What is it about?

A method for generating synthetic correlated stochastic data from uncorrelated sequences is detailed and applied to the problem of inflow turbulence generation for CFD simulations. The technique constructs divergence-free anisotropic random fields with the sensible spectrum and complete complex correlation in space and time.

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Why is it important?

Scale-resolving computational fluid dynamics (CFD) methods require carefully constructed boundary conditions to produce accurate results. The inflow data should be unsteady and the successive realizations must follow specific statistics while ideally having a particular correlation in both space and time.

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This page is a summary of: Divergence-free turbulent inflow data from realistic covariance tensor, Physics of Fluids, January 2023, American Institute of Physics, DOI: 10.1063/5.0136568.
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