Estimation of spray flow characteristics using ensemble Kalman filter

Abstract

Spray flows are widely used in several industrial applications, such as combustion engines. Accurate measurement of spray flow characteristics requires sophisticated equipment and techniques. In recent years, the discrete droplet method (DDM), which analyses droplet scattering, has become a mature technique and has been applied to various analyses. We propose an estimation system based on particle image velocimetry (PIV) measurements and an ensemble Kalman filter, together with DDM, to efficiently investigate spray flow characteristics. The proposed method performs data assimilation on the velocity distribution in a two-dimensional cross-section obtained by PIV to estimate the characteristics of the spray flow in three dimensions. In this study, the system was constructed so that droplet particle is ensembled during data assimilation to estimate the droplet diameter distribution indirectly. The proposed method can be used to estimate the spray velocity and droplet size distribution. The numerical solution obtained using DDM was used as a criterion for assimilation and validated by conducting twin experiments. The results showed that, in terms of spray velocity, the estimation error for the velocity component parallel to the main flow was 2% and that for the velocity component perpendicular to the main flow was around 10%. Finally, the velocity and particle size distributions of the spray stream and the three-dimensional droplet distribution were estimated by assimilating the velocity distributions measured by PIV. This technique predicts the spray angle and droplet size distribution from the two-dimensional velocity field of the PIV only and is expected to contribute to the development of injectors and atomizers.