MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS

IF 1.5 4区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal for Uncertainty Quantification Pub Date : 2023-12-01 DOI:10.1615/int.j.uncertaintyquantification.2023048277

Ferdinand Uilhoorn

引用次数: 0

Abstract

In data assimilation, the description of the model error uncertainty is utmost important because incorrectly defined may lead to information loss about the real state of the system. In this work, we proposed a novel approach that finds the optimal distribution for describing the model error uncertainty within a particle filtering framework. The method was applied to nonlinear waves in compressible flows. We investigated the influence of observation noise statistics, resolution of the numerical model, smoothness of the solutions and sensor location. The results showed that in almost all situations the Pearson Type I is preferred, but with different curve-shape characteristics, namely, skewed, nearly symmetric, ∩-, ∪- and J-shaped. The distributions became in most cases ∪-shaped when the sensors were located nearby the discontinuities.

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利用皮尔逊系统进行模型误差估计，并应用于可压缩流中的非线性波

在数据同化过程中，对模型误差不确定性的描述至关重要，因为不正确的定义可能会导致系统真实状态信息的丢失。在这项工作中，我们提出了一种新方法，在粒子滤波框架内找到描述模型误差不确定性的最佳分布。该方法被应用于可压缩流中的非线性波。我们研究了观测噪声统计、数值模型分辨率、解的平滑度和传感器位置的影响。结果表明，几乎在所有情况下，Pearson I 型都是首选，但具有不同的曲线形状特征，即偏斜、近乎对称、∩形、∪形和 J 形。在大多数情况下，当传感器位于不连续点附近时，曲线分布呈"∪"形。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal for Uncertainty Quantification ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.60

自引率

5.90%

发文量

期刊介绍： The International Journal for Uncertainty Quantification disseminates information of permanent interest in the areas of analysis, modeling, design and control of complex systems in the presence of uncertainty. The journal seeks to emphasize methods that cross stochastic analysis, statistical modeling and scientific computing. Systems of interest are governed by differential equations possibly with multiscale features. Topics of particular interest include representation of uncertainty, propagation of uncertainty across scales, resolving the curse of dimensionality, long-time integration for stochastic PDEs, data-driven approaches for constructing stochastic models, validation, verification and uncertainty quantification for predictive computational science, and visualization of uncertainty in high-dimensional spaces. Bayesian computation and machine learning techniques are also of interest for example in the context of stochastic multiscale systems, for model selection/classification, and decision making. Reports addressing the dynamic coupling of modern experiments and modeling approaches towards predictive science are particularly encouraged. Applications of uncertainty quantification in all areas of physical and biological sciences are appropriate.