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Estimating sliding drop width via side-view features using recurrent neural networks.

Sajjad Shumaly¹, Fahimeh Darvish¹, Xiaomei Li¹

¹Max Planck Institute for Polymer Research (MPI-P), Ackermannweg 10, 55128, Mainz, Germany.

Scientific Reports

|May 26, 2024

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This summary is machine-generated.

Researchers can now estimate sliding drop width from side-view videos using machine learning, eliminating the need for extra cameras. This advance improves the analysis of drop dynamics and surface interactions.

Area of Science:

Fluid dynamics
Surface science
Machine learning applications

Background:

High-speed side-view videos are used to study sliding drop dynamics.
Accurate measurement of drop width is crucial for understanding sliding physics and friction.
Current methods for width measurement require cumbersome additional equipment, limiting analysis.

Purpose of the Study:

To develop a method for estimating sliding drop width solely from side-view videos.
To eliminate the need for front-view cameras or mirrors in drop dynamics experiments.
To enable comprehensive analysis of sliding drops, including interactions with surface defects.

Main Methods:

Exploration of various regression and multivariate sequence analysis (MSA) models.

Keywords:

Bidirectional LSTM (BiLSTM)Convolutional LSTM (ConvLSTM)Convolutional neural network (CNN)Drop width estimation Gated recurrent unit (GRU)Long short-term memory (LSTM)Multivariate sequence analysis Recurrent neural network (RNN)Sliding drops

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Application of Long Short-Term Memory (LSTM) neural network with a 20-frame sliding window.

Validation of model performance using root mean square error (RMSE).

Main Results:

The LSTM model achieved the best performance with an RMSE of 67 µm.
This RMSE represents a prediction error of 2.4% for drop widths ranging from 1.6 to 4.4 mm.
The LSTM model successfully estimated drop width across the entire 5 cm sliding length.

Conclusions:

Machine learning, specifically LSTM, can accurately estimate sliding drop width from side-view videos.
This method simplifies experimental setups and expands the scope of drop dynamics research.
The technique allows for previously unattainable continuous width measurements during sliding.