A Hybrid CFD–ML Approach for Rapid Assessment of Particle Dispersion in a Port-Industrial Environment

Airborne particulate emissions originating from bulk-material handling operations constitute an increasingly critical environmental and public health issue in port-industrial areas located near residential areas. This study introduces a novel hybrid framework integrating high-fidelity Computational Fluid Dynamics (CFD) with surrogate Machine Learning (ML) techniques for the rapid assessment of particle dispersion in port-industrial environments. Focusing on the Port of El Grao in Castellón de la Plana (Spain), the study employs detailed geometric reconstructions derived from LiDAR data and cadastral maps to build an accurate three-dimensional digital model of the area. The turbulent atmospheric boundary layer and particle dispersion dynamics were simulated using two different OpenFOAM solvers within a circular computational domain designed to reproduce realistic wind conditions. The ML surrogate model, based on a decoder-style Multilayer Perceptron (MLP) architecture, processes two-dimensional slices of dispersion fields across particle diameter classes, enabling predictions in milliseconds with an acceleration factor of approximately 8×106 over traditional CFD while preserving high fidelity, as validated by error metrics such as the F1 score and Precision values exceeding 0.8 and 0.76 respectively. This approach not only addresses computational inefficiencies but also lays the groundwork for real-time air quality monitoring and sustainable urban planning, potentially integrating with digital twins fed by live weather data.