Abstract
The deformation of air bubbles in a liquid flow field is of relevant interest in phenomena such as cavitation, air entrainment, and foaming. In complex situations, this problem cannot be addressed theoretically, while the accuracy of an approach based on Computational Fluid Dynamics (CFD) is often unsatisfactory. In this study, a novel approach to the problem is proposed, based on the combined use of a shadowgraph technique, to obtain experimental data, and some machine learning algorithms to build prediction models. Three models were developed to predict the equivalent diameter and aspect ratio of air bubbles moving near a plunging jet. The models were different in terms of their input variables. Five variants of each model were built, changing the implemented machine learning algorithm: Additive Regression of Decision Stump, Bagging, K-Star, Random Forest and Support Vector Regression. In relation to the prediction of the equivalent diameter, two models provided satisfactory predictions, assessed on the basis of four different evaluation metrics. The third model was slightly less accurate in all its variants. Regarding the forecast of the bubble’s aspect ratio, the difference in the input variables of the prediction models shows a greater influence on the accuracy of the results. However, the proposed approach proves to be promising to address complex problems in the study of multi-phase flows.
Highlights
The study of the dynamics of air bubbles in a liquid flow field is of practical interest in many areas of environmental, chemical, naval and ocean engineering
Three different models were built to predict the equivalent diameter and aspect ratio of air bubbles moving near a plunging jet
The experimental data were obtained from measurements carried out using a shadowgraph technique
Summary
The study of the dynamics of air bubbles in a liquid flow field is of practical interest in many areas of environmental, chemical, naval and ocean engineering. Even if the pressure field is not known, the availability of a good amount of data on the evolution of the size and shape of a suitable number of bubbles, as well as of data on some characteristics of the flow field, leads us to consider an interesting alternative approach for predicting the deformation of individual bubbles: machine learning algorithms These procedures, suited to dealing with nonlinear regression problems dependent on several variables, have been widely used in recent years in solving a variety of water engineering problems [24,25,26,27,28,29,30,31,32,33,34]. Measurements carried out using a shadowgraph technique, which will be described in detail
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