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Visibility network analysis of large-scale intermittency in convective surface layer turbulence

Published online by Cambridge University Press:  31 August 2021

Subharthi Chowdhuri Open the ORCID record for Subharthi Chowdhuri [Opens in a new window] ,
Giovanni Iacobello Open the ORCID record for Giovanni Iacobello [Opens in a new window]  and
Tirtha Banerjee Open the ORCID record for Tirtha Banerjee [Opens in a new window]
Subharthi Chowdhuri*
Affiliation:
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Dr Homi Bhaba Road, Pashan, Pune 411008, India
Giovanni Iacobello
Affiliation:
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
Tirtha Banerjee
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA
*
†Email address for correspondence: subharthi.cat@tropmet.res.in
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Abstract

Large-scale intermittency is a widely observed phenomenon in convective surface layer turbulence that induces non-Gaussian temperature statistics, while such a signature is not observed for velocity signals. Although approaches based on probability density functions have been used so far, those are not able to explain to what extent the signals’ temporal structure impacts the statistical characteristics of the velocity and temperature fluctuations. To tackle this issue, a visibility network analysis is carried out on a field-experimental dataset from a convective atmospheric surface layer flow. Through surrogate data and network-based measures, we demonstrate that the temperature intermittency is related to strong nonlinear dependencies in the temperature signals. Conversely, a competition between linear and nonlinear effects tends to inhibit the temperature-like intermittency behaviour in streamwise and vertical velocities. Based on present findings, new research avenues are likely to be opened up in studying large-scale intermittency in convective turbulence.

JFM classification

Geophysical and Geological Flows: Atmospheric flows Turbulent Flows: Intermittency
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 925 , 25 October 2021 , A38
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

