Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-10T10:41:37.079Z Has data issue: false hasContentIssue false

Large-eddy simulation of turbulent flow over a parametric set of bumps

Published online by Cambridge University Press:  13 March 2019

Racheet Matai*
Affiliation:
Department of Aerospace Engineering, Iowa State University, Ames, IA 50010, USA
Paul Durbin
Affiliation:
Department of Aerospace Engineering, Iowa State University, Ames, IA 50010, USA
*
Email address for correspondence: rmatai@iastate.edu

Abstract

Turbulent flow over a series of increasingly high, two-dimensional bumps is studied by well-resolved large-eddy simulation. The mean flow and Reynolds stresses for the lowest bump are in good agreement with experimental data. The flow encounters a favourable pressure gradient over the windward side of the bump, but does not relaminarize, as is evident from near-wall fluctuations. A patch of high turbulent kinetic energy forms in the lee of the bump and extends into the wake. It originates near the surface, before flow separation, and has a significant influence on flow development. The highest bumps create a small separation bubble, whereas flow over the lowest bump does not separate. The log law is absent over the entire bump, evidencing strong disequilibrium. This dataset was created for data-driven modelling. An optimization method is used to extract fields of variables that are used in turbulence closure models. From this, it is shown how these models fail to correctly predict the behaviour of these variables near to the surface. The discrepancies extend further away from the wall in the adverse pressure gradient and recovery regions than in the favourable pressure gradient region.

Type
JFM Papers
Copyright
© 2019 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

Alving, A. E., Smits, A. J. & Watmuff, J. H. 1990 Turbulent boundary layer relaxation from convex curvature. J. Fluid Mech. 211, 529556.10.1017/S0022112090001689Google Scholar
Arolla, S. K. 2016 Inflow turbulence generation for eddy-resolving simulations of turbomachinery flows. J. Fluids Engng 138 (3), 031201.Google Scholar
Bandyopadhyay, P. R. & Ahmed, A. 1993 Turbulent boundary layers subjected to multiple curvatures and pressure gradients. J. Fluid Mech. 246, 503527.10.1017/S0022112093000242Google Scholar
Blackwelder, R. F. & Kovasznay, L. S. 1972 Large-scale motion of a turbulent boundary layer during relaminarization. J. Fluid Mech. 53 (1), 6183.10.1017/S0022112072000047Google Scholar
Breuer, M., Peller, N., Rapp, C. & Manhart, M. 2009 Flow over periodic hills – numerical and experimental study in a wide range of Reynolds numbers. Comput. Fluids 38, 433.10.1016/j.compfluid.2008.05.002Google Scholar
Duraisamy, K., Zhang, Z. J. & Singh, A. P. 2015 New approaches in turbulence and transition modeling using data-driven techniques. In 53rd AIAA Aerospace Sciences Meeting, AIAA Paper 2015–1284.Google Scholar
Durbin, P. A. 2018 Some recent developments in turbulence closure modeling. Annu. Rev. Fluid Mech. 50, 127.10.1146/annurev-fluid-122316-045020Google Scholar
Durbin, P. A. & Reif, B. P. 2011 Statistical Theory and Modeling for Turbulent Flows. Wiley.Google Scholar
Matai, R.2018 LES of flow over bumps and machine learning augmented turbulence modeling. PhD thesis, Iowa State University.Google Scholar
Matai, R. & Durbin, P. 2019 Zonal eddy viscosity models based on machine learning. Flow Turbul. Combust., doi:10.1007/s10494-019-00011-5.Google Scholar
Mollicone, J.-P., Battista, F., Gualtieri, P. & Casciola, C. 2017 Effect of geometry and Reynolds number on the turbulent separated flow behind a bulge in a channel. J. Fluid Mech. 823, 100133.10.1017/jfm.2017.255Google Scholar
Parish, E. J. & Duraisamy, K. 2016 A paradigm for data-driven predictive modeling using field inversion and machine learning. J. Comput. Phys. 305, 758774.Google Scholar
Patel, V. 1965 Calibration of the Preston tube and limitations on its use in pressure gradients. J. Fluid Mech. 23 (1), 185208.10.1017/S0022112065001301Google Scholar
Seifert, A. & Pack, L. G. 2002 Active flow separation control on wall-mounted hump at high Reynolds numbers. AIAA J. 40 (7), 13631372.Google Scholar
Singh, A. P., Matai, R., Mishra, A., Duraisamy, K. & Durbin, P. A. 2017 Data-driven augmentation of turbulence models for adverse pressure gradient flows. In 23rd AIAA Computational Fluid Dynamics Conference, AIAA Paper 2017–3626.Google Scholar
Slotnick, J., Khodadoust, A., Alonso, J., Darmofal, D., Gropp, W., Lurie, E. & Mavriplis, D.2014 CFD vision 2030 study: a path to revolutionary computational aerosciences. NASA. Tech. Rep. CR-2014-218178.Google Scholar
Spalart, P. R. & Watmuff, J. H. 1993 Experimental and numerical study of a turbulent boundary layer with pressure gradients. J. Fluid Mech. 249, 337371.10.1017/S002211209300120XGoogle Scholar
Tsuji, Y. & Morikawa, Y. 1976 Turbulent boundary layer with pressure gradient alternating in sign. Aeronaut. Q. 27 (1), 1528.10.1017/S0001925900007514Google Scholar
Webster, D., Degraaff, D. & Eaton, J. 1996 Turbulence characteristics of a boundary layer over a two-dimensional bump. J. Fluid Mech. 320, 5369.10.1017/S0022112096007458Google Scholar
Weller, H. G., Tabor, G., Jasak, H. & Fureby, C. 1998 A tensorial approach to computational continuum mechanics using object-oriented techniques. Comput. Phys. 12 (6), 620631.10.1063/1.168744Google Scholar
Wu, X. & Squires, K. D. 1998 Numerical investigation of the turbulent boundary layer over a bump. J. Fluid Mech. 362, 229271.10.1017/S0022112098008982Google Scholar