ABSTRACT
An extended quadrature method of moments using the kernel density function (-EQMOM) is used to approximate solutions to the evolution equation for univariate and bivariate composition probability distribution functions (PDFs) of a passive scalar for binary and ternary mixing. The key element of interest is the molecular mixing term, which is described using the Fokker–Planck (FP) molecular mixing model. The direct numerical simulations (DNSs) of Eswaran and Pope [“Direct numerical simulations of the turbulent mixing of a passive scalar,” Phys. Fluids 31, 506 (1988)] and the amplitude mapping closure (AMC) of Pope [“Mapping closures for turbulent mixing and reaction,” Theor. Comput. Fluid Dyn. 2, 255 (1991)] are taken as reference solutions to establish the accuracy of the FP model in the case of binary mixing. The DNSs of Juneja and Pope [“A DNS study of turbulent mixing of two passive scalars,” Phys. Fluids 8, 2161 (1996)] are used to validate the results obtained for ternary mixing. Simulations are performed with both the conditional scalar dissipation rate (CSDR) proposed by Fox [Computational Methods for Turbulent Reacting Flows (Cambridge University Press, 2003)] and the CSDR from AMC, with the scalar dissipation rate provided as input and obtained from the DNS. Using scalar moments up to fourth order, the ability of the FP model to capture the evolution of the shape of the PDF, important in turbulent mixing problems, is demonstrated. Compared to the widely used assumed -PDF model [S. S. Girimaji, “Assumed β-pdf model for turbulent mixing: Validation and extension to multiple scalar mixing,” Combust. Sci. Technol. 78, 177 (1991)], the -EQMOM solution to the FP model more accurately describes the initial mixing process with a relatively small increase in computational cost.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the support of the US National Science Foundation under the SI2–SSE Award No. NSF–ACI 1440443.
- 1. S. B. Pope, Turbulent Flows (Cambridge University Press, 2000). Google ScholarCrossref
- 2. R. O. Fox, Computational Models for Turbulent Reacting Flows (Cambridge University Press, 2003). Google ScholarCrossref
- 3. J. E. Broadwell and R. E. Breidenthal, “A simple model of mixing and chemical reaction in a turbulent shear layer,” J. Fluid Mech. 125, 397 (1982). https://doi.org/10.1017/s0022112082003401, Google ScholarCrossref
- 4. M. M. Koochesfahani and P. E. Dimotakis, “Mixing and chemical reactions in a turbulent liquid mixing layer,” J. Fluid Mech. 170, 83 (1986). https://doi.org/10.1017/s0022112086000812, Google ScholarCrossref
- 5. M. G. Mungal and P. E. Dimotakis, “Mixing and combustion with low heat release in a turbulent shear layer,” J. Fluid Mech. 148, 349 (1984). https://doi.org/10.1017/s002211208400238x, Google ScholarCrossref
- 6. S. S. Girimaji, “Assumed β-pdf model for turbulent mixing: Validation and extension to multiple scalar mixing,” Combust. Sci. Technol. 78, 177 (1991). https://doi.org/10.1080/00102209108951748, Google ScholarCrossref, ISI
- 7. M. A. Cremer, P. A. McMurtry, and A. R. Kerstein, “Effects of turbulence length–scale distribution on scalar mixing in homogeneous turbulent flow,” Phys. Fluids 6, 2143 (1994). https://doi.org/10.1063/1.868439, Google ScholarScitation
- 8. P. A. McMurtry and P. Givi, “Direct numerical simulations of mixing and reaction in a nonpremixed homogeneous turbulent flow,” Combust. Flame 77, 171 (1989). https://doi.org/10.1016/0010-2180(89)90035-7, Google ScholarCrossref, ISI
- 9. V. Eswaran and S. B. Pope, “Direct numerical simulations of the turbulent mixing of a passive scalar,” Phys. Fluids 31, 506 (1988). https://doi.org/10.1063/1.866832, Google ScholarScitation, ISI
- 10. E. E. O’Brien and T.-L. Jiang, “The conditional dissipation rate of an initially binary scalar in homogeneous turbulence,” Phys. Fluids A 3, 3121 (1991). https://doi.org/10.1063/1.858127, Google ScholarScitation, ISI
- 11. M. R. Overholt and S. B. Pope, “Direct numerical simulation of a passive scalar with imposed mean gradient in isotropic turbulence,” Phys. Fluids 8, 3128 (1996). https://doi.org/10.1063/1.869099, Google ScholarScitation, ISI
