Volume 28, Issue 4 (Winter 2025)                   jwss 2025, 28(4): 143-156 | Back to browse issues page

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Jafari Nodoushan E, Shirzadi A. Simulation of Scouring Behind Coastal Walls by Lagrangian Method Using the 𝜇 (I) Rheology Model. jwss 2025; 28 (4) :143-156
URL: http://jstnar.iut.ac.ir/article-1-4447-en.html
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of kurdistan, Sanandaj, Iran. , a.shirzadi@uok.ac.ir
Abstract:   (49 Views)
The rapid and complex movement of sediments in rivers and coastal areas with highly erosive and unsteady flows presents river engineers with numerous problems in the geomorphology of alluvial rivers. Accurately predicting these complex processes in the water-sediment system (a multiphase, dense, granular flow system) is still a major challenge for mesh-based models. Due to the ability of meshless Lagrangian methods to model large deformations and discontinuities, meshless Lagrangian methods can provide a unique way to deal with this complexity. In the current research, the capabilities of the weakly compressibility moving particle semi-implicit (WC-MPS) model in soil-fluid interaction modeling are developed to enable the modeling of sediment transport and erosion effects behind coastal walls. In this method, granular material is considered a non-Newtonian and viscoplastic fluid. The 𝜇(I) rheological model has been used to predict the non-Newtonian behavior of the granular phase. To verify the application of the present model in simulating the interaction of liquid and solid phases, first, the widely used problem of dam break on an erodible bed was modeled. The NRMSE model was calculated to be approximately 6%, which indicates the efficiency and accuracy of the target model in this problem. At the end, the scouring of coastal walls was simulated by the WC-MPS method using 𝜇(I) rheology model. Investigations show that the processes related to erosion and scouring can be well modeled using the current Lagrangian method. The numerical results show excellent agreement with the laboratory measurements. It should be noted that the mean error of the mentioned model is estimated to be 10%.
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Type of Study: Research | Subject: Ggeneral
Received: 2024/08/29 | Accepted: 2024/10/28 | Published: 2025/01/29

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