Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier–Stokes-Based Jet Noise Prediction

The Reynolds-averaged Navier–Stokes (RANS)-based method is a practical tool to provide rapid assessment of jet noise-reduction concepts. However, the RANS-based requires modeling assumptions represent noise generation and propagation, which often reduces predictive accuracy due model-form uncertainties. In this work, ensemble Kalman filter-based acoustic inversion introduced reduce uncertainties in turbulent kinetic energy dissipation rate based on far-field axial centerline velocity data. results show that data are more effective from infer compared Moreover, inferred source able improve estimation flowfield at unobserved locations. Further, model parameters also considered uncertain quantities, demonstrating ability proposed framework both RANS models. Finally, one realistic case with experimental investigated practicality framework. opens up possibility for inverse sources by incorporating relatively straightforward be measured field.