- 12. P. K. Yeung, D. A. Donzis, and K. R. Sreenivasan, “High-Reynolds-number simulation of turbulent mixing,” Phys. Fluids 17, 081703 (2005). https://doi.org/10.1063/1.2001690, Google ScholarScitation
- 13. P. K. Yeung, S. Xu, and K. R. Sreenivasan, “Schmidt number effects on turbulent transport with uniform mean scalar gradient,” Phys. Fluids 14, 4178 (2002). https://doi.org/10.1063/1.1517298, Google ScholarScitation, ISI
- 14. H. Pitsch, “Large-eddy simulation of turbulent combustion,” Annu. Rev. Fluid Mech. 38, 453 (2006). https://doi.org/10.1146/annurev.fluid.38.050304.092133, Google ScholarCrossref
- 15. P. P. Popov and S. B. Pope, “Large eddy simulation/probability density function simulations of bluff body stabilized flames,” Combust. Flame 161, 3100 (2014). https://doi.org/10.1016/j.combustflame.2014.05.018, Google ScholarCrossref
- 16. P. P. Popov, H. Wang, and S. B. Pope, “Specific volume coupling and convergence properties in hybrid particle/finite volume algorithms for turbulent reactive flows,” J. Comput. Phys. 294, 110 (2015). https://doi.org/10.1016/j.jcp.2015.03.001, Google ScholarCrossref
- 17. D. W. Meyer and P. Jenny, “A mixing model for turbulent flows based on parameterized scalar profiles,” Phys. Fluids 18, 035105 (2006). https://doi.org/10.1063/1.2182005, Google ScholarScitation, ISI
- 18. D. W. Meyer, “A new particle interaction mixing model for turbulent dispersion and turbulent reactive flows,” Phys. Fluids 22, 035103 (2010). https://doi.org/10.1063/1.3327288, Google ScholarScitation, ISI
- 19. L. Valino, “A field Monte Carlo formulation for calculating the probability density function of a single scalar in a turbulent flow,” Flow, Turbul. Combust. 60, 157 (1998). https://doi.org/10.1023/a:1009968902446, Google ScholarCrossref
- 20. V. Sabelnikov and O. Soulard, “Rapidly decorrelating velocity-field model as a tool for solving one-point Fokker-Planck equations for probability density functions of turbulent reactive scalars,” Phys. Rev. E 72, 016301 (2005). https://doi.org/10.1103/physreve.72.016301, Google ScholarCrossref
- 21. W. P. Jones, S. Navarro-Martinez, and O. Rhl, “Large eddy simulation of hydrogen auto-ignition with a probability density function method,” Proc. Combust. Inst. 31, 1765 (2007). https://doi.org/10.1016/j.proci.2006.07.041, Google ScholarCrossref
- 22. W. P. Jones and S. Navarro-Martinez, “Large eddy simulation of autoignition with a subgrid probability density function method,” Combust. Flame 150, 170 (2007). https://doi.org/10.1016/j.combustflame.2007.04.003, Google ScholarCrossref
- 23. W. P. Jones and S. Navarro-Martinez, “Numerical study of n-heptane auto-ignition using LES-PDF methods,” Flow, Turbul. Combust. 83, 407 (2009). https://doi.org/10.1007/s10494-009-9228-9, Google ScholarCrossref
- 24. A. Garmory, E. S. Richardson, and E. Mastorakos, “Micromixing effects in a reacting plume by the stochastic fields method,” Atmos. Environ. 40, 1078 (2006). https://doi.org/10.1016/j.atmosenv.2005.11.002, Google ScholarCrossref
- 25. A. Garmory and E. Mastorakos, “Aerosol nucleation and growth in a turbulent jet using the stochastic fields method,” Chem. Eng. Sci. 63, 4078 (2008). https://doi.org/10.1016/j.ces.2008.05.012, Google ScholarCrossref
- 26. A. Garmory, I. S. Kim, R. E. Britter, and E. Mastorakos, “Simulations of the dispersion of reactive pollutants in a street canyon, considering different chemical mechanisms and micromixing,” Atmos. Environ. 43, 4670 (2009). https://doi.org/10.1016/j.atmosenv.2008.07.033, Google ScholarCrossref
- 27. V. Sabelnikov and O. Soulard, “White in time scalar advection model as a tool for solving joint composition PDF equations,” Flow, Turbul. Combust. 77, 333 (2006). https://doi.org/10.1007/s10494-006-9049-z, Google ScholarCrossref
- 28. R. O. Fox, “The Fokker–Planck closure for turbulent molecular mixing: Passive scalars,” Phys. Fluids 4, 1230 (1992). https://doi.org/10.1063/1.858241, Google ScholarScitation
