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DOE OSTI · 2503555

Developing Drag Models for Non-Spherical Particles through Machine Learning

Ni, Rui [Johns Hopkins Univ., Baltimore, MD (United States)]
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Abstract

The overarching goal of this project is to produce comprehensive experimental and numerical datasets for gas-solid flows in well-controlled settings to understand the aerodynamic drag of non-spherical particles in the dense regime. The datasets and the gained knowledge will be utilized to train deep neural networks in TensorFlow to formulate a general drag model for use directly in NETL MFiX-DEM module in order to help to advance the accuracy and prediction fidelity of the computational tools that will be used in designing and optimizing fluidized beds and chemical looping reactors.

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BibTeXRIS

Ni, Rui [Johns Hopkins Univ., Baltimore, MD (United States)]. 2025-01-20. Developing Drag Models for Non-Spherical Particles through Machine Learning. https://doi.org/10.2172/2503555

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