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Physically sound, self-learning digital twins for sloshing fluids.

Beatriz Moya1, Iciar Alfaro1, David Gonzalez1

  • 1Aragon Institute of Engineering Research (I3A), Universidad de Zaragoza, Zaragoza, Spain.

Plos One
|June 17, 2020
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Summary
This summary is machine-generated.

A new self-learning digital twin strategy accurately predicts fluid sloshing by inferring fluid behavior from videos. This enables real-time simulation-assisted decision-making for fluid dynamics.

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Area of Science:

  • Fluid dynamics
  • Robotics
  • Computational physics

Background:

  • Fluid sloshing is critical in applications like robotic fluid manipulation.
  • Accurate simulation-assisted decision-making requires precise fluid behavior prediction.
  • Existing methods may lack real-time predictive capabilities for complex fluid dynamics.

Purpose of the Study:

  • To develop a novel self-learning digital twin strategy for fluid sloshing phenomena.
  • To infer the constitutive behavior of fluids from observational data.
  • To enable real-time prediction and comparison of fluid responses.

Main Methods:

  • Utilizing video sequences of fluid sloshing to infer linear or non-linear constitutive behavior.
  • Constructing a reduced-order model (ROM) through thermodynamics-informed data-driven learning.
  • Integrating augmented reality for real-time visualization and comparison with actual fluid responses.

Main Results:

  • The developed digital twin strategy accurately predicts fluid responses to container movements.
  • Real-time forecasting of fluid behavior is achieved through the data-driven ROM.
  • The system provides insightful physics information and enables direct comparison of predicted vs. actual fluid behavior.

Conclusions:

  • The self-learning digital twin offers a powerful tool for understanding and predicting fluid sloshing.
  • Thermodynamics-informed data-driven learning is effective for constructing predictive fluid dynamics models.
  • The integration of AR enhances the utility for simulation-assisted decision-making and scientific insight.