An enhanced cell-based model for contaminant dispersion in marine environments

In practice, achieving a balance between accuracy, stability, and computational efficiency in modeling contaminant dispersion in marine environments remains challenging due to complex physical dynamics and numerical constraints. To address these challenges, an enhanced cell-based model (CBM) is developed and applied to simulate pollutant transport in the ocean. The CBM discretizes the spatial domain into uniform cells, resulting in a naturally parallelizable structure, and characterizes the transport process by incorporating both water flow-driven convection and diffusion effects. Moreover, two approaches are proposed for estimating the diffusion coefficient, and their performance is compared to a first-order upwind scheme finite-difference method (FDM) solution. Finally, the CBM is comprehensively compared with both the FDM and the finite-element method (FEM) solvers under varying spatial and temporal resolutions. Simulation results show that the CBM is less affected by the Courant-Friedrichs-Lewy (CFL) conditions and demonstrates stable convergence where the FDM fails or requires stricter settings. In addition, the CBM offers a favorable trade-off between accuracy and computational efficiency under coarse configurations. These results indicate that the CBM provides a reliable foundation for dynamic modeling and integration with learning-based frameworks in marine environment simulations.

Authors

Institutions

Publication Details

Published
2025-06-16
DOI
https://doi.org/10.1109/oceans58557.2025.11104304
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An enhanced cell-based model for contaminant dispersion in marine environments

Bart De Schutter, Ying Ma, Meichen Guo
Air Quality Monitoring and Forecasting
article

An enhanced cell-based model for contaminant dispersion in marine environments

Bart De Schutter, Ying Ma, Meichen Guo
article en

Abstract

In practice, achieving a balance between accuracy, stability, and computational efficiency in modeling contaminant dispersion in marine environments remains challenging due to complex physical dynamics and numerical constraints. To address these challenges, an enhanced cell-based model (CBM) is developed and applied to simulate pollutant transport in the ocean. The CBM discretizes the spatial domain into uniform cells, resulting in a naturally parallelizable structure, and characterizes the transport process by incorporating both water flow-driven convection and diffusion effects. Moreover, two approaches are proposed for estimating the diffusion coefficient, and their performance is compared to a first-order upwind scheme finite-difference method (FDM) solution. Finally, the CBM is comprehensively compared with both the FDM and the finite-element method (FEM) solvers under varying spatial and temporal resolutions. Simulation results show that the CBM is less affected by the Courant-Friedrichs-Lewy (CFL) conditions and demonstrates stable convergence where the FDM fails or requires stricter settings. In addition, the CBM offers a favorable trade-off between accuracy and computational efficiency under coarse configurations. These results indicate that the CBM provides a reliable foundation for dynamic modeling and integration with learning-based frameworks in marine environment simulations.

Delft University of Technology (NL)
European Commission
Life below water
Openalex Percentile: Top 81%
Air Quality Monitoring and Forecasting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.