Computational Fluid Dynamics 2008 by Takaji Inamuro (auth.), Prof. Haecheon Choi, Prof. Hyong

By Takaji Inamuro (auth.), Prof. Haecheon Choi, Prof. Hyong Gwon Choi, Prof. Jung Yul Yoo (eds.)

This quantity offers the court cases of the ICCFD five - The 5th foreign convention on Computational Fluid Dynamics, held July 07 - eleven, 2008 in Seoul, South Korea.

The contributions have been selected out of 3 different types of study: leading edge modeling of stream physics, cutting edge set of rules improvement for move simulation, optimization and keep an eye on, and complex multidisciplinary functions utilizing the abovementioned leading edge technologies.

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Therefore, the weakly ionized gas is far from thermodynamic equilibrium. Meanwhile the drift motion and diffusion of charged particles, including the ambipolar diffusion, profoundly modifies the transport properties of the ionized medium. The electromagnetic effects generated by DCD and DBD can be summarized as the Lorentz acceleration, electrostatic/electrodynamic force, plasma and electrode heating all of which have limited magnitude [6,7]. The Lorentz acceleration is significant only in the presence of an externally applied magnetic field and due to the relatively low current density the magnitude of electrodynamic force is just a few kN/m3.

Lattice Boltzmann simulations for flow and heat/mass transfer problems in a three-dimensional porous structure. Int. J. Numer. Meth. : Simulation of Rayleigh-B´enard convection using a lattice Boltzmann method. Phys. Rev. : A novel thermal model for the lattice Boltzmann method in incompressible limit. J. Comput. Phys. : Sedimentation of homogeneous suspensions of non-Brownian spheres. Phys. : A lattice Boltzmann method for incompressible two-phase flows with large density differences. J. Comput.

Applying the DWR approach to this situation yields the following “primal” residual: ρ(uh )(z −zh ) := (R(uh ), z v −zhv )K + (r(uh ), z v −zhv )∂K\(Γin ∪Γrigid ) K∈Th + (z p −zhp , ∇ · vh )K − (v in − vh , ∂n z v )∂K∩Γin , . Γin ΓQ ΓQ n · σ · d ds cdrag := c S Γout S σ := 12 ν(∇v + ∇v T ) − pI d := (0, 1)T flow direction Fig. 11. Configuration of the drag minimization problem Adaptive Finite Element Discretization of Flow Problems 41 Fig. 12. Streamlines of uncontrolled flow (P = 0), controlled flow (P = Popt ), and adapted mesh Table 2.

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