechanical Systems and Signal Processing

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“…The information of simulated particle based on the grids obtained by MC simulation, such as particle flux, number and weight, is used to generate the global weight window parameters, which are in turn used to in the next iteration of MC simulation. The method has been widely used for global radiation environment calculation of complex models such as reactor devices [11][12][13], tokamak devices [7,14], nuclear power plants [15,16]and thick shields [17,18].…”

Section: Introductionmentioning

confidence: 99%

Global variance reduction method based on volume and non-counting area corrections for global Monte Carlo simulations

Liu,

Zuo,

Niu

et al. 2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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A Global Variance Reduction (GVR) method based on volume and non-counting area corrections is proposed to improve the efficiency of the Monte Carlo simulation of the radiation field in the large space. The volume correction factor is introduced to modify the global weight window parameters to solve the over-splitting problem caused by the volume difference of counting grids. The GVR method based on volume correction yields a global figure of merit G FOM about 39 times larger than the direct simulation, and the standard relative error  is reduced by about two orders of magnitude.For the problem of the excessive computational resources occupied by the non-counting area, the non-counting area correction method is proposed, which results in a further 40% improvement of the G FOM factor. The average error and maximum error of the radiation field in the large space obtained by three iterations of simulations using the corrected GVR method are 1.2% and 4.2%, respectively. And the G FOM factor is improved by about 561 times compared with the direct simulation. The GVR method based on volume and non-counting area corrections can effectively guide the simulated particle transport, which improves the accuracy and efficiency of the global Monte Carlo simulation of the radiation field.

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