DCC-DNN: A deep neural network model to predict the drag coefficients of spherical and non-spherical particles aided by empirical correlations

Presa-Reyes, Maria; Mahyawansi, Pratik; Hu, Beichao; McDaniel, Dwayne; Chen, Shu-Ching

doi:10.1016/j.powtec.2024.119388

Powder Technology

2024

DOI: 10.1016/j.powtec.2024.119388

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DCC-DNN: A deep neural network model to predict the drag coefficients of spherical and non-spherical particles aided by empirical correlations

Maria Presa-Reyes,

Pratik Mahyawansi,

Beichao Hu

et al.

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DCC-DNN: A deep neural network model to predict the drag coefficients of spherical and non-spherical particles aided by empirical correlations

Cited by 4 publications

References 45 publications

Advancing building management with nano-enhanced carbon materials: a machine learning-driven business and economic analysis

Advancing building management with nano-enhanced carbon materials: a machine learning-driven business and economic analysis

Accurate machine-learning-based prediction of aerodynamic and heat transfer coefficients for cylindrical biomass particles

Terminal velocity and drag coefficient of a smooth steel sphere moving in the water-filled vertical and inclined glass pipe (Newton regime)

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