Resumen:
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[EN] The calculation of turbulence statistics is considered the key unsolved problem of fluid mechanics, i.e., precisely the computation of arbitrary statistical velocity moments from first principles alone. Using symmetry ...[+]
[EN] The calculation of turbulence statistics is considered the key unsolved problem of fluid mechanics, i.e., precisely the computation of arbitrary statistical velocity moments from first principles alone. Using symmetry theory, we derive turbulent scaling laws for moments of arbitrary order in two regions of a turbulent channel flow. Besides the classical scaling symmetries of space and time, the key symmetries for the present work reflect the two well-known characteristics of turbulent flows: non-Gaussianity and intermittency. To validate the new scaling laws we made a new simulation at an unprecedented friction Reynolds number of 10 000, large enough to test the new scaling laws. Two key results appear as an application of symmetry theory, which allowed us to generate symmetry invariant solutions for arbitrary orders of moments for the underlying infinite set of moment equations. First, we show that in the sense of the generalization of the deficit law all moments of the streamwise velocity in the channel center follow a power-law scaling, with exponents depending on the first and second moments alone. Second, we show that the logarithmic law of the mean streamwise velocity in wall-bounded flows is indeed a valid solution of the moment equations, and further, all higher moments in this region follow a power law, where the scaling exponent of the second moment determines all higher moments. With this we give a first complete mathematical framework for all moments in the log region, which was first discovered about 100 years ago.
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Agradecimientos:
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The authors gratefully acknowledge computing time on the Gauss Centre for Supercomputing e.V. on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre under Project No. pr92la, on the supercomputer Lichtenberg ...[+]
The authors gratefully acknowledge computing time on the Gauss Centre for Supercomputing e.V. on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre under Project No. pr92la, on the supercomputer Lichtenberg II at TU Darmstadt under Project No. project00072, and on the supercomputer CLAIX-2018 at RWTH-Aachen under Project No. bund0008. S. V. K. gratefully acknowledges funding from projects OB96/39-1 and M. O. for partial funding from OB 96/48-1, both financed by the German Research Foundation (DFG) . S. H. and F. A.-A. were supported by Contract No. RTI2018-102256-B-I00 of Ministerio de Ciencia, innovacion y Universidades/FEDER. F. A.-A. is partially funded by GVA/FEDER project ACIF2018. Finally, the authors thank Paul Hollmann for help with the manuscript.
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