| Summary: | Numerical modelling of scour around offshore structures is still a challenging research topic for engineers and scientists due to the complex flow-structure-seabed interactions. In comparison to single-phase models and Eulerian models with Exner equation, a multiphase approach has advantages in interpreting the flow-particle and particle-particle interactions. In the present study, an Euler-Lagrange multiphase approach is adopted to develop a new scour model in order to simulate the air-water-sediment interplay simultaneously while being computationally efficient. The model is able to represent free-surface flow with a mobile bed, which is often critical for realistic scour modelling. Based on the open source computational fluid dynamics (CFD) software package OpenFOAM®, the model solves the Navier-Stokes equations on an Eulerian computational grid. The sediment particles are traced using the multiphase particle-in-cell (MP-PIC) method in a Lagrangian approach. The drag force from the fluid, body forces and inter-particle stresses as well as the interphase momentum transfer are all accounted for in the model. The model system is calibrated using several simple test cases, including a falling particle and steady flow passing isolated blocks, to identify optimal parameters for model operation. The model is then validated against available experimental data on a steady current around a vertical cylinder and sand suspension under oscillatory sheet flow, amongst other tests, with satisfactory agreement. Application of the model against laboratory experiments includes benchmark scour cases underneath a horizontal pipeline under currents and waves, respectively. The tunnel erosion and lee-wake erosion stages are captured well by the model. The scour prediction matches with the measurements. In addition, the onset of scour is reproduced vigorously without any additional numerical assumptions or approximations. The model's capability to resolve the scour process and reveal the mechanisms involved is presented well.
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