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

The particle drag force coefficient is a critical variable in modeling multiphase energy systems. Even though various empirical drag models with limited conditions have been published before, developing a general drag model is still an important topic. Existing analytical and modeling techniques of particle drag are incapable of supporting the application's complexity due to their limitations to very specific conditions. This paper proposes the Drag Coefficient Correlation-aided Deep Neural Network (DCC-DNN) architecture to predict the particle drag force coefficient from various single-particle experimental data. Beyond sphericity and Reynolds number, the proposed approach includes an expanded set of features supported by the literature. Simultaneously, model regularization and meta-learning help train a generalized and more reliable drag model, despite the limited data available and the variance exhibited in individual single-particle studies. The presented model applies to spherical and non-spherical particles, providing much-needed generality and reliability for industrial applications. [Display omitted] • Modeling Drag on Non-Spherical Particles Using DNN Models. • The model was trained using over 3000 experimental data points. • DNN aided by correlations using Stack generalization and Mixture of Expert (MoE). • DNN performance supersedes all the previous drag correlations. • Advanced shape features such as lengthwise and crosswise sphericity are considered. [ABSTRACT FROM AUTHOR]