Abstract
A study on the application of the Tensor Train decomposition method to 3D direct numerical simulation data of channel turbulence flow is presented. The approach is validated with respect to compression rate and storage requirement. In tests with synthetic data, it is found that grid-aligned self-similar patterns are well captured and also the application to non-grid-aligned self-similarity yields satisfying results. It is observed that the shape of the input Tensor significantly affects the compression rate. Applied to data of channel turbulent flow, the Tensor Train format allows for surprisingly high compression rates whilst ensuring low relative errors. However, the results indicate that representation of highly irregular flows at low ranks cannot be expected.
Similar content being viewed by others
References
Adams, N.: A stochastic extension of the approximate deconvolution model. Phys. Fluids 23(055103), 1–9 (2011)
Adrian, R., Meinhart, C., Tomkins, C.: Vortex organization in the outer region of the turbulent boundary layer. J. Fluid Mech. 422, 1–54 (2000). https://doi.org/10.1017/S0022112000001580
Bakewell, H., Lumley, J.: Viscous sublayer and adjacent wall region in turbulent pipe flow. Phys. Fluids 10(9), 1880–1889 (1967). https://doi.org/10.1063/1.1762382
Ballani, J., Grasedyck, L.: Tree adaptive approximation in the hierarchical tensor format. SIAM J. Sci. Comput. 36, A1415–A1431 (2014)
Bürger, K., et al.: Vortices within vortices: hierarchical nature of vortex tubes in turbulence. arXiv:1210.3325 [physics.flu-dyn] (2013)
Cattani, C.: Wavelet analysis of self similar functions. J. Dyn. Syst. Geom. Theor. 9(1), 75–97 (2011). https://doi.org/10.1080/1726037X.2011.10698594
Corino, E., Brodkey, R.: A visual investigation of the wall region in turbulent flow. J. Fluid Mech. 37, 1–30 (1969). https://doi.org/10.1017/S0022112069000395
De Stefano, G., Nejadmalayeri, A., Vasilyev, O.: Wall-resolved wavelet-based adaptive large-eddy simulation of bluff-body flows with variable thresholding. J. Fluid Mech. 788, 303–336 (2016). https://doi.org/10.1017/jfm.2015.708
De Stefano, G., Vasilyev, O.: A fully adaptive wavelet-based approach to homogeneous turbulence simulation. J. Fluid Mech. 695, 149–172 (2012). https://doi.org/10.1017/jfm.2012.6
Dolgov, S., Khoromskij, B., Savostyanov, D.: Superfast Fourier transform using QTT approximation. J. Fourier Anal. Appl. 18, 915 (2012). https://doi.org/10.1007/s00041-012-9227-4
Farge, M., Rabreau, G.: Transformée en ondelettes pour détecter et analyser les structures cohérentes dans les écoulements turbulents bidimensionnels. C. R. Acad. Sci. Paris 307, 1479–1486 (1988)
Farge, M., Schneider, K., Kevlahan, N.: Non-Gaussianity and coherent vortex simulation for two-dimensional turbulence using an adaptive orthogonal wavelet basis. Phys. Fluids 11, 2187 (1999). https://doi.org/10.1063/1.870080
Frisch, U., Sulem, P.L., Nelkin, M.: A simple dynamical model of intermittent fully developed turbulence. J. Fluid Mech. 87(4), 719–736 (1978). https://doi.org/10.1017/S0022112078001846
Fröhlich, J., Schneider, K.: Numerical simulation of decaying turbulence in an adaptive wavelet basis. Appl. Comput. Harmonic Anal. 3, 393–397 (1996). https://doi.org/10.1006/acha.1996.0033
Fröhlich, J., Uhlmann, M.: Orthonormal polynomial wavelets on the interval and applications to the analysis of turbulent flow fields. SIAM J. Appl. Math. 63(5), 1789–1830 (2003)
Germano, M., Piomelli, U., Moin, P., Cabot, W.H.: A dynamic subgrid scale eddy viscosity model. Phys. Fluids A 48, 273–337 (1991)
Goldstein, D., Vasilyev, O.: Stochastic coherent adaptive large eddy simulation method. Phys. Fluids 16, 2497 (2004). https://doi.org/10.1063/1.1736671
Grasedyck, L., Kressner, D., Tobler, C.: A literature survey of low-rank tensor approximation techniques. GAMM Mitteilungen 36, 53–78 (2013)
Hackbusch, W.: Tensor Spaces and Numerical Tensor Calculus. Springer, Heidelberg (2012)
Hackbusch, W., Kühn, S.: A new scheme for the tensor representation. J. Fourier Anal. Appl. 15, 706–722 (2009)
Hackbusch, W., Schneider, R.: Tensor spaces and hierarchical tensor representations. In: Dahlke, S., Dahmen, W., Griebel, M., Hackbusch, W., Ritter, K., Schneider, R., Schwab, C., Yserentant, H. (eds.) Extraction of Quantifiable Information from Complex Systems. Lecture Notes in Computational Science and Engineering, vol. 102, pp. 237–361. Springer, New York (2014)
Hitchcock, F.: The expression of a tensor or a polyadic as a sum of products. J. Math. Phys. 6, 164–189 (1927)
