Geometry-Aware Latent Representation Learning for Drag Coefficient Prediction of Irregular Particles

Predicting the drag coefficient $C_{\mathrm{d}}$ of irregular particles requires representing their complex three-dimensional geometry in a form suitable for predictive models. Empirical drag correlations and many data-driven models represent particle geometry through a small set of hand-crafted shape descriptors. Geometric differences omitted by these descriptors are unavailable to the predictor. We propose a variational autoencoder-multilayer perceptron (VAE-MLP) framework that learns a latent representation directly from voxelized particle geometry. Geometry reconstruction and drag coefficient prediction jointly guide the learning of this representation. We compare VAE-MLP with a descriptor-based MLP (Descriptor-MLP) and established empirical correlations using CFD data for synthetic particle geometries at Reynolds numbers of 10–200. When the evaluated geometric variations are represented in the training data, the two learned models achieve comparable accuracy for regular ellipsoids, while VAE-MLP yields lower errors for irregular particles. When high-order surface perturbations are excluded from training, VAE-MLP achieves lower overall mean absolute relative error in all five training runs. At the strongest tested perturbation, the mean errors across these runs are 4.97% for VAE-MLP and 9.88% for Descriptor-MLP. These results support learning geometric representations directly from particle geometry to improve drag prediction for the irregular geometries and unseen surface perturbations examined here.