Hostname: page-component-76fb5796d-vvkck Total loading time: 0 Render date: 2024-04-28T13:12:17.344Z Has data issue: false hasContentIssue false

Experimental measurement of spatio-temporally resolved energy dissipation rate in turbulent Rayleigh–Bénard convection

Published online by Cambridge University Press:  27 March 2024

Fang Xu
Affiliation:
Centre for Complex Flows and Soft Matter Research, Southern University of Science and Technology, Shenzhen 518055, PR China Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
Lu Zhang
Affiliation:
Centre for Complex Flows and Soft Matter Research, Southern University of Science and Technology, Shenzhen 518055, PR China Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
Ke-Qing Xia*
Affiliation:
Centre for Complex Flows and Soft Matter Research, Southern University of Science and Technology, Shenzhen 518055, PR China Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China Department of Physics, Southern University of Science and Technology, Shenzhen 518055, PR China
*
Email address for correspondence: xiakq@sustech.edu.cn

Abstract

We report a home-built velocity-gradient-tensor-resolved particle image velocimetry (VGTR-PIV) system which spatio-temporally resolves all components of the velocity gradient tensor. This technique is applied to the paradigmatic turbulent Rayleigh–Bénard convection system in a cylindrical cell at three representative positions, i.e. centre, side and bottom regions. The VGTR-PIV system allows us to directly measure, for the first time, the spatio-temporally resolved energy dissipation rate and enstrophy in turbulent thermal convection. In the experiment, the Rayleigh number $Ra$ varied in the range $2 \times 10^8 \leqslant Ra \leqslant 8 \times 10^9$ and the Prandtl number $Pr$ was fixed at $Pr = 4.34$. Compared with the fully resolved energy dissipation rate $\varepsilon$, the pseudo-dissipation provides the best estimate within $3\,\%$, the planar (two-dimensional) surrogate has a larger relative error and the one-dimensional surrogate leads to the largest error. The power-law scalings of the time-averaged energy dissipation rate with the Rayleigh number follow $\langle \varepsilon _c \rangle _t / (\nu ^3 H^{-4}) = 9.86 \times 10^{-6} Ra^{1.54 \pm 0.02}$, $\langle \varepsilon _s \rangle _t / (\nu ^3 H^{-4}) = 9.26 \times 10^{-3} Ra^{1.25 \pm 0.02}$ and $\langle \varepsilon _b \rangle _t / (\nu ^3 H^{-4}) = 2.70 \times 10^{-2} Ra^{1.23 \pm 0.02}$ in the centre, side and bottom regions, respectively where $\nu$ is dynamic viscosity and $H$ is cell height. These scaling relations, along with our earlier measured time-averaged energy dissipation rate at the bottom wall surface $\langle \varepsilon _w \rangle _t / (\nu ^3 H^{-4}) = 9.65 \times 10^{-2} Ra^{1.25 \pm 0.02}$ (J. Fluid Mech., vol. 947, 2022, A15), provide important constraints against which theoretical models may be tested. For the centre and side locations in the convection cell, the probability density functions (p.d.f.s) of the energy dissipation rate and enstrophy both follow a stretched exponential distribution. For the bottom region, the p.d.f.s of dissipation and enstrophy exhibit a stretched exponential distribution outside the viscous boundary layer and an exponential distribution inside the viscous boundary layer. It is also found that extreme events with high dissipation are the most intermittent in the side region, whereas the bottom region is less intermittent than the cell centre.

