Augmentation of Cross-Sectional Spray Measurements with Discrete Droplet Model Using Ensemble Kalman Filter

IF 1.1 4区工程技术 Q4 MECHANICS

International Journal of Computational Fluid Dynamics Pub Date : 2022-02-07 DOI:10.1080/10618562.2022.2052281

Shun Takahashi, T. Misaka, Shotaro Nara, Naoki Sugiyama, Tetsuo Nohara, Yuiki Kuramoto, Y. Kawamoto, Akira Obara, Rina Osada, Asuka Kikuchi, M. Ochiai, Kazuo Osumi, N. Ishikawa

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引用次数: 2

Abstract

Spray flows containing droplets and particles are used in various industrial fields. In this study, we investigate an efficient and reliable way to predict the spray flow of droplets by combining the discrete droplet model (DDM) with ensemble data assimilation for application to such industrial problems. The aim is to augment cross-sectional measurements such as particle image velocimetry (PIV) with fast DDM simulations of droplets. In this paper, we focus on the numerical experiment of data assimilation, which is also known as twin experiments, and discuss how such cross-sectional measurements and DDM can be integrated by the ensemble Kalman filter. The results showed that the position and velocity of the droplet and the spray nozzle's state were estimated by assimilating the time-averaged velocity measurements on the cross-section using a carefully prepared ensemble of droplets. Furthermore, the droplet size distribution was estimated indirectly through DDM.

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用集合卡尔曼滤波增强离散液滴模型的截面喷雾测量

含有液滴和颗粒的喷雾流用于各种工业领域。在本研究中，我们将离散液滴模型(DDM)与集成数据同化相结合，探索一种有效可靠的方法来预测液滴的喷雾流动，并将其应用于此类工业问题。目的是增强截面测量，如粒子图像测速(PIV)与快速DDM模拟液滴。在本文中，我们着重于数据同化的数值实验，也被称为孪生实验，并讨论了如何将这种截面测量和DDM用集合卡尔曼滤波进行积分。结果表明，通过吸收精心制备的液滴集合在截面上的时间平均速度测量值，可以估计出液滴的位置和速度以及喷嘴的状态。此外，通过DDM间接估计了液滴的粒径分布。

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 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.