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

González Barberá, Alejandro; Nabi, Raheem; Macias, Aina; Monrós-Andreu, Guillem; Chiva, Sergio

doi:10.3390/environments13010019

Open AccessArticle

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

by

Alejandro González Barberá

^1,2

,

Raheem Nabi

¹,

Aina Macias

¹

,

Guillem Monrós-Andreu

¹

and

Sergio Chiva

^1,*

¹

Department of Mechanical Engineering and Construction, Universitat Jaume I, 12006 Castellón de la Plana, Spain

²

Department of Computer Science and Engineering, Universitat Jaume I, 12071 Castellón de la Plana, Spain

^*

Author to whom correspondence should be addressed.

Environments 2026, 13(1), 19; https://doi.org/10.3390/environments13010019

Submission received: 19 November 2025 / Revised: 17 December 2025 / Accepted: 24 December 2025 / Published: 31 December 2025

(This article belongs to the Special Issue Advances in Urban Air Pollution: 2nd Edition)

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Abstract

Airborne dust emissions from bulk cargo handling in port terminals can degrade local air quality, but traditional dispersion models are often too slow or coarse to support rapid operational decisions. There is thus a pressing need for efficient tools that retain the spatial detail of CFD while enabling near-real-time scenario evaluation. In this work, we develop and test a hybrid framework that couples an RANS-based CFD model of dust dispersion with a neural network surrogate to rapidly predict exposure patterns for a bulk terminal under variable wind and operational 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 \times 10^{6}

over traditional CFD while preserving high fidelity, as validated by performance 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.

Keywords: machine learning; CFD; OpenFOAM; particulate matter; port areas

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MDPI and ACS Style

González Barberá, A.; Nabi, R.; Macias, A.; Monrós-Andreu, G.; Chiva, S. A Hybrid CFD–ML Approach for Rapid Assessment of Particle Dispersion in a Port-Industrial Environment. Environments 2026, 13, 19. https://doi.org/10.3390/environments13010019

AMA Style

González Barberá A, Nabi R, Macias A, Monrós-Andreu G, Chiva S. A Hybrid CFD–ML Approach for Rapid Assessment of Particle Dispersion in a Port-Industrial Environment. Environments. 2026; 13(1):19. https://doi.org/10.3390/environments13010019

Chicago/Turabian Style

González Barberá, Alejandro, Raheem Nabi, Aina Macias, Guillem Monrós-Andreu, and Sergio Chiva. 2026. "A Hybrid CFD–ML Approach for Rapid Assessment of Particle Dispersion in a Port-Industrial Environment" Environments 13, no. 1: 19. https://doi.org/10.3390/environments13010019

APA Style

González Barberá, A., Nabi, R., Macias, A., Monrós-Andreu, G., & Chiva, S. (2026). A Hybrid CFD–ML Approach for Rapid Assessment of Particle Dispersion in a Port-Industrial Environment. Environments, 13(1), 19. https://doi.org/10.3390/environments13010019

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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

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