Computational Models for Turbulent Reacting Flows (Cambridge by Rodney O. Fox

By Rodney O. Fox

This ebook offers the present state-of-the-art in computational types for turbulent reacting flows, and analyzes rigorously the strengths and weaknesses of many of the strategies defined. the focal point is on formula of useful versions in place of numerical concerns bobbing up from their answer. A theoretical framework in response to the one-point, one-time joint chance density functionality (PDF) is built. it truly is proven that every one quite often hired versions for turbulent reacting flows might be formulated by way of the joint PDF of the chemical species and enthalpy. types according to direct closures for the chemical resource time period in addition to transported PDF equipment are coated intimately. An creation to the speculation of turbulent and turbulent scalar delivery is equipped for completeness. The booklet is aimed toward chemical, mechanical, and aerospace engineers in academia and undefined, in addition to builders of computational fluid dynamics codes for reacting flows.

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Extra resources for Computational Models for Turbulent Reacting Flows (Cambridge Series in Chemical Engineering)

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In the dissipative range, the turbulent kinetic energy is dissipated by molecular viscosity. The turbulent energy cascade in non-stationary homogeneous turbulence can be described by spectral transport equations derived from the Navier–Stokes equation as described in the next section. As the Reynolds number increases, the separation between the energy-containing and dissipative ranges increases, and turbulent eddies in these two ranges become statistically independent. , if i = j, then u i ∇ 2 u j = u i ∇ 2 u j = 0.

16) are found by averaging with respect to the internal-age transfer function23 H (α, β) and the environments:24 ∞ 2 φ(α) = pn n=1 φ(n) (β)H (α, β) dβ. 18) 0 For the PFR and the CSTR, H (α, β) has particularly simple forms: Hpfr (α, β) = δ(β − α) 21 22 23 24 and Hcstr (α, β) = E cstr (β). , non-equal-volume mixing), the IEM model yields poor predictions. , the E-model of Baldyga and Bourne (1989)) that account for the evolution of p1 should be employed to model non-equal-volume mixing. In Chapter 6, this is shown to be a general physical requirement for all micromixing models, resulting from the fact that molecular diffusion in a closed system conserves mass.

The velocity was extracted from DNS of isotropic turbulence (Rλ = 140) with U = 0. (Courtesy of P. K. ) Unlike simple random variables that have no space or time dependence, the statistics of the random velocity field in homogeneous turbulence can be described at many different levels of complexity. 3 However, from a CFD modeling perspective, such a theory would be of little practical use. Thus, we will consider only one-point and two-point formulations that describe a homogeneous turbulent flow by the velocity statistics at one or two fixed points in space and/or time.

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