- 29. R. O. Fox, “Improved Fokker–Planck model for the joint scalar, scalar gradient PDF,” Phys. Fluids 6, 334 (1994). https://doi.org/10.1063/1.868088, Google ScholarScitation
- 30. A. D. Fokker, “Die mittlere energie rotierender elektrischer dipole im strahlungsfeld,” Ann. Phys. 348, 810 (1914). https://doi.org/10.1002/andp.19143480507, Google ScholarCrossref
- 31. H. Risken, “The Fokker-Planck equation,” in Springer Series in Synergetics (Springer Berlin Heidelberg, Berlin, Heidelberg, 1989), Vol. 18. Google ScholarCrossref
- 32. A. Kolmogoroff, “Über die analytischen methoden in der wahrscheinlichkeitsrechnung,” Math. Ann. 104, 415 (1931). https://doi.org/10.1007/bf01457949, Google ScholarCrossref
- 33. M. V. Smoluchowski, Physikalische Zeitschrift, edited by H. T. Simon and P. Debye (S. Hirzel, Leipzig, 1916), Vol. 17, pp. 557–585. Google Scholar
- 34. B. F. Spencer and L. A. Bergman, “On the numerical solution of the Fokker-Planck equation for nonlinear stochastic systems,” Nonlinear Dyn. 4, 357 (1993). https://doi.org/10.1007/bf00120671, Google ScholarCrossref
- 35. P. Kumar and S. Narayanan, “Solution of Fokker-Planck equation by finite element and finite difference methods for nonlinear systems,” Sadhana 31, 445–461 (2006). https://doi.org/10.1007/bf02716786, Google ScholarCrossref
- 36. D. L. Marchisio and R. O. Fox, Computational Models for Polydisperse Particulate and Multiphase Systems (Cambridge University Press, 2013). Google ScholarCrossref
- 37. C. Yuan, F. Laurent, and R. O. Fox, “An extended quadrature method of moments for population balance equations,” J. Aerosol Sci. 51, 1 (2012). https://doi.org/10.1016/j.jaerosci.2012.04.003, Google ScholarCrossref
- 38. R. McGraw, “Description of aerosol dynamics by the quadrature method of moments,” Aerosol Sci. Technol. 27, 255 (1997). https://doi.org/10.1080/02786829708965471, Google ScholarCrossref
- 39. D. L. Marchisio, R. Vigil, and R. O. Fox, “Quadrature method of moments for aggregation–breakage processes,” J. Colloid Interface Sci. 258, 322 (2003). https://doi.org/10.1016/s0021-9797(02)00054-1, Google ScholarCrossref
- 40. D. L. Marchisio and R. O. Fox, “Solution of population balance equations using the direct quadrature method of moments,” J. Aerosol Sci. 36, 43 (2005). https://doi.org/10.1016/j.jaerosci.2004.07.009, Google ScholarCrossref
- 41. R. O. Fox, “On the relationship between Lagrangian micromixing models and computational fluid dynamics,” Chem. Eng. Process. 37, 521 (1998). https://doi.org/10.1016/s0255-2701(98)00059-2, Google ScholarCrossref
- 42. L. Wang and R. O. Fox, “Comparison of micromixing models for CFD simulation of nanoparticle formation,” AIChE J. 50, 2217 (2004). https://doi.org/10.1002/aic.10173, Google ScholarCrossref
- 43. V. Vikas, Z. J. Wang, A. Passalacqua, and R. O. Fox, “Realizable high-order finite-volume schemes for quadrature-based moment methods,” J. Comput. Phys. 230, 5328 (2011). https://doi.org/10.1016/j.jcp.2011.03.038, Google ScholarCrossref
- 44. V. Vikas, Z. J. Wang, and R. O. Fox, “Realizable high-order finite-volume schemes for quadrature-based moment methods applied to diffusion population balance equations,” J. Comput. Phys. 249, 162 (2013). https://doi.org/10.1016/j.jcp.2013.05.002, Google ScholarCrossref
- 45. A. Juneja and S. B. Pope, “A DNS study of turbulent mixing of two passive scalars,” Phys. Fluids 8, 2161 (1996). https://doi.org/10.1063/1.868990, Google ScholarScitation, ISI
- 46. S. B. Pope, “Mapping closures for turbulent mixing and reaction,” Theor. Comput. Fluid Dyn. 2, 255 (1991). https://doi.org/10.1007/bf00271466, Google ScholarCrossref
- 47. J. Villermaux and L. Falk, “A generalized mixing model for initial contacting of reactive fluids,” Chem. Eng. Sci. 49, 5127 (1994). https://doi.org/10.1016/0009-2509(94)00303-3, Google ScholarCrossref
- 48. C. Dopazo and E. E. O’Brien, “Isochoric turbulent mixing of two rapidly reacting chemical species with chemical heat release,” Phys. Fluids 16, 2075 (1973). https://doi.org/10.1063/1.1694268, Google ScholarScitation, ISI
- 49. R. O. Fox, “The Lagrangian spectral relaxation model for differential diffusion in homogeneous turbulence,” Phys. Fluids 11, 1550 (1999). https://doi.org/10.1063/1.870018, Google ScholarScitation, ISI
