Reconstruction of Turbulent Flows at High Reynolds Numbers Using Data Assimilation Techniques

What is it about?

Turbulence models are one of the largest sources of errors of a RANS simulation. We present two data assimilation techniques for correcting these errors in order to reconstruct entire flow fields from few experimental data. We show that our techniques are more accurate than the state of the art, and observe that only small perturbations to the control parameters are necessary to obtain significant improvements in the flow-field reconstruction.

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