Type
JFM Papers
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adrian, R.J. & Yao, C.-S. 1985 Pulsed laser technique application to liquid and gaseous flows and the scattering power of seed materials. Appl. Opt. 24 (1), 4452.CrossRefGoogle ScholarPubMed
Ahlers, G., Grossmann, S. & Lohse, D. 2009 Heat transfer and large scale dynamics in turbulent Rayleigh–Bénard convection. Rev. Mod. Phys. 81 (2), 503537.CrossRefGoogle Scholar
Berberan-Santos, M.N., Bodunov, E.N. & Valeur, B. 2005 Mathematical functions for the analysis of luminescence decays with underlying distributions 1. Kohlrausch decay function (stretched exponential). Chem. Phys. 315 (1–2), 171182.CrossRefGoogle Scholar
Brown, E. & Ahlers, G. 2008 Azimuthal asymmetries of the large-scale circulation in turbulent Rayleigh–Bénard convection. Phys. Fluids 20 (10), 105105.CrossRefGoogle Scholar
Buaria, D., Pumir, A. & Bodenschatz, E. 2022 Generation of intense dissipation in high Reynolds number turbulence. Phil. Trans. R. Soc. Lond. A 380 (2218), 20210088.Google ScholarPubMed
Chertkov, M., Falkovich, G. & Kolokolov, I. 1998 Intermittent dissipation of a passive scalar in turbulence. Phys. Rev. Lett. 80 (10), 21212124.CrossRefGoogle Scholar
Chillà, F. & Schumacher, J. 2012 New perspectives in turbulent Rayleigh–Bénard convection. Eur. Phys. J. E 35, 58.CrossRefGoogle ScholarPubMed
Emran, M.S. & Schumacher, J. 2008 Fine-scale statistics of temperature and its derivatives in convective turbulence. J. Fluid Mech. 611, 1334.CrossRefGoogle Scholar
Frisch, U. 1995 Turbulence: The Legacy of A. N. Kolmogorov. Cambridge University Press.CrossRefGoogle Scholar
Gotoh, T. & Yang, J. 2022 Transition of fluctuations from Gaussian state to turbulent state. Phil. Trans. R. Soc. Lond. A 380 (2218), 20210097.Google ScholarPubMed
Grossmann, S. & Lohse, D. 2000 Scaling in thermal convection: a unifying theory. J. Fluid Mech. 407, 2756.CrossRefGoogle Scholar
Grossmann, S. & Lohse, D. 2001 Thermal convection for large Prandtl numbers. Phys. Rev. Lett. 86 (15), 33163319.CrossRefGoogle ScholarPubMed
Kaczorowski, M. & Xia, K.-Q. 2013 Turbulent flow in the bulk of Rayleigh–Bénard convection: small-scale properties in a cubic cell. J. Fluid Mech. 722, 596617.CrossRefGoogle Scholar
Kolmogorov, A.N. 1962 A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number. J. Fluid Mech. 13, 8285.CrossRefGoogle Scholar
Lam, S., Shang, X.-D., Zhou, S.-Q. & Xia, K.-Q. 2002 Prandtl number dependence of the viscous boundary layer and the Reynolds numbers in Rayleigh–Bénard convection. Phys. Rev. E 65 (6), 066306.CrossRefGoogle ScholarPubMed
Launder, B.E. & Spalding, D.B. 1974 The numerical computation of turbulent flows. Comput. Meth. Appl. Mech. Engng 3 (2), 269289.CrossRefGoogle Scholar
Li, X.-M., Huang, S.-D., Ni, R. & Xia, K.-Q. 2021 Lagrangian velocity and acceleration measurements in plume-rich regions of turbulent Rayleigh–Bénard convection. Phys. Rev. Fluids 6 (5), 053503.CrossRefGoogle Scholar
Lohse, D. & Xia, K.-Q. 2010 Small-scale properties of turbulent Rayleigh–Bénard convection. Annu. Rev. Fluid Mech. 42, 335364.CrossRefGoogle Scholar
Meneveau, C. & Sreenivasan, K.R. 1991 The multifractal nature of turbulent energy dissipation. J. Fluid Mech. 224, 429484.CrossRefGoogle Scholar
Ni, R., Huang, S.-D. & Xia, K.-Q. 2011 Local energy dissipation rate balances local heat flux in the center of turbulent thermal convection. Phys. Rev. Lett. 107 (17), 174503.CrossRefGoogle Scholar
Ni, R., Huang, S.-D. & Xia, K.-Q. 2012 Lagrangian acceleration measurements in convective thermal turbulence. J. Fluid Mech. 692, 395419.CrossRefGoogle Scholar
Pope, S.B. 2000 Turbulent Flows. Cambridge University Press.CrossRefGoogle Scholar