- 50. E. S. Richardson and J. H. Chen, “Application of PDF mixing models to premixed flames with differential diffusion,” Combust. Flame 159, 2398 (2012). https://doi.org/10.1016/j.combustflame.2012.02.026, Google ScholarCrossref
- 51. E. Madadi-Kandjani and A. Passalacqua, “An extended quadrature-based moment method with log-normal kernel density functions,” Chem. Eng. Sci. 131, 323 (2015). https://doi.org/10.1016/j.ces.2015.04.005, Google ScholarCrossref
- 52. C. Chalons, R. O. Fox, and M. Massot, in Studying Turbulence Using Numerical Simulation Databases, Center for Turbulence Research, Summer Program 2010 (Stanford University, 2010), p. 347. Google Scholar
- 53. T. T. Nguyen, F. Laurent, R. O. Fox, and M. Massot, “Solution of population balance equations in applications with fine particles: Mathematical modeling and numerical schemes,” J. Comput. Phys. 325, 129 (2016). https://doi.org/10.1016/j.jcp.2016.08.017, Google ScholarCrossref
- 54. C. K. Madnia, S. H. Frankel, and P. Givi, “Direct numerical simulations of the unmixedness in a homogeneous reacting turbulent flow,” Chem. Eng. Commun. 109, 19 (1991). https://doi.org/10.1080/00986449108910971, Google ScholarCrossref
- 55. S. H. Frankel, C. K. Madnia, and P. Givi, “Modeling of the reactant conversion rate in a turbulent shear flow,” Chem. Eng. Commun. 113, 197 (1992). https://doi.org/10.1080/00986449208936012, Google ScholarCrossref
- 56. X. Hu, A. Passalacqua, and R. O. Fox, “Application of quadrature-based uncertainty quantification to the NETL small-scale challenge problem SSCP-I,” Powder Technol. 272, 100 (2015). https://doi.org/10.1016/j.powtec.2014.11.030, Google ScholarCrossref
- 57. R. H. Kraichnan, “Closures for probability distributions,” Bull. Am. Phys. Soc. 34, 2298 (1989). Google Scholar
- 58. R. H. Kraichnan, “Models of intermittency in hydrodynamic turbulence,” Phys. Rev. Lett. 65, 575 (1990). https://doi.org/10.1103/physrevlett.65.575, Google ScholarCrossref
- 59. H. Chen, S. Chen, and R. H. Kraichnan, “Probability distribution of a stochastically advected scalar field,” Phys. Rev. Lett. 63, 2657 (1989). https://doi.org/10.1103/physrevlett.63.2657, Google ScholarCrossref
- 60. B. Sundaram, A. Y. Klimenko, M. J. Cleary, and Y. Ge, “A direct approach to generalised multiple mapping conditioning for selected turbulent diffusion flame cases,” Combust. Theory Modell. 20, 735 (2016). https://doi.org/10.1080/13647830.2016.1174308, Google ScholarCrossref
- 61. J. C. Cheng and R. O. Fox, “Kinetic modeling of nanoprecipitation using CFD coupled with a population balance,” Ind. Eng. Chem. Res. 49, 10651 (2010). https://doi.org/10.1021/ie100558n, Google ScholarCrossref
- 62. C. Yuan and R. O. Fox, “Conditional quadrature method of moments for kinetic equations,” J. Comput. Phys. 230, 8216 (2011). https://doi.org/10.1016/j.jcp.2011.07.020, Google ScholarCrossref
- 63. D. L. Wright, Jr., “Numerical advection of moments of the particle size distribution in Eulerian models,” J. Aerosol Sci. 38, 352 (2007). https://doi.org/10.1016/j.jaerosci.2006.11.011, Google ScholarCrossref
- 64. B. Perthame, “Second-order Boltzmann schemes for compressible Euler equations in one and two space dimensions,” SIAM J. Numer. Anal. 29, 1 (1992). https://doi.org/10.1137/0729001, Google ScholarCrossref
- 65. O. Desjardins, R. Fox, and P. Villedieu, “A quadrature-based moment method for dilute fluid-particle flows,” J. Comput. Phys. 227, 2514 (2008). https://doi.org/10.1016/j.jcp.2007.10.026, Google ScholarCrossref
- 66. R. Fox, “A quadrature-based third-order moment method for dilute gas-particle flows,” J. Comput. Phys. 227, 6313 (2008). https://doi.org/10.1016/j.jcp.2008.03.014, Google ScholarCrossref
- 67. F. Laurent and T. T. Nguyen, “Realizable second-order finite-volume schemes for the advection of moment sets of the particle size distribution,” J. Comput. Phys. 337, 309 (2017). https://doi.org/10.1016/j.jcp.2017.02.046, Google ScholarCrossref
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