The development of image processing technique to study the interfacial behavior of air-water slug two-phase flow in horizontal pipes

Abstract

We present a novel motion estimation technique for image-based river velocimetry. It is based on the so-called optical flow, which is a well developed method for rigid motion estimation in image sequences, devised in computer vision community. Contrary to PIV (Particle Image Velocimetry) techniques, optical flow formulation is flexible enough to incorporate physics equations that govern the observed quantity motion. Over the past years, it has been adopted by experimental fluid dynamics community where many new models were introduced to better represent different fluids motions, (see (Heitz et al., 2010) for a review). Our optical flow is based on the scalar transport equation and is augmented with a weighted diffusion term to compensate for small scale (non-captured) contributions. Additionally, since there is no ground truth data for such type of image sequences, we present a new evaluation method to assess the results. It is based on trajectory reconstruction of few Lagrangian particles of interest and a direct comparison against their manually-reconstructed trajectories. The new motion estimation technique outperformed traditional optical flow and PIV-based methods.

Fish-eye camera and image processing for commanding a solar tracker

“…(6). Notice that segmentation and 256 scale-of-grays are used [24, 25]. That is, in this paper 256 grays are used, where the black tone (absence of all color) is associated to P = 0, and the white tone (presence of full color) is associated by P = 256.…”

Overview of Void Fraction Measurement Techniques, Databases and Correlations for Two-Phase Flow in Small Diameter Channels

“…Differently from other methods, the use of photographed images gives direct information about the phase flow, such as bubble size, bubble shape and flow pattern. With these flow properties it is possible to determine additional information such as the void fraction or the phase's distribution [53,54]. For slug flow regime, the presence of disperse bubble is a challenge on the implementation…”

Quantifying interfacial parameters of upward and downward annular flow condensation from high-speed visualization

“…Several studies dedicated to developing quantitative flow visualization techniques for gas-liquid flows have been published recently. The majority of them were focused on accurately determining film thickness, phase fractions and/or intermittent flow parameters (e.g., slug velocity, frequency and size) in adiabatic (air-water) flows [4,5]. Comparisons against more established experimental techniques [6,7] and numerical simulations have been carried out [8].…”