{"id":256,"date":"2015-01-27T22:28:51","date_gmt":"2015-01-27T22:28:51","guid":{"rendered":"http:\/\/faculty.engineering.ucdavis.edu\/bombardelli\/?page_id=256"},"modified":"2015-02-05T19:35:22","modified_gmt":"2015-02-05T19:35:22","slug":"numerical-modeling-of-sediment-transport-near-the-bed-using-a-two-phase-flow-approach","status":"publish","type":"page","link":"https:\/\/faculty.engineering.ucdavis.edu\/bombardelli\/research\/numerical-modeling-of-sediment-transport-near-the-bed-using-a-two-phase-flow-approach\/","title":{"rendered":"Numerical Modeling of Sediment Transport Near the Bed Using a Two-Phase Flow Approach"},"content":{"rendered":"

Numerical Modeling of Sediment Transport Near the Bed Using a Two-Phase Flow Approach<\/strong><\/h3>\n

Motivation<\/span><\/strong><\/p>\n

It is a notably difficult task to provide accurate\u00a0 prediction of particle\u00a0 motion and interaction among particles when they move close to the bed of rivers\/channels. Some of the difficulties are associated to :<\/p>\n\n\n\n\n\n
\u2022<\/td>\nParticles present different motion patterns near bed: sliding, rolling and saltation.<\/td>\n<\/tr>\n
\u2022<\/td>\nSmall particles can be ejected from the wall and be transported by suspension.<\/td>\n<\/tr>\n
\u2022<\/td>\nParticles collide with the bed and among them.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

How can we incorporate all these elements in a model to predict bedload transport, avoiding empirical correlations?
\n
\n<\/b>Project Objectives <\/span><\/b>
\n<\/b>
\nThe main objective of this work is to develop a two-phase flow model to integrate fluid and particle motion using an Eulerian-Lagrangian approach close to the channel bed. This work focuses on the water flow close to the wall and the transport of sediment in bed load motion, and it includes processes that have been disregarded in most existing models, most notably inter-particle collision.<\/p>\n

The specific objectives of this study are:<\/p>\n\n\n\n\n\n\n
\u2022<\/td>\nTo develop and validate an Eulerian-Lagrangian model to simulate the saltation of particles close to the bed. The Lagrangian model is intended to be coupled with highly-resolved velocity fields, using Large Eddy Simulations (LES) or Direct Numerical Simulations (DNS). Validation of this model is accomplished via-comparison with experimental data.<\/td>\n<\/tr>\n
\u2022<\/td>\nTo optimize the computational effort of the Lagrangian model to calculate particle trajectories and velocities, by revisiting the formulations for all forces; with special emphasis is put on the Basset force.<\/td>\n<\/tr>\n
\u2022<\/td>\nTo examine existing bed-representation sub-models, to propose a simple yet realistic new approach and to validate it via comparison with experimental data.<\/td>\n<\/tr>\n
\u2022<\/td>\nTo gain more understanding about the response of saltating particles to the velocity fluctuations in the flow (i.e., to turbulence). To that end, a highly-resolved flow simulation, considering one-way coupling between phases, is developed.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n


\nResults<\/b>
\n<\/span><\/b>
\n2-D Particle Tracking Code<\/b>
\nA two-dimensional (2-D) particle tracking computational code was developed in FORTRAN, including a rebound sub-model and a bed roughness representation. The model was validated through comparison of numerical predictions with experimental data.<\/p>\n

\"Figure<\/a><\/p>\n

To optimize the computational cost of the particle tracking model, a new methodology to calculate the Basset force was developed and it was tested for particles of both small and large size (See Bombardelli, Gonzalez and Ni\u00f1o, In press).<\/p>\n

3-D Particle Tracking Code<\/b>
\nA three-dimensional (3-D) particle tracking model was developed in FORTRAN, which includes the description of both the particle translational and rotational velocity at every moment. An assessment of existing sub-models for bed roughness representation is introduced in this chapter together with a new sub-model. The validation of the best sub-model is accomplished by comparing its performance with experimental data. The computational code also considers the motion of multiple particles and an algorithm to treat the inter-particle collisions.<\/p>\n

Multiple particle simulations under a non turbulent velocity were performed, using the inter-particle collision algorithm\u00a0 presented in Yamamoto et al. (2001). A video featuring an example of two particles colliding using this methodology is presented here.<\/p>\n

Video 1: 2D View of a bed collision and an interparticle collision event<\/p>\n