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Two-Fluid Model CFD

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Abstract

In this chapter we first address TFM CFD simulations for several cases of bubbly flow that are of engineering interest. We then consider the application of the two-phase 1D TFM stability results of previous chapters to TFM CFD, specifically to the numerical convergence of an ill-posed model.

Multidimensional TFM CFD usually implies turbulence modeling and this chapter addresses both a stable Reynolds Averaged (RANS) TFM and an unstable RANS (URANS) model.

The first topic is the derivation of a k–ε model for a RANS TFM and several applications are discussed. Next a special near-wall TFM averaging is performed to match the two-phase logarithmic law of the wall of Marie et al. (International Journal of Multiphase Flow, 23: 227–247, 1997) to the two-phase k–ε model so that convergence is preserved. Finally the simulation of a chaotic bubble plume with a URANS model is considered. A subscale Smagorinsky model stabilizes the nonlinear small scale turbulence but is not sufficient for linear stability of the model. Additional interfacial forces are needed and the stabilizing effect of the interfacial pressure difference and the collision force is investigated, following the analysis of Chap. 5. These forces prevent ill-posed short wavelength oscillations and allow convergence of the numerical model. The convergence of the turbulence spectrum of the well-posed model is demonstrated using the statistical approach of Chap. 4 for chaotic flow.

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Authors and Affiliations

  1. School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA

    Martín López de Bertodano, William Fullmer & Victor H. Ransom

  2. School of Exact Sciences, University of Central Buenos Aires & CONICET, National Atomic Energy Commission, Buenos Aires, Argentina

    Alejandro Clausse

Authors
  1. Martín López de Bertodano
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  2. William Fullmer
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  3. Alejandro Clausse
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  4. Victor H. Ransom
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de Bertodano, M.L., Fullmer, W., Clausse, A., Ransom, V.H. (2017). Two-Fluid Model CFD. In: Two-Fluid Model Stability, Simulation and Chaos. Springer, Cham. https://doi.org/10.1007/978-3-319-44968-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-44968-5_9

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  • Online ISBN: 978-3-319-44968-5

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