Multidisciplinary Computational Rotor Noise Prediction for Helicopter Rotor in High-speed Forward Flight

摘要

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for computational prediction of rotorcraft noise under a wide range of flight conditions is presented. MUTE is an aggregation of multidisciplinary computational tools to accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD) with high-fidelity wake modeling, Computational Structural Dynamics (CSD), and Acoustic Analysis. Near the rotor blade, the Reynolds Averaged Navier-Stokes (RANS) CFD solver is used to accurately model the physics of source noise around the rotor blade, such as the viscous boundary layer, vortex shedding, and shock effects etc. The complex rotor wake effects are modeled with either fast free-wake method or CFD overset grid method or high-fidelity Particle Vortex Transport Method (PVTM). The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions of rotorcraft are correctly represented in the prediction system. The blade loading information (for impermeable surface method) and/or the flow field parameters around the rotor blade ( for permeable surface method) predicted by the CFD/CSD coupling approach are used to get the acoustic signatures at the far-field observer locations with a high-fidelity noise propagation code, PSU-WOPWOP, which is based on the Ffowcus Williams - Hawkings (FW-H) equation. The validations of the MUTE tool for rotor noise prediction at high-speed forward flight condition are presented here, where the high-speed impulsive noise is dominated. The predicted noise results from both impermeable surface method and permeable surface method are compared with the DNW experimental data set