Development and assessment of a particle tracking velocimetry (PTV) measurement technique for the experimental investigation of oil drops behaviour in dispersed oil–water two-phase flow within a centrifugal pump impeller

Abstract

by Rafael F.L. de Cerqueira, Rodolfo Marcilli Perissinotto, William Monte Verde, Jorge Luiz Biazussi, Marcelo Souza de Castro, Antonio Carlos Bannwart, published at International Journal of Multiphase Flow, Vol. 159,  February 2023.

Abstract

The objective of the current work is to present the development of a Particle Tracking Velocimetry (PTV) algorithm for the analysis of oil drops behaviour in two-phase oil–water dispersions within a centrifugal pump impeller. The drop tracking was realized through high-speed camera images in a transparent pump prototype, which enabled the visualization of oil drops dispersed in water in all the impeller channels. The PTV algorithm is based on deep-learning techniques for image processing. The drops are detected by a combined U-Net and Convolutional Neural Network (CNN) method, with the former generating a binary image and the latter detecting valid oil drop contours. After detection, the Labelled Object Velocimetry (LOV) is adopted to calculate the instantaneous oil drop velocity. A synthetic image generator based on a Generative Adversarial Network (GAN) is then developed to assess the results from the U-Net, CNN, and LOV models. Additional validation studies are performed using the results from Perissinotto et al. (2019a). The results reveal that the presented deep-learning PTV algorithm is robust and provides consistent and reliable data for the dispersed oil phase in two-phase oil–water flows.

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