Hostname: page-component-7bb8b95d7b-s9k8s Total loading time: 0 Render date: 2024-09-19T11:38:10.901Z Has data issue: false hasContentIssue false

Machine-aided turbulence theory

Published online by Cambridge University Press:  31 August 2018

Javier Jiménez*
Affiliation:
School of Aeronautics, U. Politécnica Madrid, 28040 Madrid, Spain
*
Email address for correspondence: jimenez@torroja.dmt.upm.es

Abstract

The question of whether significant subvolumes of a turbulent flow can be identified by automatic means, independently of a priori assumptions, is addressed using the example of two-dimensional decaying turbulence. Significance is defined as influence on the future evolution of the flow, and the problem is cast as an unsupervised machine ‘game’ in which the rules are the Navier–Stokes equations. It is shown that significance is an intermittent quantity in this particular flow, and that, in accordance with previous intuition, its most significant features are vortices, while the least significant ones are dominated by strain. Subject to cost considerations, the method should be applicable to more general turbulent flows.

Type
JFM Rapids
Copyright
© 2018 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

Breiman, L. 2001 Random forests. Mach. Learn. 45, 532.Google Scholar
Brown, G. L. & Roshko, A. 2012 Turbulent shear layers and wakes. J. Turbul. 13, N51.Google Scholar
Cardesa, J. I., Vela-Martín, A. & Jiménez, J. 2017 The turbulent cascade in five dimensions. Science 357, 782784.Google Scholar
Cencini, M. & Vulpiani, A. 2013 Finite-size Lyapunov exponent: review on applications. J. Phys. A: Math. Theor. 46, 254019.Google Scholar
Cimbala, J. M., Nagib, H. M. & Roshko, A. 1988 Large structure in the far wakes of two-dimensional bluff bodies. J. Fluid Mech. 190, 265298.Google Scholar
Cormen, T. H., Leiserson, C. E. & Rivest, R. L. 1990 Introduction to Algorithms. MIT Press.Google Scholar
Ding, R. & Li, J. 2007 Nonlinear finite-time Lyapunov exponent and predictability. Phys. Lett. A 364, 396400.Google Scholar
Eckmann, J. & Ruelle, D. 1985 Ergodic theory of chaos and strange attractors. Rev. Mod. Phys. 57, 617656.Google Scholar
Jiménez, J. 2018 Coherent structures in wall-bounded turbulence. J. Fluid Mech. 842, P1.Google Scholar
Jiménez, J. & Pinelli, A. 1999 The autonomous cycle of near-wall turbulence. J. Fluid Mech. 389, 335359.Google Scholar
Jiménez, J., Wray, A. A., Saffman, P. G. & Rogallo, R. S. 1993 The structure of intense vorticity in isotropic turbulence. J. Fluid Mech. 255, 6590.Google Scholar
Kline, S. J., Reynolds, W. C., Schraub, F. A. & Runstadler, P. W. 1967 Structure of turbulent boundary layers. J. Fluid Mech. 30, 741773.Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. 2015 Deep learning. Nature 521, 436444.Google Scholar
Lu, S. S. & Willmarth, W. W. 1973 Measurements of the structure of the Reynolds stress in a turbulent boundary layer. J. Fluid Mech. 60, 481511.Google Scholar
McWilliams, J. C. 1990 The vortices of two-dimensional turbulence. J. Fluid Mech. 219, 361385.Google Scholar
Richardson, L. F. 1920 The supply of energy from and to atmospheric eddies. Proc. R. Soc. Lond. A 97, 354373.Google Scholar
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T. & Hassabis, D. 2017 Mastering the game of Go without human knowledge. Nature 550, 354359.Google Scholar
Vincent, A. & Meneguzzi, M. 1991 The spatial structure and statistical properties of homogeneous turbulence. J. Fluid Mech. 225, 120.Google Scholar