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A numerical approach for particle-vortex interactions based on volume-averaged equations
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Description
To study the dynamics of particles in turbulence when their sizes are comparable to the smallest eddies in the flow, the Kolmogorov length scale, efficient and accurate numerical models for the particle-fluid interaction are still missing. Therefore, we here extend the treatment of the particle feedback on the fluid based on the volume-averaged fluid equations (VA simulation) in the previous study of the present authors, by estimating the fluid force correlated with the disturbed flow. We validate the model against interface-resolved simulations using the immersed-boundary method. Simulations of single particles show that the history effect is well captured by the present estimation method based on the disturbed flow. Similarly, the simulation of the flow around a rotating particle demonstrates that the lift force is also well captured by the proposed method. We also consider the interaction between non-negligible size particles and an array of Taylor-Green vortices. For density ratios $��_d/��_c\geq$ 10, the results show that the particle motion captured by the VA approach is closer to that of the fully-resolved simulations than that obtained with a traditional two-way coupling simulation. The flow disturbance is also well represented by the VA simulation. In particular, it is found that history effects enhance the curvature of the trajectory in vortices and this enhancement increases with the particle size. Furthermore, the flow field generated by a neighboring particle at distances of around ten particle diameters significantly influences particle trajectories. The computational cost of the VA simulation proposed here is considerably lower than that of the interface-resolved simulation.
54 pages, 20 figures
Journal
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- International Journal of Multiphase Flow
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International Journal of Multiphase Flow 104 188-205, 2018-07
Elsevier BV
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Details 詳細情報について
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- CRID
- 1360285707179205632
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- ISSN
- 03019322
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- Article Type
- journal article
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- Data Source
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- Crossref
- KAKEN
- OpenAIRE