What is it about?
The impact of a drop on a solid surface is an important phenomenon that has various implications and applications. Especially when the drop splashes, it can cause soil erosion, dispersal of plant pathogens, and deterioration of printing and paint qualities, among others. Therefore, it is necessary to predict the deformation of a splashing drop to minimize the adverse effects. However, the multiphase nature causes complications in the prediction. To tackle this problem, several drop-impact studies have adopted artificial intelligence (AI) models and have shown excellent performances. However, the models developed in these studies use physical parameters as inputs and outputs, thus difficult to capture the deformation of the impacting drop. In this research, the architecture of an encoder–decoder, which can take images as input and output, has been adopted to develop an image-based AI model to predict the drop deformation. By taking a pre-impact image sequence as the input, the trained encoder–decoder has successfully generated an image sequence that shows the deformation of a drop during the impact, as the output. Remarkably, the generated image sequences are very similar to the actual image sequences captured during the experiment. The quantitative evaluation of the generated image sequences showed that in each frame of these generated image sequences, the spreading diameter of the drop was found to be in good agreement with that of the actual image sequences. Moreover, there was also a high accuracy in splashing/non-splashing prediction. These findings demonstrate the ability of the trained encoder–decoder to generate image sequences that can accurately represent the deformation of a drop during the impact.
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Why is it important?
The approach proposed by this research offers a faster and more cost-effective alternative to conventional experimental and numerical studies. This achievement is important in understanding and minimizing the adverse effects of splashing. In addition, it has shown the great potential of using AI and machine learning for scientific studies.
Perspectives
I hope this article will inspire more researchers to use AI to predict phenomena with complex shapes and deformation.
Dr Jingzu Yee
Tokyo Noko Daigaku
Read the Original
This page is a summary of: Prediction of the morphological evolution of a splashing drop using an encoder–decoder, Machine Learning Science and Technology, April 2023, Institute of Physics Publishing,
DOI: 10.1088/2632-2153/acc727.
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