Covariance-oriented sample transformation: A new sampling method for reactor-physics uncertainty analysis

Sui, Zhuojie; Cao, Liangzhi; Wan, Chenghui; Zou, Xiaoyang

doi:10.1016/j.anucene.2019.07.001

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…erefore, it is an issue to notably reduce and determine the sample size on the basis of ensuring the consistent UQ results with those under infinite sample size and infinitesimal statistical fluctuations. In this context, Sui et al [83] have proposed a covariance-oriented sample transformation (COST) to generate multivariate normal distribution samples for uncertainty analysis. In this method, samples from the standard normal distribution are transformed linearly in which the mean and covariance of the transformed samples are ensured equal to that of the input parameter population, respectively.…”

Section: Initial Experimental Designmentioning

confidence: 99%

Basic Framework and Main Methods of Uncertainty Quantification

Zhang

Wang

2020

Mathematical Problems in Engineering

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Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry. This review firstly discusses the sources and the types of uncertainties and gives an overall discussion on the goal, practical significance, and basic framework of the research of UQ. Then, the core ideas and typical methods of several important UQ processes are introduced, including sensitivity analysis, uncertainty propagation, model calibration, Bayesian inference, experimental design, surrogate model, and model uncertainty analysis.

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Section: Initial Experimental Designmentioning

confidence: 99%

Basic Framework and Main Methods of Uncertainty Quantification

Zhang

Wang

2020

Mathematical Problems in Engineering

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“…In order to fully describe the probability distribution of the input parameters, the sample size required by the conventional sampling methods is very huge, resulting in corresponding computational challenge. The COST method has been proposed in our previous work to provide the converged UQ results with a minimal sample size [6].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis to C5g7-Td Benchmark Based on the Cost Method

et al. 2021

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A method of Covariance-Oriented Sample Transformation (COST) has been proposed in our previous work to provide the converged uncertainty analysis results with a minimal sample size. The transient calculation of nuclear reactor is a key part of the reactor-physics simulation, so the accuracy and confidence of the neutron kinetics results have attracted much attention. In this paper, the Uncertainty Quantification (UQ) function of the high fidelity neutronics code NECP-X has been developed based on our home-developed uncertainty analysis code UNICORN, building a platform for the UQ of the transient calculation. Furthermore, the well-known space-time heterogeneous neutron kinetics benchmark C5G7 and its uncertainty propagation from the nuclear data to the interested key parameters of the core have been investigated. To address the problem of “the curse of dimensionality” caused by the large number of input parameters, the COST method has been applied to generate multivariate normal-distribution samples in uncertainty analysis. As a result, the law of the assembly/pin normalized power and their uncertainty with respect to time after introducing an instantaneous perturbation has been obtained. From the numerical results, it can be observed that the maximum relative uncertainties for the assembly normalized power can up to be about 1.65% and the value for the pin-wise power distributions can be about 2.71%.

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