FRPM/FMCAS Application for Prediction of Wind Turbine Noise

Kurzfassung

The wind energy industry is moving towards larger rotor blades. Larger rotors will experience more substantial and more dynamic loads due to the fluctuating and heterogeneous wind field. The project SmartBlades 2.0 investigates rotor blade design concepts that alleviate aerodynamic loading using active and passive mechanisms. As part of the project, acoustic evaluation is embedded in the design process. The present work evaluates two concepts for load alleviation separately, an inboard slat and an outboard flap, using FRPM/FMCAS (Fast Random Particle Mesh/Fast Multipole Code for Acoustic Shielding) numerical prediction toolchain developed at DLR. The numerical tools are validated using the experimental results of an active trailing-edge of small-scale model measured in the Acoustic Wind tunnel Braunschweig (AWB). The active trailing-edge, designed as a plain flap, generates flap side-edge noise. The FRPM/FMCAS tool was able to produce a comparable sound spectrum for the negative flap deflection. Whereas, for the positive flap deflection, the experimental result is obfuscated by the adverse pressure gradient effect. The self-noise from a slat at the inboard section of a rotor blade with a 44.45 m radius was investigated and compared with that from the outboard trailing-edge using FRPM/FMCAS. Furthermore, the rotational effect of the rotor was included in the post-processing to emulate the noise observed at ground level. The findings show an increase in the slat’s overall sound pressure level and a maximum radiation upwind of the wind turbine for the case with the largest wind speed that represents the off-design condition. In operational conditions, the slat adds at most 2 dB to the overall sound pressure level. The toolchain evaluates wind turbine noise with conventional or unconventional blade design, and the problem can be scaled up for a full-scale analysis. As such, the tools presented can be used to design low-noise wind turbines efficiently.