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Adaptive Grid Techniques for the Efficient Simulation of Shallow Coastal Systems

https://doi.org/10.23947/2587-8999-2025-9-3-44-55

Abstract

Introduction. Shallow coastal systems are highly dynamic and require accurate numerical models for predicting tides, storm surges, and coastal hazards. Traditional uniform-grid approaches often incur high computational costs, limiting their applicability for operational forecasting. Adaptive grid techniques provide a promising alternative by concentrating resolution in dynamically important regions while reducing the total computational burden.

Materials and Methods. We developed an adaptive-grid framework based on the depth-averaged shallow-water equations. The model employs a second-order finite-volume scheme with TVD limiting on a quadtree mesh. Mesh adaptation is driven by gradient indicators of free-surface elevation and velocity, ensuring high resolution in areas with steep gradients, tidal fronts, and complex bathymetry. Three numerical experiments were performed: a harmonic tide, a wind-driven storm surge, and combined tidal-wind forcing.

Results. The proposed method demonstrated robust wetting-drying capabilities, a mass conservation error below 0.06%, and skill metrics of RMSE ≤ 0.07 m and NSE ≥ 0.90. Compared to a uniform grid of the same finest resolution, Adaptive Mesh Refinement (AMR) reduced the mean cell count by ~32% and wall time by ~1.5×, with less than 3.5% change in the L₂ error norm.

Discussion. The results confirm that adaptive meshing preserves physical accuracy while substantially reducing computational cost. This makes the method a suitable tool for high-resolution coastal hazard assessment and operational forecasting.

Conclusion. Further work will focus on extending the approach to three-dimensional flows and incorporating data assimilation for real-time applications.

About the Authors

A. I. Sukhinov
Don State Technical University
Russian Federation

Alexander I. Sukhinov, Corresponding Member of the Russian Academy of Sciences, Doctor of Physical and
Mathematical Sciences, Professor, Director of the Research Institute of Mathematical Modeling and Forecasting of Complex Systems

1, Gagarin Sq., Rostov-on-Don, 344003



S. V. Protsenko
Taganrog Institute named after A.P. Chekhov (branch) of RSUE
Russian Federation

Sofia V. Protsenko, Candidate of Physical and Mathematical Sciences, Associate Professor of the Department of Mathematics, Research Fellow

48, Initiative St., Taganrog, 347936



E. A. Protsenko
Taganrog Institute named after A.P. Chekhov (branch) of RSUE
Russian Federation

Elena A. Protsenko, Candidate of Physical and Mathematical Sciences, Associate Professor of the Department of Mathematics, Leading Research Fellow

48, Initiative St., Taganrog, 347936



References

1. Zhang T., Wang J., Li X., Yu F. Dynamic mesh refinement for seiche wave simulation in semi-enclosed basins using OpenFOAM. Environmental Fluid Mechanics. 2022;22:1075–1094. https://doi.org/10.1007/s10652-022-09891-2

2. Mendoza O., Piggott M., Cotter C. Dynamic unstructured mesh adaptation for estuarine flooding. Water. 2022;14(3):391. https://doi.org/10.3390/w14030391

3. Kim S.H., Lee H., Jeong J. Nested-grid simulation of nutrient transport using Delft3D FM. Coastal Engineering. 2023;181:104247. https://doi.org/10.1016/j.coastaleng.2022.104247

4. Dawson C., Kubatko E.J., Westerink J.J. Adaptive mesh refinement in ADCIRC for hurricane-induced storm surge. Ocean Modelling. 2021;158:101736. https://doi.org/10.1016/j.ocemod.2020.101736

5. Guo L., Feng J., Song Z. Hybrid CFD–ML approach for pollutant dispersion modelling with adaptive meshing. Water. 2023;15(7):1295. https://doi.org/10.3390/w15071295

6. Popinet S. Adaptive quadtree meshing for shallow flows using Basilisk. Journal of Computational Physics. 2021;447:110656. https://doi.org/10.1016/j.jcp.2021.110656

7. Bihlo A., MacLachlan S. Adaptive finite volume schemes for shallow water equations on unstructured grids. Ocean Modelling. 2021;162:101831. https://doi.org/10.1016/j.ocemod.2021.101831

8. Zhang Y., Zhang J. Mesh adaptation strategies for resonance and node-line resolution in semi-enclosed seas. Journal of Marine Science and Engineering. 2023;11(2):356. https://doi.org/10.3390/jmse11020356

9. De Lillo F., Cecconi F., Lacorata G., Vulpiani A. Lagrangian chaos and turbulent dispersion. EPL. 2008;84:50009. https://doi.org/10.1209/0295-5075/84/50009


Review

For citations:


Sukhinov A.I., Protsenko S.V., Protsenko E.A. Adaptive Grid Techniques for the Efficient Simulation of Shallow Coastal Systems. Computational Mathematics and Information Technologies. 2025;9(3):44-55. https://doi.org/10.23947/2587-8999-2025-9-3-44-55

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ISSN 2587-8999 (Online)