Raffel, M., Willert, C.E., Wereley, S.T. & Kompenhans, J. 2007 Particle Image Velocimetry. Springer.CrossRefGoogle Scholar
Richardson, L.F. 1922 Weather Prediction by Numerical Process. Cambridge University Press.Google Scholar
Schumacher, J., Scheel, J.D., Krasnov, D., Donzis, D.A., Yakhot, V. & Sreenivasan, K.R. 2014 Small-scale universality in fluid turbulence. Proc. Natl Acad. Sci. USA 111 (30), 1096110965.CrossRefGoogle ScholarPubMed
Shang, X.-D., Tong, P. & Xia, K.-Q. 2008 Scaling of the local convective heat flux in turbulent Rayleigh–Bénard convection. Phys. Rev. Lett. 100 (24), 244503.CrossRefGoogle ScholarPubMed
Sharp, K.V., Kim, K.C. & Adrian, R. 2000 Dissipation estimation around a Rushton turbine using particle image velocimetry. In Laser Techniques Applied to Fluid Mechanics (ed. R.J. Adrian, D.F.G. Durão, F. Durst, M.V. Heitor, M. Maeda & J.H. Whitelaw), pp. 337–354. Springer.CrossRefGoogle Scholar
Shraiman, B.I. & Siggia, E.D. 1990 Heat transport in high-Rayleigh-number convection. Phys. Rev. A 42 (6), 36503653.CrossRefGoogle ScholarPubMed
Sun, C. & Xia, K.-Q. 2005 Scaling of the Reynolds number in turbulent thermal convection. Phys. Rev. E 72 (6), 067302.CrossRefGoogle ScholarPubMed
Taylor, G.I. 1938 The spectrum of turbulence. Proc. R. Soc. Lond. A 164 (919), 476490.CrossRefGoogle Scholar
Verzicco, R. & Camussi, R. 2003 Numerical experiments on strongly turbulent thermal convection in a slender cylindrical cell. J. Fluid Mech. 477, 1949.CrossRefGoogle Scholar
Vishnu, V.T., De, A.K. & Mishra, P.K. 2022 Statistics of thermal plumes and dissipation rates in turbulent Rayleigh–Bénard convection in a cubic cell. Intl J. Heat Mass Transfer 182, 121995.CrossRefGoogle Scholar
Wang, G., Yang, F., Wu, K., Ma, Y., Peng, C., Liu, T. & Wang, L.P. 2021 Estimation of the dissipation rate of turbulent kinetic energy: a review. Chem. Engng Sci. 229 (11), 116133.CrossRefGoogle Scholar
Xia, K.-Q. 2013 Current trends and future directions in turbulent thermal convection. Theor. Appl. Mech. Lett. 3 (5), 052001.CrossRefGoogle Scholar
Xia, K.-Q., Huang, S.-D., Xie, Y.-C. & Zhang, L. 2023 a Tuning heat transport via coherent structure manipulation: recent advances in thermal turbulence. Natl. Sci. Rev. 10 (6), nwad012.CrossRefGoogle ScholarPubMed
Xia, K.-Q., Xu, F. & Zhang, L. 2023 b Three-dimensional properties of viscous boundary layer in buoyancy driven turbulence. In 15th International Symposium on Particle Image Velocimetry 2023. San Diego, California, USA.Google Scholar
Xin, Y.-B., Xia, K.-Q. & Tong, P. 1996 Measured velocity boundary layers in turbulent convection. Phys. Rev. Lett. 77 (7), 12661269.CrossRefGoogle ScholarPubMed
Xu, F., Zhang, L. & Xia, K.-Q. 2022 Three-dimensional properties of the viscous boundary layer in turbulent Rayleigh–Bénard convection. J. Fluid Mech. 947, A15.CrossRefGoogle Scholar
Yeung, P.K., Donzis, D.A. & Sreenivasan, K.R. 2012 Dissipation, enstrophy and pressure statistics in turbulence simulations at high Reynolds numbers. J. Fluid Mech. 700, 515.CrossRefGoogle Scholar
Yeung, P.K., Zhai, X.M. & Sreenivasan, K.R. 2015 Extreme events in computational turbulence. Proc. Natl Acad. Sci. USA 112 (41), 1263312638.CrossRefGoogle ScholarPubMed
Zeff, B.W., Lanterman, D.D., McAllister, R., Roy, R., Kostelich, E.J. & Lathrop, D.P. 2003 Measuring intense rotation and dissipation in turbulent flows. Nature 421 (6919), 146149.CrossRefGoogle ScholarPubMed
Zhang, Y., Zhou, Q. & Sun, C. 2017 Statistics of kinetic and thermal energy dissipation rates in two-dimensional turbulent Rayleigh–Bénard convection. J. Fluid Mech. 814, 165184.CrossRefGoogle Scholar