The present study extracts human-understandable insights from machine learning (ML)-
based mesoscale closure in fluid-particle flows via several novel data-driven analysis …

Physics-based simulations are often used to model and understand complex physical
systems in domains such as fluid dynamics. Such simulations, although used frequently …

Developing deterministic neighborhood-informed point-particle closure models using
machine learning has garnered interest recently from the dispersed multiphase flow …

Physics-based simulations are often used to model and understand complex physical
systems and processes in domains like fluid dynamics. Such simulations, although used …

Filtered mesoscale model can be formulated from highly-resolved continuum or discrete
simulations. The embedded microscopic homogeneous drag closure (HDC) is of key …

As a hydraulic lifting pipeline structure widely used in deep-sea oil, gas transportation, and
sediment dredging projects, the pipeline configuration is related to the improvement of …

The drag force model is vital for capturing gas–solid flow dynamics in many simulation
approaches. Most of the homogeneous drag models in the literature are expressed as a …

The development of hydrodynamic force/torque closure models with physical fidelity is
crucial for ensuring reliable Euler–Lagrange simulations in particle‐laden flows. Our …

This study presents conventional and artificial neural network‐based data‐driven modeling
(DDM) methods to model simultaneously the filtered mesoscale drag, heat transfer and …

Modelling of fluid–particle interactions is a major area of research in many fields of science
and engineering. There are several techniques that allow modelling of such interactions …