Data-driven optimization of forcing in the resolvent analysis of wall turbulence

Abstract eng:
The resolvent framework developed by McKeon & Sharma [1] for wall turbulence is a systems-level model that treats the nonlinear term of the Navier-Stokes equations as a forcing that acts upon the linear dynamics to output a velocity and pressure response across wavenumber-frequency space. An optimization scheme was developed to determine a full spectral representation of the unknown nonlinear forcing such that the resulting velocity spectra optimally matched those of DNS for turbulent channel flow. Results show that this optimization not only determines a forcing field that leads to excellent reproduction of the DNS velocity spectra but also highlights the potential of this framework to make predictions about statistical quantities not explicitly constrained in the optimization scheme. This study represents continued progress in the development of the resolvent model as a systematic pathway towards both understanding the dynamics of wall turbulence and control objectives such as turbulent drag reduction.

• Export as: BibTeX | MARC | MARCXML | DC | EndNote | NLM | RefWorks
• Add to your basket