Augmentation of Cross-Sectional Spray Measurements with Discrete Droplet Model Using Ensemble Kalman Filter. Issue 2 (7th February 2022)

• Record Type: Journal Article
• Authors: Takahashi, Shun; Misaka, Takashi; Nara, Shotaro; Sugiyama, Naoki; Nohara, Tetsuo; Kuramoto, Yuiki; Kawamoto, Yuki; Obara, Akira; Osada, Rina; Kikuchi, Asuka; Ochiai, Masayuki; Osumi, Kazuo; Ishikawa, Naoya
• Is Part Of: International journal of computational fluid dynamics. Volume 36:Issue 2(2022)
• Page Start: 125
• Page End: 137
• Publication Date: 2022-02-07
• Subjects: Injector flow -- discrete droplet model -- ensemble Kalman filter -- PIV measurement -- data assimilation; Fluid dynamics -- Data processing -- Periodicals; 532.05
• DOI: 10.1080/10618562.2022.2052281
• Languages: English
• ISSNs: 1061-8562

Abstract

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.