物理模型误差对中子应用状态估计的影响

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Esaim-Probability and Statistics Pub Date : 2023-01-01 DOI:10.1051/proc/202373158

Yonah Conjungo Taumhas, David Labeurthre, Francois Madiot, Olga Mula, Tommaso Taddei

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引用次数: 0

摘要

本文考虑了基于有限中子通量观测的核电厂核电场状态估计的反问题。为此，我们使用了参数化背景数据弱方法。该方法将观测结果与中子通量的参数化偏微分方程模型相结合。因为，一般来说，即使是最复杂的模型也不能完美地捕捉现实，因此不可避免地会出现模型错误。我们研究了当我们使用扩散模型来描述中子通量时，模型误差对功率重建的影响，并假设真实的物理是由中子输运模型控制的。

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Impact of physical model error on state estimation for neutronics applications

In this paper, we consider the inverse problem of state estimation of nuclear power fields in a power plant from a limited number of observations of the neutron flux. For this, we use the Parametrized Background Data Weak approach. The method combines the observations with a parametrized PDE model for the behavior of the neutron flux. Since, in general, even the most sophisticated models cannot perfectly capture reality, an inevitable model error is made. We investigate the impact of the model error in the power reconstruction when we use a diffusion model for the neutron flux, and assume that the true physics are governed by a neutron transport model.

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来源期刊

Esaim-Probability and Statistics STATISTICS & PROBABILITY-

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The journal publishes original research and survey papers in the area of Probability and Statistics. It covers theoretical and practical aspects, in any field of these domains. Of particular interest are methodological developments with application in other scientific areas, for example Biology and Genetics, Information Theory, Finance, Bioinformatics, Random structures and Random graphs, Econometrics, Physics. Long papers are very welcome. Indeed, we intend to develop the journal in the direction of applications and to open it to various fields where random mathematical modelling is important. In particular we will call (survey) papers in these areas, in order to make the random community aware of important problems of both theoretical and practical interest. We all know that many recent fascinating developments in Probability and Statistics are coming from "the outside" and we think that ESAIM: P&S should be a good entry point for such exchanges. Of course this does not mean that the journal will be only devoted to practical aspects.