Computational Models for Turbulent Reacting Flows by Rodney O. Fox

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By Rodney O. Fox

This survey of the present cutting-edge in computational types for turbulent reacting flows conscientiously analyzes the strengths and weaknesses of a few of the thoughts defined. Rodney Fox specializes in the formula of sensible versions instead of numerical concerns coming up from their answer. He develops a theoretical framework in keeping with the one-point, one-time joint chance density functionality (PDF). The learn unearths that every one typically hired types for turbulent reacting flows will be formulated when it comes to the joint PDF of the chemical species and enthalpy.

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Instead, the closure problem is moved to the so-called conditional acceleration and diffusion terms that arise in the one-point PDF description. At present, truly general predictive models for these terms have yet to be formulated and thoroughly validated. Nevertheless, existing models can be used successfully for complex reacting-flow calculations, and are thus discussed in some detail. The numerical methods needed to implement transported PDF simulations are introduced in Chapter 7. These differ from ‘standard’ CFD-based models by their use of Monte-Carlo simulations to represent the flow, and thus require special attention to potential numerical and statistical errors.

1. Expressed in terms of the turbulence Reynolds number Re L = k 2 /εν. 3/4 For example, L u = Re L η. 3. 1. Expressed in terms of the turbulence Reynolds number Re L = k 2 /εν. 1/2 For example, τu = Re L τη . 41) −∞ +∞ Ri j (r, t) = i j (κ, t) eiκ·r dκ. 42) −∞ This relation shows that for homogeneous turbulence, working in terms of the two-point spatial correlation function or in terms of the velocity spectrum tensor is entirely equivalent. In the turbulence literature, models formulated in terms of the velocity spectrum tensor are referred to as spectral models (for further details, see McComb (1990) or Lesieur (1997)).

10) The conditional PDF can be employed to compute conditional expected values. For example, the conditional mean of U1 given U2 (x, t) = V2 and U3 (x, t) = V3 is defined by U1 (x, t)|V2 , V3 ≡ U1 (x, t)|U2 (x, t) = V2 , U3 (x, t) = V3 ≡ +∞ −∞ V1 fU1 |U2 ,U3 (V1 |V2 , V3 ; x, t) dV1 . 11) The unconditional mean can be found from the conditional mean by averaging with respect to the joint PDF of U2 and U3 : +∞ U1 (x, t) = U1 |V2 , V3 fU2 ,U3 (V2 , V3 ; x, t) dV2 dV3 . 12) −∞ Likewise, the conditional variance of U1 (x, t) given U2 (x, t) = V2 and U3 (x, t) = V3 is defined by (U1 − U1 |V2 , V3 )2 |V2 , V3 ≡ +∞ −∞ (V1 − U1 |V2 , V3 )2 fU1 |U2 ,U3 (V1 |V2 , V3 ; x, t) dV1 .

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