A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion
Coefficient and Average Solute Transport Velocity in Porous Media
Abstract
Dispersion coefficients and the average solute transport velocity are
pivotal for groundwater solute transport modeling. Accurately and
efficiently determining these parameters is challenging due to
difficulties in directly correlating them with pore-space structure. To
address this issue, we introduced the Physics-enhanced Convolutional
Neural Network-Transformer (PhysenCT-Net), an innovative model designed
to concurrently estimate the longitudinal dispersion coefficient and
average solute transport velocity in three-dimensional porous media.
PhysenCT-Net exhibited excellent predictive performance on unseen
testing datasets and significantly reduced computational demands.
Comprehensive evaluations confirmed its robust generalization across
various flow conditions and pore structures. Notably, the longitudinal
dispersion coefficient predictions closely align with established
empirical relationships involving Péclet number, affirming the model’s
physical interpretability and potential to aid in simulating transport
phenomena in porous media.