基于图像分析测量数据的扩展卡尔曼滤波FEM的浅水流高程数据同化

IF 1.1 4区工程技术 Q4 MECHANICS

International Journal of Computational Fluid Dynamics Pub Date : 2022-02-07 DOI:10.1080/10618562.2022.2027391

T. Kurahashi, Kohei Ikarashi, Toshiaki Kenchi, Toshihiko Eto

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引用次数: 0

摘要

本文提出了一种基于扩展卡尔曼滤波有限元法(extended Kalman filter FEM)的考虑岸线运动的浅水流场数据同化分析方法。我们知道，如果采用卡尔曼滤波与有限元法相结合的方法，可以得到比普通有限元法更接近实际观测值的解。以溃坝问题为研究对象，进行了数值实验。本文通过图像分析得到扩展卡尔曼滤波FEM中使用的观测值，并通过改变浅水流的控制方程来研究其估计精度。

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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Data Assimilation of Water Elevation in Shallow Water Flow Based on the Extended Kalman Filter FEM Using Measurement Data from Image Analysis

In this paper, we present a data assimilation analysis in a shallow water flow field considering shoreline movement, based on the extended Kalman filter finite element method (extended Kalman filter FEM). It is known that if the combined method of the Kalman filter and the finite element method(FEM) is employed, a solution can be obtained that is closer to the practical observed value than that based on normal FEM. A dam-break problem is targeted in numerical experiments. In this study, the observed values used in the extended Kalman filter FEM are obtained by image analysis, and we investigate the estimation accuracy by changing the governing equation for shallow water flow.

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来源期刊

International Journal of Computational Fluid Dynamics 物理-力学

CiteScore

2.70

自引率

7.70%

发文量

审稿时长

3 months

期刊介绍： The International Journal of Computational Fluid Dynamics publishes innovative CFD research, both fundamental and applied, with applications in a wide variety of fields. The Journal emphasizes accurate predictive tools for 3D flow analysis and design, and those promoting a deeper understanding of the physics of 3D fluid motion. Relevant and innovative practical and industrial 3D applications, as well as those of an interdisciplinary nature, are encouraged.