Reynolds decomposition

In fluid dynamics and turbulence theory, a Reynolds decomposition is a mathematical technique used to separate a field into its mean and fluctuating components.[1]

Decomposition

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A Reynolds decomposition of a field   (e.g., a velocity field) is given by   where   denotes the mean of   (which can be a time, space, or ensemble average), and   denotes the fluctuations from that mean.[2] The fluctuating field is defined as   and satisfies[1][3]   Note that the mean field   is also frequently denoted as  .[4]

Application

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Direct numerical simulation, or resolution of the Navier–Stokes equations (nearly) completely in both space and time, is only possible on extremely fine computational grids using small time steps even for low Reynolds numbers. Running direct numerical simulations often becomes prohibitively computationally expensive at high Reynolds' numbers. Due to computational constraints, simplifications of the Navier-Stokes equations are useful to parameterize turbulence that are smaller than the computational grid, allowing larger computational domains.[5]

Reynolds decomposition allows the simplification of the Navier–Stokes equations by substituting in the sum of the steady component and perturbations to the velocity profile and taking the mean value, to obtain the Reynolds-averaged Navier–Stokes equations. The resulting equation contains a nonlinear term known as the Reynolds stresses, representing effects of turbulence.

See also

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References

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  1. ^ a b Pope, Stephen (2001). Turbulent Flows. p. 83.
  2. ^ Alfonsi, G. (July 2009). "Reynolds-Averaged Navier–Stokes Equations for Turbulence Modeling". Applied Mechanics Reviews. 62 (4): 040802. doi:10.1115/1.3124648.
  3. ^ Adrian, R (2000). "Analysis and Interpretation of instantaneous turbulent velocity fields". Experiments in Fluids. 29 (3): 275–290. Bibcode:2000ExFl...29..275A. doi:10.1007/s003489900087. S2CID 122145330.
  4. ^ Kundu, Pijush (27 March 2015). Fluid Mechanics. Academic Press. p. 609. ISBN 978-0-12-405935-1.
  5. ^ Mukerji, Sudip (1997). Turbulence Computations with 3-D Small-Scale Additive Turbulent Decomposition and Data-Fitting Using Chaotic Map Combinations (PhD thesis). University of Kentucky. doi:10.2172/666048. OSTI 666048. ProQuest 304354392.