Horenko, I.: On clustering of non-stationary meteorological time series. Dyn. Atmos. Ocean 49, 164–187 (2010)
Huber, B., Wolf, S.: Xerus—a general purpose tensor library. https://libxerus.org/ (2014–2015)
Hughes, T.: Multiscale phenomena: Greens functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methods. Comput. Methods Appl. Mech. Eng. 127, 387–401 (1995)
Hunt, J.C.R.: Vorticity and vortex dynamics in complex turbulent flows. In: Canadian Society for Mechanical Engineering, Transactions (ISSN 0315-8977), vol. 11, no. 1, 1987, pp. 21–35 (1987)
Hunt, J.C.R., Wray, A.A., Moin, P.: Eddies, streams, and convergence zones in turbulent flows. In: Studying Turbulence Using Numerical Simulation Databases, vol. 2 (1988)
Jimenez, J., Moin, P.: The minimal flow unit in near-wall turbulence. J. Fluid Mech. 225, 213–240 (1991)
John, V., Kindl, A.: A variational multiscale method for turbulent flow simulation with adaptive large scale space. J. Comput. Phys. 229, 301–312 (2010)
Kevlahan, N., Alam, J., Vasilyev, O.: Scaling of space-time modes with Reynolds number in two-dimensional turbulence. J. Fluid Mech. 570, 217–226 (2007). https://doi.org/10.1017/S0022112006003168
Khoromskij, B.: O(\(d \log {N}\))-quantics approximation of \({N}-d\) tensors in high-dimensional numerical modeling. Constr. Approx. 34, 257 (2011). https://doi.org/10.1007/s00365-011-9131-1
Khoromskij, B.: Tensor numerical methods for multidimensional PDEs: theoretical analysis and initial applications. ESAIM Proc. 48, 1–28 (2015). https://doi.org/10.1051/proc/201448001
Khoromskij, B., Miao, S.: Superfast wavelet transform using QTT approximation I: Haar wavelets. Comput. Methods Appl. Math. 14, 537–553 (2014)
Kim, J., Moin, P., Moser, R.: Turbulence statistics in fully developed channel flow at low Reynolds number. J. Fluid Mech. 177, 133–166 (1987)
Kline, S., Robinson, S.: Quasi-coherent structures in the turbulent boundary layer. i—status report on a community-wide summary of the data. In: Kline, S., Afgan, N. (eds.) Near-Wall Turbulence, pp. 200–217. Routledge (1990)
Kolda, T., Bader, B.: Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009)
Kolmogorov, A.: The local structure of turbulence in incompressible viscous fluid for very large Reynolds number. C. R. Acad. Sci. U.S.S.R. 30, 301 (1941)
Kolmogorov, A.: A refinement of previous hypothesis concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number. J. Fluid Mech. 62, 82 (1962)
Kutz, J.: Deep learning in fluid dynamics. J. Fluid Mech. 814, 1–4 (2017)
von Larcher, T., Beck, A., Klein, R., Horenko, I., Metzner, P., Waidmann, M., Igdalov, D., Gassner, G., Munz, C.D.: Towards a framework for the stochastic modelling of subgrid scale fluxes for large eddy simulation. Meteorol. Z. 24(3), 313–342 (2015). https://doi.org/10.1127/metz/2015/0581
Lesieur, M.: Turbulence in Fluids: Stochastic and Numerical Modelling. Nijhoff, The Hague (1987)
Ling, J., Kurzawski, A., Templeton, J.: Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. J. Fluid Mech. 807, 155–166 (2016)
Liu, S., Meneveau, C., Katz, J.: On the properties of similarity subgrid-scale models as deduced from measurements in a turbulent jet. J. Fluid Mech. 275, 83–91 (1994)
Mallat, S.: A Wavelet Tour of Signal Processing, 3rd edn. Academic Press, Boston (2009)
Metzner, P., Putzig, L., Horenko, I.: Analysis of persistent non-stationary time series and applications. CAMCoS 7, 175–229 (2012)
Moser, R., Kim, J., Mansour, N.: Direct numerical simulation of turbulent channel flow up to R\(\text{ e }_\tau \)=590. Phys. Fluids 11(4), 943–945 (1999)
Nejadmalayeri, A., Vezolainen, A., DeStefano, G., Vasilyev, O.: Fully adaptive turbulence simulations based on lagrangian spatio-temporally varying wavelet thresholding. J. Fluid Mech. 749, 794–817 (2014). https://doi.org/10.1017/jfm.2014.241
Nejadmalayeri, A., Vezolainen, A., Vasilyev, O.: Reynolds number scaling of coherent vortex simulation and stochastic coherent adaptive large eddy simulation. Phys. Fluids 25(11), 110–823 (2013). https://doi.org/10.1063/1.4825260
Oboukhov, A.: Some specific features of atmospheric turbulence. J. Fluid Mech. 62, 77 (1962)
Oseledets, I.: Approximation of \(2^{d}\times 2^{d}\) matrices using tensor decomposition. SIAM J. Matrix Anal. Appl. 31, 2130–2145 (2010)
Oseledets, I., Tyrtyshnikov, E.: Breaking the curse of dimensionality, or how to use SVD in many dimensions. SIAM J. Sci. Comput. 31, 3744–3759 (2009)
