Computational Fluid Dynamics: Incompressible Turbulent Flows by Takeo Kajishima, Kunihiko Taira (auth.)

By Takeo Kajishima, Kunihiko Taira (auth.)

This textbook provides numerical answer ideas for incompressible turbulent flows that happen in quite a few medical and engineering settings together with aerodynamics of ground-based cars and low-speed airplane, fluid flows in power platforms, atmospheric flows, and organic flows. This ebook encompasses fluid mechanics, partial differential equations, numerical equipment, and turbulence types, and emphasizes the basis on how the governing partial differential equations for incompressible fluid movement could be solved numerically in a correct and effective demeanour. large discussions on incompressible circulation solvers and turbulence modeling also are provided. this article is a perfect educational source and reference for college students, learn scientists, engineers attracted to examining fluid flows utilizing numerical simulations for primary study and business applications.

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Extra info for Computational Fluid Dynamics: Incompressible Turbulent Flows

Example text

We observe that the advection speed c is modified to be (c + k 2 α3 − k 4 α5 + · · · ) with truncation error. The error is generated when we have nonzero α j for odd j. Hence, we see that the phase speed is altered by the inclusion of the odd-derivative terms in the truncation error. The profile of a propagating wave can be influenced by this wave number-dependent phase-speed error, leading to the wave becoming dispersive. Furthermore, (−k 2 α2 + k 4 α4 − · · · ) is added as a component to the solution which alters the growth or decay of the solution.

The idea of finite-volume methods is to solve for the unknown variables through such relations. Structured and unstructured grids can be used. For finite-volume methods, LeVeque [11] and Ferziger and Peri´c [5] are excellent references. Finite-Element Method (FEM) This method utilizes the weak formulation of the governing equations, which has a test function multiplied to both sides of the equation [17]. The profile of the variable is provided by the product of the variable and a basis function at the vertex.

We accordingly find that Eq. 81) becomes ∂f c ∂2 f ∂f +c − + cO( ∂t ∂x 2 ∂x 2 2 ) = 0. 82) Note that we started with a pure advection equation, Eq. 74), and did not include any diffusive effects. By employing the upwind difference, Eq. 82) now 2 contains a diffusive term c2 ∂∂x 2f with diffusivity of c /2. In contrast to physical diffusivity caused by viscous diffusion, such numerical effect due to truncation error is referred to as numerical diffusion. When this effect appears in the momentum equation, numerical diffusivity is called numerical viscosity.

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