REFERENCES

Basu, S., Foufoula-Georgiou, E., Lashermes, B. & Arnéodo, A. 2007 Estimating intermittency exponent in neutrally stratified atmospheric surface layer flows: A robust framework based on magnitude cumulant and surrogate analyses. Phys. Fluids 19 (11), 115102.CrossRefGoogle Scholar
Batchelor, G.K. 1953 The Theory of Homogeneous Turbulence. Cambridge University Press.Google Scholar
Batchelor, G.K. & Townsend, A.A. 1949 The nature of turbulent motion at large wave-numbers. Proc. Math. Phys. Engng Sci. 199 (1057), 238–255.Google Scholar
Belmonte, A. & Libchaber, A. 1996 Thermal signature of plumes in turbulent convection: the skewness of the derivative. Phys. Rev. E 53 (5), 4893.CrossRefGoogle ScholarPubMed
Cabrit, O., Mathis, R. & Marusic, I. 2013 On the fundamental fluctuating wall-shear-stress. In International Symposium on Turbulence and Shear Flow Phenomena (TSFP-8), num. TBL3A. Begel House.Google Scholar
Camussi, R. & Verzicco, R. 2004 Temporal statistics in high Rayleigh number convective turbulence. Eur. J. Mech. 23 (3), 427–442.CrossRefGoogle Scholar
Castaing, B., Gunaratne, G., Heslot, F., Kadanoff, L., Libchaber, A., Thomae, S., Wu, X.Z., Zaleski, S. & Zanetti, G. 1989 Scaling of hard thermal turbulence in Rayleigh–Bénard convection. J. Fluid Mech. 204, 1–30.CrossRefGoogle Scholar
Charakopoulos, A.K., Karakasidis, T.E., Papanicolaou, P.N. & Liakopoulos, A. 2014 The application of complex network time series analysis in turbulent heated jets. Chaos 24 (2), 024408.CrossRefGoogle Scholar
Chowdhuri, S., Kumar, S. & Banerjee, T. 2020 Revisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence. J. Fluid Mech. 899, A26.CrossRefGoogle Scholar
Chowdhuri, S., McNaughton, K.G. & Prabha, T.V. 2019 An empirical scaling analysis of heat and momentum cospectra above the surface friction layer in a convective boundary layer. Boundary-Layer Meteorol. 170 (2), 257–284.CrossRefGoogle Scholar
Chu, C.R., Parlange, M.B., Katul, G.G. & Albertson, J.D. 1996 Probability density functions of turbulent velocity and temperature in the atmospheric surface layer. Water Resour. Res. 32 (6), 1681–1688.CrossRefGoogle Scholar
Donner, R.V. & Donges, J.F. 2012 Visibility graph analysis of geophysical time series: potentials and possible pitfalls. Acta Geophys. 60 (3), 589–623.CrossRefGoogle Scholar
Dupont, S., Argoul, F., Gerasimova-Chechkina, E., Irvine, M.R. & Arneodo, A. 2020 Experimental evidence of a phase transition in the multifractal spectra of turbulent temperature fluctuations at a forest canopy top. J. Fluid Mech. 896, A15.CrossRefGoogle Scholar
Emran, M.S. & Schumacher, J. 2008 Fine-scale statistics of temperature and its derivatives in convective turbulence. J. Fluid Mech. 611, 13–34.CrossRefGoogle Scholar
Feraco, F., Marino, R., Pumir, A., Primavera, L., Mininni, P.D., Pouquet, A. & Rosenberg, D. 2018 Vertical drafts and mixing in stratified turbulence: sharp transition with Froude number. Europhys. Lett. 123 (4), 44002.CrossRefGoogle Scholar
Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S. & Okuno, Y. 2017 Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Phys. Rev. Appl. 7 (4), 044027.CrossRefGoogle Scholar
Hasson, U., Iacovacci, J., Davis, B., Flanagan, R., Tagliazucchi, E., Laufs, H. & Lacasa, L. 2018 A combinatorial framework to quantify peak/pit asymmetries in complex dynamics. Sci. Rep. 8 (1), 3557.CrossRefGoogle ScholarPubMed
He, X., Wang, Y. & Tong, P. 2018 Dynamic heterogeneity and conditional statistics of non-Gaussian temperature fluctuations in turbulent thermal convection. Phys. Rev. Fluids 3 (5), 052401.CrossRefGoogle Scholar
Huang, Y.X., Schmitt, F.G., Lu, Z.M. & Liu, Y.L. 2008 An amplitude-frequency study of turbulent scaling intermittency using empirical mode decomposition and Hilbert spectral analysis. Europhys. Lett. 84 (4), 40010.CrossRefGoogle Scholar
Hutchins, N. & Marusic, I. 2007 Evidence of very long meandering features in the logarithmic region of turbulent boundary layers. J. Fluid Mech. 579, 1–28.CrossRefGoogle Scholar
Iacobello, G., Marro, M., Ridolfi, L., Salizzoni, P. & Scarsoglio, S. 2019 Experimental investigation of vertical turbulent transport of a passive scalar in a boundary layer: statistics and visibility graph analysis. Phys. Rev. Fluids 4 (10), 104501.CrossRefGoogle Scholar
Iacobello, G., Ridolfi, L. & Scarsoglio, S. 2020 A review on turbulent and vortical flow analyses via complex networks. Physica A 563, 125476.CrossRefGoogle Scholar
Iacobello, G., Ridolfi, L. & Scarsoglio, S. 2021 Large-to-small scale frequency modulation analysis in wall-bounded turbulence via visibility networks. J. Fluid Mech. 918, A13.CrossRefGoogle Scholar
Iacobello, G., Scarsoglio, S. & Ridolfi, L. 2018 Visibility graph analysis of wall turbulence time-series. Phys. Lett. A 382 (1), 1–11.CrossRefGoogle Scholar
Jimenez, J. 1998 Turbulent velocity fluctuations need not be Gaussian. J. Fluid Mech. 376, 139–147.CrossRefGoogle Scholar
Kaimal, J.C. & Finnigan, J.J. 1994 Atmospheric Boundary Layer Flows: Their Structure and Measurement. Oxford University Press.CrossRefGoogle Scholar
