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Multiscale fluid–particle thermal interaction in isotropic turbulence

Published online by Cambridge University Press:  25 October 2019

M. Carbone
Affiliation:
Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
A. D. Bragg*
Affiliation:
Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
M. Iovieno
Affiliation:
Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
*
Email address for correspondence: andrew.bragg@duke.edu

Abstract

We use direct numerical simulations to investigate the interaction between the temperature field of a fluid and the temperature of small particles suspended in the flow, employing both one- and two-way thermal coupling, in a statistically stationary, isotropic turbulent flow. Using statistical analysis, we investigate this variegated interaction at the different scales of the flow. We find that the variance of the carrier flow temperature gradients decreases as the thermal response time of the suspended particles is increased. The probability density function (PDF) of the carrier flow temperature gradients scales with its variance, while the PDF of the rate of change of the particle temperature, whose variance is associated with the thermal dissipation due to the particles, does not scale in such a self-similar way. The modification of the fluid temperature field due to the particles is examined by computing the particle concentration and particle heat fluxes conditioned on the magnitude of the local fluid temperature gradient. These statistics highlight that the particles cluster on the fluid temperature fronts, and the important role played by the alignments of the particle velocity and the local fluid temperature gradient. The temperature structure functions, which characterize the temperature fluctuations across the scales of the flow, clearly show that the fluctuations of the carrier flow temperature increments are monotonically suppressed in the two-way coupled regime as the particle thermal response time is increased. Thermal caustics dominate the particle temperature increments at small scales, that is, particles that come into contact are likely to have very large differences in their temperatures. This is caused by the non-local thermal dynamics of the particles: the scaling exponents of the inertial particle temperature structure functions in the dissipation range reveal very strong multifractal behaviour. Further insight is provided by the flux of temperature increments across the scales. Altogether, these results reveal a number of non-trivial effects, with a number of important practical consequences.

