Reynolds-averaged Navier-Stokes (RANS) Integration for Shock- Noise (RISN) is a computer program that evaluates acoustic analogies to predict jet noise. Jet noise is due to turbulence from the chaotic flow within the exhaust of a rocket or air-breathing jet engine. The source of jet noise is the turbulent mixing of the exhaust, screech (tones) due to a feedback loop between the semi-periodic shock cells and the nozzle, and broadband shockassociated noise due to the interaction of the turbulence with the shock cells. Acoustic analogies are rearrangements of the Navier-Stokes equations into a left-hand-side propagation operator and a right-hand-side equivalent noise source. RISN is capable of predicting the noise spectrum from all source components within supersonic offdesign jets. Furthermore, the noise from three-dimensional and axisymmetric nozzles can be predicted as long as a steady RANS solution is present. RISN predictions are based upon integrations of computational fluid dynamic solutions. Predictions consist of the spectral density at observers positioned around the nozzle exit.

RISN is written in Fortran and reads either empirically constructed or computational fluid dynamic solutions of the Navier-Stokes equations. A Fortran name list contains the arguments of the model. This includes the type of prediction to conduct, the observer locations, the format of the output, and the jet operating conditions. RISN then evaluates, based on what type of noise is present in the jet (mixing, screech, broadband shock-associated noise), a set of acoustic analogies that correspond to the different components of jet noise. This yields a set of spectral densities per unit frequency at each observer location relative to the nozzle exit; these are summed to form the total noise from the jet. Output files contain the spectral densities, frequencies, non-dimensional frequencies, and decibel levels of each noise component, and the total noise at each observer location.

This work was performed by Steven Miller of Langley Research Center. For more information, contact Langley Research Center at This email address is being protected from spambots. You need JavaScript enabled to view it.. Refer to LAR-18083-1.