Oseledets, I., Tyrtyshnikov, E.: TT-cross approximation for multidimensional arrays. Linear Algebra Appl. 432, 70–88 (2010)
Oseledets, I., Tyrtyshnikov, E.: Algebraic wavelet transform via quantics tensor train decomposition. SIAM J. Sci. Comput. 33, 1315–1328 (2011)
Paladin, G., Vulpiani, A.: Degrees of freedom of turbulence. Phys. Rev. A 35, 1971–1973 (1987). https://doi.org/10.1103/PhysRevA.35.1971
Pope, S.: Turbulent Flows. Cambridge University Press, Cambridge (2000)
Richardson, L.: Weather Prediction by Numerical Process. Cambridge University Press, Cambridge (1922)
Robinson, S.: Coherent motions in the turbulent boundary layer. Ann. Rev. Fluid Mech. 23, 601–639 (1991)
Sagaut, P.: Large Eddy Simulation for Incompressible Flows, 3rd edn. Springer, Berlin (2006)
Scotti, A., Meneveau, C.: A fractal model for large eddy simulation of turbulent flows. Physica D 127, 198–232 (1999)
Smagorinsky, J.: General circulation experiments with the primitive equations. Mon. Wea. Rev. 93, 99–164 (1963)
Ting, L., Klein, R., Knio, O.M.: Vortex Dominated Flows: Analysis and Computation for Multiple Scales. Series in Applied Mathematical Sciences, vol. 116. Springer, Berlin (2007)
Tucker, L.: Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311 (1966)
Uhlmann, M.: Generation of a temporally well-resolved sequence of snapshots of the flow-field in turbulent plane channel flow (2000). http://www-turbul.ifh.uni-karlsruhe.de/uhlmann/reports/produce.pdf. Accessed July 2017
Uhlmann, M.: Generation of initial fields for channel flow investigation. Intermediate Report (2000). http://www-turbul.ifh.uni-karlsruhe.de/uhlmann/home/report.html. Accessed July 2017
Uhlmann, M.: The need for de-aliasing in a Chebyshev pseudo-spectral method. Technical note no. 60 (2000). http://www-turbul.ifh.uni-karlsruhe.de/uhlmann/reports/dealias.pdf. Accessed July 2017
Vergassola, M., Frisch, U.: Wavelet transforms of self-similar processes. Phys. D Nonlinear Phenom. 54(1), 58–64 (1991). https://doi.org/10.1016/0167-2789(91)90107-K. http://www.sciencedirect.com/science/article/pii/016727899190107K
Wallace, J., Eckelmann, H., Brodkey, R.: The wall region in turbulent shear flow. J. Fluid Mech. 54(1), 39–48 (1972). https://doi.org/10.1017/S0022112072000515
Wang, J., Wu, J., Ling, J., Iaccarino, G., Xiao, H.: A comprehensive physics-informed machine learning framework for predictive turbulence modeling. arXiv:1701.07102v1 [physics.flu-dyn] (2017)
Willmarth, W., Lu, S.: Structure of the Reynolds stress near the wall. J. Fluid Mech. 55(1), 65–92 (1972). https://doi.org/10.1017/S002211207200165X
Wu, J., Wang, J., Xiao, H., Ling, J.: A priori assessment of prediction confidence for data-driven turbulence modeling. arXiv:1607.04563v2 [physics.comp-ph] (2017)
Zhang, Z., Duraisamy, K.: Machine learning methods for data-driven turbulence modeling. AIAA Aviation (2015). https://doi.org/10.2514/6.2015-2460
Acknowledgements
This research has been funded by Deutsche Forschungsgemeinschaft (DFG) through grant CRC 1114 ‘Scaling Cascades in Complex Systems’, Project B04 ‘Multiscale Tensor decomposition methods for partial differential equations’. The authors thank Prof. Illia Horenko (CRC 1114 Mercator Fellow) as well as Prof. Reinhold Schneider and Prof. Harry Yserentant for rich discussions and for steady support. Thomas von Larcher thanks Sebastian Wolf and Benjamin Huber (both at TU Berlin, Germany) very much for developing the Tensor library Xerus which has been used for data analysis, as well as for their round-the-clock support in the project. The data were generated and processed using resources of the North-German Supercomputing Alliance (HLRN), Germany, and of the Department of Mathematics and Computer Science, Freie Universität Berlin, Germany. The authors thank Alexander Kuhn and Christian Hege (both at Zuse Institute Berlin, Germany) for steady support in data processing and data visualization.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Oleg V. Vasilyev.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Thomas von Larcher: formerly Freie Universität Berlin.
Rights and permissions
About this article
Cite this article
von Larcher, T., Klein, R. On identification of self-similar characteristics using the Tensor Train decomposition method with application to channel turbulence flow. Theor. Comput. Fluid Dyn. 33, 141–159 (2019). https://doi.org/10.1007/s00162-019-00485-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00162-019-00485-z