  Noise prediction requires the resolution of relevant acoustic sources on all
scales of a turbulent flow. High-resolution direct numerical and large-eddy
simulation would be ideal but both are usually too costly despite developments
in high performance computing. Lower-order modeling approaches are therefore of
general interest. A crucial but standing problem for accurate predictive
modeling is the estimation of missing noise from the modeled scales. In this
paper we address this problem by presenting a novel lower-order framework that
couples the one-dimensional turbulence model to the Ffowcs-Williams and
Hawkings approach for prediction of the far-field noise of a subsonic turbulent
round jet.
