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In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
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Physics Perception in Sloshing Scenes With Guaranteed Thermodynamic Consistency.

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    Area of Science:

    • Fluid dynamics
    • Machine learning
    • Physics perception

    Background:

    • Limited data and partial measurements are common challenges in physics perception.
    • Understanding and predicting the behavior of sloshing liquids is crucial in various applications.

    Purpose of the Study:

    • To develop a strategy for learning the full state of sloshing liquids from limited free-surface measurements.
    • To enable real-time fluid reasoning and prediction of future scenarios.

    Main Methods:

    • Utilizing recurrent neural networks (RNNs) to project limited information onto a reduced-order manifold.
    • Training deep neural networks with inductive biases to ensure thermodynamic consistency.
    • Integrating a computer vision system for real-world performance testing.

    Main Results:

    • Successful reconstruction of unknown information from partial measurements.
    • Capability to perform real-time fluid reasoning and predict future states.
    • Demonstrated performance with real-world data via a computer vision system.

    Conclusions:

    • The proposed RNN-based approach effectively reconstructs and predicts sloshing liquid dynamics from limited data.
    • The method ensures physical consistency by adhering to thermodynamic principles.
    • The system provides real-time insights and predictions, enhanced by augmented reality visualization.