What is it about?
Particle-laden turbulent flow is observed in many natural and industrial contexts. Turbulent flow is inherently complex, and the addition of particle interactions further increases this complexity. Using deep learning models as a complementary approach can enhance analytical and statistical methods, improving our understanding of the behavior of such flows.
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Why is it important?
Understanding the dynamics of particle-laden turbulent flows is essential due to their critical role in a wide range of natural and industrial processes. Some cases are spray combustion, air pollution transport, sediment flow in rivers, volcanic ash dispersion, respiratory droplet spread, cloud formation, aerosol drug delivery, and ventilation systems.
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This page is a summary of: Data-driven deep learning models in particle-laden turbulent flow, Physics of Fluids, February 2025, American Institute of Physics,
DOI: 10.1063/5.0251765.
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