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Updated: Oct 31, 2025

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Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets.

Sahar Andalib1, Kunihiko Taira2, H Pirouz Kavehpour2

  • 1Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA, 90095, USA. sandalib@ucla.edu.

Scientific Reports
|July 1, 2021
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Summary

This study uses machine learning to predict methanol droplet evaporation regimes and behaviors. Data-driven models accurately estimate droplet evolution, even for conditions not seen during training, advancing understanding of complex fluid dynamics.

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Area of Science:

  • Fluid Dynamics
  • Physical Chemistry
  • Data Science

Background:

  • Sessile droplet evaporation is critical in various applications like biodiagnostics and microfabrication.
  • Studying multi-component droplet evaporation experimentally is complex due to parameter space and non-axisymmetric behavior.
  • Compositional changes can alter droplet properties, leading to different evaporation regimes.

Purpose of the Study:

  • To analyze sessile methanol droplet evolution using data-driven classification and regression techniques.
  • To develop machine learning models for predicting droplet evaporation regimes, base diameter, contact angle, and humidity levels.
  • To demonstrate the capability of machine learning in capturing the physics of binary droplet evolution.

Main Methods:

  • Trained machine learning models using experimental data of methanol droplet evolution.
  • Employed classification algorithms (Decision Tree, Naïve Bayes) to estimate droplet evaporation regimes.
  • Utilized regression algorithms to predict droplet base diameter, contact angle, and surrounding humidity over time.

Main Results:

  • Decision Tree classifier outperformed Naïve Bayes for droplet regime estimation.
  • Regression models showed promising performance in estimating humidity, base diameter, and contact angle.
  • Models accurately predicted methanol droplet evolution under novel conditions, validating their predictive power.

Conclusions:

  • Machine learning offers a powerful approach to study complex droplet evaporation phenomena.
  • Data-driven models can effectively capture the underlying physics of binary droplet systems.
  • This methodology provides a robust tool for analyzing and predicting droplet behavior in diverse scientific and industrial fields.