Type
JFM Papers
Copyright
© 2019 Cambridge University Press 

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References

Bec, J., Biferale, L., Cencini, M., Lanotte, A., Musacchio, S. & Toschi, F. 2007 Heavy particle concentration in turbulence at dissipative and inertial scales. Phys. Rev. Lett. 98, 084502.Google Scholar
Bec, J., Homann, H. & Krstulovic, G. 2014 Clustering, fronts, and heat transfer in turbulent suspensions of heavy particles. Phys. Rev. Lett. 112, 234503.Google Scholar
Beylkin, G. 1995 On the fast fourier transform of functions with singularities. Appl. Comput. Harmon. Anal. 2 (4), 363381.Google Scholar
Boivin, M., Simonin, O. & Squires, K. D. 1998 Direct numerical simulation of turbulence modulation by particles in isotropic turbulence. J. Fluid Mech. 375, 235263.Google Scholar
Botto, L. & Prosperetti, A. 2012 A fully resolved numerical simulation of turbulent flow past one or several spherical particles. Phys. Fluids 24 (1), 013303.Google Scholar
Bragg, A. D. & Collins, L. R. 2014a New insights from comparing statistical theories for inertial particles in turbulence: I. Spatial distribution of particles. New J. Phys. 16, 055013.Google Scholar
Bragg, A. D. & Collins, L. R. 2014b New insights from comparing statistical theories for inertial particles in turbulence: II. Relative velocities of particles. New J. Phys. 16, 055014.Google Scholar
Bragg, A. D., Ireland, P. J. & Collins, L. R. 2015a Mechanisms for the clustering of inertial particles in the inertial range of isotropic turbulence. Phys. Rev. E 92, 023029.Google Scholar
Bragg, A. D., Ireland, P. J. & Collins, L. R. 2015b On the relationship between the non-local clustering mechanism and preferential concentration. J. Fluid Mech. 780, 327343.Google Scholar
Canuto, C., Hussaini, M. Y., Quarteroni, A. & Zang, T. A. 1988 Spectral Methods in Fluid Mechanics. Springer.Google Scholar
Carbone, M. & Iovieno, M. 2018 Application of the non-uniform fast Fourier transform to the direct numerical simulation of two-way coupled turbulent flows. WIT Trans. Engng Sci. 120, 237248.Google Scholar
Celani, A., Lanotte, A., Mazzino, A. & Vergassola, M. 2000 Universality and saturation of intermittency in passive scalar turbulence. Phys. Rev. Lett. 84, 23852388.Google Scholar
Chun, J., Koch, D. L., Rani, S., Ahluwalia, A. & Collins, L. R. 2005 Clustering of aerosol particles in isotropic turbulence. J. Fluid Mech. 536, 219251.Google Scholar
Das, S. K., Choi, S. U. S. & Patel, H. E. 2006 Heat transfer in nanofluids – a review. Heat Transfer Engng 27 (10), 319.Google Scholar
De Lillo, F., Cencini, M., Durham, W. M., Barry, M., Stocker, R., Climent, E. & Boffetta, G. 2014 Turbulent fluid acceleration generates clusters of gyrotactic microorganisms. Phys. Rev. Lett. 112, 044502.Google Scholar
Dhariwal, R. & Rani, S. L. 2018 Effects of deterministic and stochastic forcing schemes on the relative motion of inertial particles in DNS of isotropic turbulence. Powder Technol. 339, 4669.Google Scholar
Elghobashi, S. 1991 Particle-laden turbulent flows: direct simulation and closure models. Appl. Sci. Res. 48 (3), 301314.Google Scholar
Elghobashi, S. 1994 On predicting particle-laden turbulent flows. Appl. Sci. Res. 52 (4), 309329.Google Scholar
Gotoh, T. & Watanabe, T. 2015 Power and nonpower laws of passive scalar moments convected by isotropic turbulence. Phys. Rev. Lett. 115, 114502.Google Scholar
Götzfried, P., Kumar, B., Shaw, R. A. & Schumacher, J. 2017 Droplet dynamics and fine-scale structure in a shearless turbulent mixing layer with phase changes. J. Fluid Mech. 814, 452483.Google Scholar
Grabowski, W. W. & Wang, L. P. 2013 Growth of cloud droplets in a turbulent environment. Annu. Rev. Fluid Mech. 45 (1), 293324.Google Scholar
Gualtieri, P., Picano, F., Sardina, G. & Casciola, C. M. 2013 Clustering and turbulence modulation in particle-laden shear flows. J. Fluid Mech. 715, 134162.Google Scholar
Gualtieri, P., Picano, F., Sardina, G. & Casciola, C. M. 2015 Exact regularized point particle method for multiphase flows in the two-way coupling regime. J. Fluid Mech. 773, 520561.Google Scholar
Gustavsson, K. & Mehlig, B. 2011 Ergodic and non-ergodic clustering of inertial particles. Eur. Phys. Lett. 96, 60012.Google Scholar
Gustavsson, K. & Mehlig, B. 2016 Statistical models for spatial patterns of heavy particles in turbulence. Adv. Phys. 65 (1), 157.Google Scholar
van Hinsberg, M. A. T., ten Thije Boonkkamp, J. H. M., Toschi, F. & Clercx, H. J. H. 2012 On the efficiency and accuracy of interpolation methods for spectral codes. SIAM J. Sci. Comput. 34 (4), B479B498.Google Scholar
Hochbruck, M. & Ostermann, A. 2010 Exponential integrators. Acta Numerica 19, 209286.Google Scholar
Holzer, M. & Siggia, E. D. 1994 Turbulent mixing of a passive scalar. Phys. Fluids 6 (5), 18201837.Google Scholar
Horwitz, J. A. K. & Mani, A. 2016 Accurate calculation of Stokes drag for point-particle tracking in two-way coupled flows. J. Comput. Phys. 318, 85109.Google Scholar
Ireland, P. J., Bragg, A. D. & Collins, L. R. 2016a The effect of Reynolds number on inertial particle dynamics in isotropic turbulence. Part 1. Simulations without gravitational effects. J. Fluid Mech. 796, 617658.Google Scholar
