Data-informed dynamic acoustic source modeling in high-speed jets

We build on work by Zare, Jovanovic, and Georgiou (JFM, vol. 812, 2017) to develop dynamic noise models that account for the far-field acoustics of a Mach 0.9 subsonic turbulent jet. Given far-field time-averaged correlations of pressure we formulate an inverse problem to determine the forcing statistics to the linearized model that provide consistency with high-fidelity large-eddy simulation. Fluctuations in the near-field of the jet are projected to far-field sound via the Ffowcs Williams-Hawkings method. To reduce computational effort, we utilize the method of snapshots to obtain a reduced-order model of the input-output behavior of the jet, and design filters to provide an explicit dynamical representation of acoustic sources. Our data-driven approach reconciles linearized models of near-field fluctuations to acoustics at various observer angles by introducing dynamical modifications to the linearized operator. We conduct a frequency response analysis to examine the predictive capability of our model in capturing near-field coherent flow structures and far-field acoustic radiation.