OpenFOAM-based numerical modeling on wave transformation and attenuation in idealized prototype-scale mangrove forest: Regular and random waves

초록

This study examines the hydrodynamic performance of an idealized Rhizophora mangrove forest subjected to various flooding scenarios using a high-resolution computational fluid dynamics model. The numerical model was developed within the OpenFOAM framework and validated against time series of water surface elevation, wave velocity, and pressure obtained from prototype-scale experiments of Kelty et al., 2022). The simulations were further extended to capture wave–vegetation interactions, providing detailed insights into flow dynamics, wave transformation, and energy dissipation mechanisms. The investigation focused on how wave parameters, such as water depth, relative wave height, and wave steepness, and mangrove characteristics, particularly stem density, affect wave attenuation. Results show that attenuation coefficients generally decrease with increasing water depths; however, when the effect of water depth was incorporated through the submerged mangrove volume fraction, the attenuation exhibited a unified dependence on relative wave height and wave steepness. The fractional increase in mangrove density is nearly proportional to the fractional increase in wave attenuation coefficients. Empirical equations with strong correlations (R2 > 0.95) were proposed to investigate the influence of key dimensionless parameters derived from wave characteristics and vegetation properties on the attenuation coefficient. Furthermore, an artificial neural network (ANN) model was employed to identify dominant parameters driven wave attenuation, revealing relative wave height, relative forest width, and mangrove densities as primary factors, attributing to 70% of wave attenuation. The findings contribute to advancing nature-based coastal protection strategies and provide engineering guidance for incorporating mangrove forests into resilient shoreline design. © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.