Ireland, P. J., Bragg, A. D. & Collins, L. R. 2016b The effect of Reynolds number on inertial particle dynamics in isotropic turbulence. Part 2. Simulations with gravitational effects. J. Fluid Mech. 796, 659711.Google Scholar
Kraichnan, R. H. 1994 Anomalous scaling of a randomly advected passive scalar. Phys. Rev. Lett. 72, 10161019.Google Scholar
Kuerten, J. G. M. 2016 Point-particle DNS and LES of particle-laden turbulent flow – a state-of-the-art review. Flow Turbul. Combust. 97 (3), 689713.Google Scholar
Kuerten, J. G. M., van der Geld, C. W. M. & Geurts, B. J. 2011 Turbulence modification and heat transfer enhancement by inertial particles in turbulent channel flow. Phys. Fluids 23 (12), 123301.Google Scholar
Kumar, B., Schumacher, J. & Shaw, R. A. 2013 Cloud microphysical effects of turbulent mixing and entrainment. Theor. Comput. Fluid Dyn. 27, 361376.Google Scholar
Kumar, B., Schumacher, J. & Shaw, R. A. 2014 Lagrangian mixing dynamics at the cloudy-clear air interface. J. Atmos. Sci. 71 (7), 25642580.Google Scholar
Maxey, M. R. 1987 The gravitational settling of aerosol particles in homogeneous turbulence and random flow fields. J. Fluid Mech. 174, 441465.Google Scholar
Maxey, M. R. & Riley, J. 1983 Equation of motion for a small rigid sphere in a nonuniform flow. Phys. Fluids 26 (4), 883889.Google Scholar
Momenifar, M. R., Akhavan-Behabadi, M. A., Nasr, M. & Hanafizadeh, P. 2015 Effect of lubricating oil on flow boiling characteristics of R-600a/oil inside a horizontal smooth tube. Appl. Therm. Engng 91, 6272.Google Scholar
Onishi, R., Takahashi, K. & Vassilicos, J. C. 2013 An efficient parallel simulation of interacting inertial particles in homogeneous isotropic turbulence. J. Comput. Phys. 242, 809827.Google Scholar
Overholt, M. R. & Pope, S. B. 1996 Direct numerical simulation of a passive scalar with imposed mean gradient in isotropic turbulence. Phys. Fluids 8 (11), 31283148.Google Scholar
Pekurovsky, D. 2012 P3DFFT: a framework for parallel computations of Fourier transforms in three dimensions. SIAM J. Sci. Comput. 34 (4), C192C209.Google Scholar
Prasher, R., Evans, W., Meakin, P., Fish, J., Phelan, P. & Keblinski, P. 2006 Effect of aggregation on thermal conduction in colloidal nanofluids. Appl. Phys. Lett. 89 (14), 143119.Google Scholar
Pruppacher, H. R. & Klett, J. D. 2010 Microphysics of Clouds and Precipitation. Springer Netherlands.Google Scholar
Salazar, J. P. L. C. & Collins, L. R. 2012 Inertial particle relative velocity statistics in homogeneous isotropic turbulence. J. Fluid Mech. 696, 4566.Google Scholar
Shraiman, B. I. & Siggia, E. D. 2000 Scalar turbulence. Nature 405, 639646.Google Scholar
Sreenivasan, K. R. 1996 The passive scalar spectrum and the Obukhov–Corrsin constant. Phys. Fluids 8 (1), 189196.Google Scholar
Sundaram, S. & Collins, L. R. 1996 Numerical considerations in simulating a turbulent suspension of finite-volume particles. J. Comput. Phys. 124 (2), 337350.Google Scholar
Takeuchi, T. & Lin, D. N. C. 2002 Radial flow of dust particles in accretion disks. Astrophys. J. 581 (2), 1344.Google Scholar
Taylor, G. I. 1922 Diffusion by continuous movements. Proc. Lond. Math. Soc. s2–20 (1), 196212.Google Scholar
Toschi, F. & Bodenschatz, E. 2009 Lagrangian properties of particles in turbulence. Annu. Rev. Fluid Mech. 41 (1), 375404.Google Scholar
Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., Hazlewood, V., Lathrop, S., Lifka, D., Peterson, G. D. et al. 2014 Xsede: accelerating scientific discovery. Comput. Sci. Engng 16 (5), 6274.Google Scholar
Voßkuhle, M., Pumir, A., Lévêque, E. & Wilkinson, M. 2014 Prevalence of the sling effect for enhancing collision rates in turbulent suspensions. J. Fluid Mech. 749, 841852.Google Scholar
Vreman, A. W. 2016 Particle-resolved direct numerical simulation of homogeneous isotropic turbulence modified by small fixed spheres. J. Fluid Mech. 796, 4085.Google Scholar
Wang, L. P. & Maxey, M. R. 1993 Settling velocity and concentration distribution of heavy particles in homogeneous isotropic turbulence. J. Fluid Mech. 256, 2768.Google Scholar
Warhaft, Z. 2000 Passive scalars in turbulent flows. Annu. Rev. Fluid Mech. 32 (1), 203240.Google Scholar
Watanabe, T. & Gotoh, T. 2004 Statistics of a passive scalar in homogeneous turbulence. New J. Phys. 6 (1), 40.Google Scholar
Wilkinson, M. & Mehlig, B. 2005 Caustics in turbulent aerosols. Europhys. Lett. 71 (2), 186192.Google Scholar
Zaichik, L., Alipchenkov, V. M. & Sinaiski, E. G. 2009 Particles in Turbulent Flows, vol. 84. Wiley.Google Scholar
Zamansky, R., Coletti, F., Massot, M. & Mani, A. 2014 Radiation induces turbulence in particle-laden fluids. Phys. Fluids 26 (7), 071701.Google Scholar
Zamansky, R., Coletti, F., Massot, M. & Mani, A. 2016 Turbulent thermal convection driven by heated inertial particles. J. Fluid Mech. 809, 390437.Google Scholar
Zonta, F., Marchioli, C. & Soldati, A. 2008 Direct numerical simulation of turbulent heat transfer modulation in micro-dispersed channel flow. Acta Mech. 195 (1–4), 305326.Google Scholar
Zorzetto, E., Bragg, A. D. & Katul, G. 2018 Extremes, intermittency, and time directionality of atmospheric turbulence at the crossover from production to inertial scales. Phys. Rev. Fluids 3, 094604.Google Scholar