Katul, G.G., Parlange, M.B. & Chu, C.R. 1994 Intermittency, local isotropy, and non-Gaussian statistics in atmospheric surface layer turbulence. Phys. Fluids 6 (7), 2480–2492.CrossRefGoogle Scholar
Keylock, C.J. 2017 Multifractal surrogate-data generation algorithm that preserves pointwise Hölder regularity structure, with initial applications to turbulence. Phys. Rev. E 95 (3), 032123.CrossRefGoogle Scholar
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y. & Porter, M.A. 2014 Multilayer networks. J. Complex Network 2 (3), 203–271.CrossRefGoogle Scholar
Lacasa, L., Luque, B., Ballesteros, F., Luque, J. & Nuno, J.C. 2008 From time series to complex networks: the visibility graph. Proc. Natl Acad. Sci. USA 105 (13), 4972–4975.CrossRefGoogle ScholarPubMed
Lancaster, G., Iatsenko, D., Pidde, A., Ticcinelli, V. & Stefanovska, A. 2018 Surrogate data for hypothesis testing of physical systems. Phys. Rep. 748, 1–60.CrossRefGoogle Scholar
Liu, C., Zhou, W. & Yuan, W. 2010 Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence. Physica A 389 (13), 2675–2681.CrossRefGoogle Scholar
Liu, L., Hu, F. & Cheng, X.L. 2011 Probability density functions of turbulent velocity and temperature fluctuations in the unstable atmospheric surface layer. J. Geophys. Res. Atmos. 116 (D12), D12117.CrossRefGoogle Scholar
Lohse, D. & Xia, K.Q. 2010 Small-scale properties of turbulent Rayleigh–Bénard convection. Annu. Rev. Fluid Mech. 42, 335–364.CrossRefGoogle Scholar
Lyu, R., Hu, F., Liu, L., Xu, J. & Cheng, X. 2018 High-order statistics of temperature fluctuations in an unstable atmospheric surface layer over grassland. Adv. Atmos. Sci. 35 (10), 1265–1276.CrossRefGoogle Scholar
Majda, A.J. & Kramer, P.R. 1999 Simplified models for turbulent diffusion: theory, numerical modelling, and physical phenomena. Phys. Rep. 314 (4–5), 237–574.CrossRefGoogle Scholar
Manshour, P. 2015 Complex network approach to fractional time series. Chaos 25 (10), 103105.CrossRefGoogle ScholarPubMed
Manshour, P., Tabar, M.R.R. & Peinke, J. 2015 Fully developed turbulence in the view of horizontal visibility graphs. J. Stat. Mech. Theory Exp. 2015 (8), P08031.CrossRefGoogle Scholar
Matsushima, T., Nagata, K. & Watanabe, T. 2021 Wavelet analysis of shearless turbulent mixing layer. Phys. Fluids 33 (2), 025109.CrossRefGoogle Scholar
Metzger, M., McKeon, B.J. & Holmes, H. 2007 The near-neutral atmospheric surface layer: turbulence and non-stationarity. Phil. Trans. R. Soc. Lond. 365 (1852), 859–876.Google ScholarPubMed
Murugesan, M. & Sujith, R.I. 2015 Combustion noise is scale-free: transition from scale-free to order at the onset of thermoacoustic instability. J. Fluid Mech. 772, 225–245.CrossRefGoogle Scholar
Newman, M. 2018 Networks, 2nd edn. Oxford University Press.CrossRefGoogle Scholar
Panosfsky, H.A. & Dutton, J.A. 1984 Atmospheric Turbulence: Models and Methods for Engineering Applications. John Wiley & Sons.Google Scholar
Poggi, D., Porporato, A., Ridolfi, L., Albertson, J.D. & Katul, G.G. 2004 Interaction between large and small scales in the canopy sublayer. Geophys. Res. Lett. 31 (5), L05102.CrossRefGoogle Scholar
Roche, P.E. 2020 The ultimate state of convection: A unifying picture of very high Rayleigh numbers experiments. New J. Phys. 22 (7), 073056.CrossRefGoogle Scholar
Shnapp, R. 2021 On small-scale and large-scale intermittency of Lagrangian statistics in canopy flow. J. Fluid Mech. 913, R2.CrossRefGoogle Scholar
Siggia, E.D. 1994 High Rayleigh number convection. Annu. Rev. Fluid Mech. 26, 137–168.CrossRefGoogle Scholar
Sreenivasan, K.R. 1991 Fractals and multifractals in fluid turbulence. Annu. Rev. Fluid Mech. 23 (1), 539–604.CrossRefGoogle Scholar
Sreenivasan, K.R. & Antonia, R.A. 1997 The phenomenology of small-scale turbulence. Annu. Rev. Fluid Mech. 29 (1), 435–472.CrossRefGoogle Scholar
Stiperski, I. & Calaf, M. 2018 Dependence of near-surface similarity scaling on the anisotropy of atmospheric turbulence. Q. J. R. Meteorol. Soc. 144 (712), 641–657.CrossRefGoogle ScholarPubMed
Townsend, A.A. 1947 The measurement of double and triple correlation derivatives in isotropic turbulence. In Mathematical Proceedings of the Cambridge Philosophical Society, vol. 43, pp. 560–570. Cambridge University Press.CrossRefGoogle Scholar
Tsinober, A. 2014 The Essence of Turbulence as a Physical Phenomenon. Springer Science.CrossRefGoogle Scholar
Wang, Y., He, X. & Tong, P. 2019 Turbulent temperature fluctuations in a closed Rayleigh-Bénard convection cell. J. Fluid Mech. 874, 263–284.CrossRefGoogle Scholar
Wyngaard, J.C. 2010 Turbulence in the Atmosphere. Cambridge University Press.CrossRefGoogle 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 (9), 094604.CrossRefGoogle Scholar
Zou, Y., Donner, R.V., Marwan, N., Donges, J.F. & Kurths, J. 2019 Complex network approaches to nonlinear time series analysis. Phys. Rep. 787, 1–97.CrossRefGoogle Scholar
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