A machine learning approach for estimating surface tension based on pendant drop images

• Published in: Fluid phase equilibria 2021-06, Vol.538, p.113012, Article 113012
• Main Authors: Soori, Tejaswi, Rassoulinejad-Mousavi, Seyed Moein, Zhang, Lige, Rokoni, Arif, Sun, Ying
• Format: Article
• Language: English
• Subjects: Deep neural networks; Drop shape analysis; Machine learning; Pendant drop; Surface tension
• ISSN: 0378-3812; 1879-0224
• DOI: 10.1016/j.fluid.2021.113012

•Prediction of surface tension (ST) value of pendant drops by machine learning (ML).•Convolutional neural network trained using pendant drop images of known ST.•Estimating ST for unknown pendant drops outside the range of training data.•ST prediction of any arbitrary pendant drop shape during transient drop generation.•ML model capable of ST prediction independent of fluid properties. An image-based machine learning (ML) method is introduced to predict the surface tension of an ethanol-water pendant drop made of unknown ethanol-water composition. In contrast to previous neural-network based surface tension predictors that rely on using either the simplified molecular-input line-entry system or experimentally measured surface tension values, the present image-based deep neural network model directly predicts surface tensions based on arbitrary pendant drop shapes at any stage before breakup. Using convolutional neural networks (CNNs), surface tension values are accurately obtained independent of the drop size and liquid properties. Two CNN architectures are presented that accurately predict the surface tension of a pendant drop for three different ML models. To improve the generality of the ML models, image data augmentation technique is used to generate more representatives from available data. Approximating the surface tension for unknown pendant drops outside the range of the given classes has also been demonstrated. The trained machine learning models have an overall accuracy of about 98% in predicting the surface tension of a pendant drop containing an unknown ethanol-water composition. Additionally, the ML models are tested on unknown methanol-water mixtures to demonstrate the generality and the results show good predicting accuracy. Besides the accuracy of CNN models, performance measures including Precision, Recall and F1-score are reported for each surface tension value in the dataset. Compared with the physics-based axisymmetric drop shape analysis techniques, the present method is not limited to equilibrium pendant drop images and is much faster and more versatile. This image-based ML method shows great promise in directly predicting the surface tension values based on vastly available pendant drop images.