<p>For a single timestep, a spectral solver is known to be more accurate than its finite-difference counterpart. However, as we show in this article, turbulence simulations using the two methods have nearly the same accuracy. In this article, we simulate forced hydrodynamic turbulence on a uniform <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(256^3\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mn>256</mn> <mn>3</mn> </msup> </math></EquationSource> </InlineEquation> grid for Reynolds numbers 965, 1231, 1515, and 1994. We show that the two methods yield nearly the same evolution for the total energy and the flow profiles. In addition, the steady-state energy spectrum, energy flux, and probability distribution functions of the velocity and its derivatives are very similar. We argue that within a turbulence attractor, the numerical errors are likely to get canceled (rather than get added up), which leads to similar results for the finite-difference and spectral methods. These findings are very valuable, considering that a parallel finite-difference simulation is more versatile and efficient (for large grids) than its spectral counterpart.</p>

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Relative accuracy of turbulence simulations using pseudo-spectral and finite difference solvers

  • Akash Rodhiya,
  • Shashwat Bhattacharya,
  • Mahendra K Verma

摘要

For a single timestep, a spectral solver is known to be more accurate than its finite-difference counterpart. However, as we show in this article, turbulence simulations using the two methods have nearly the same accuracy. In this article, we simulate forced hydrodynamic turbulence on a uniform \(256^3\) 256 3 grid for Reynolds numbers 965, 1231, 1515, and 1994. We show that the two methods yield nearly the same evolution for the total energy and the flow profiles. In addition, the steady-state energy spectrum, energy flux, and probability distribution functions of the velocity and its derivatives are very similar. We argue that within a turbulence attractor, the numerical errors are likely to get canceled (rather than get added up), which leads to similar results for the finite-difference and spectral methods. These findings are very valuable, considering that a parallel finite-difference simulation is more versatile and efficient (for large grids) than its spectral counterpart.