Auralisation of Modelled Wind Turbine Noise for Psychoacoustic Listening Experiments

Abstract

Wind turbine noise is one of the grand challenges in the public acceptance of onshore wind farm projects. The field of psychoacoustics identifies the auralisation of wind turbine noise as a link between technical design and annoyance estimation. There is currently limited work on the auralisation of wind turbine noise, and none targets an application in psychoacoustic research.

This work investigates the auralisation of the aeroacoustics output of DTU's HAWC2 for use in annoyance estimation. A Gaussian beam tracing approach propagates the frequency domain output to observer locations. The resulting spectrograms are converted into sound signals by applying random phase and the inverse short-time Fourier transform. This work includes a binaural rendering module to enable future VR applications. The methodology's implementation results in the Wind Turbine Auralisation tool, WinTAur.

The noise signal output of WinTAur is validated using the HAWC2 model of a stall-controlled NTK 500/41 wind turbine and corresponding acoustic field measurements. Psychoacoustic sound quality metrics show significant differences between the auralised and measured noise. In the overall psychoacoustic annoyance metric, these differences mainly depend on the observer's position around the turbine. All metrics show this directionality dependence, while the loudness, sharpness and tonality metrics also indicate a dependence on wind speed. Differences in fluctuation strength show a minor dependence on the simulation case but are difficult to relate to a specific simulation parameter.
Spectral analysis of the simulation output samples reflects the limitations of HAWC2, demonstrating that it is the primary source of discrepancy. The analysis especially highlights the inaccurate prediction of the directionality and stall noise of the HAWC2 code. The choice of ground type is another probable source of discrepancy, as it does not accurately represent the measurement setup.
A subjective listening experiment demonstrates the significance of these discrepancies in human perception with generally high difference ratings between the simulated and recorded noise. The results illustrate a dependence on wind speed and the position around the turbine. These dependencies match well with the findings from the numerical validation.\\

Future work should focus on a sensitivity analysis of WinTAur since the case-independent parameters may be additional sources of discrepancy. Another recommendation is to investigate the unveiled errors in the underlying methodology. Lastly, better propagation modelling concerning the wind turbine wake and turbulence should be part of future wind turbine noise modelling.

Overall, using modelled wind turbine noise for the auralisation in psychoacoustic research has shown promising results. Validation with sound quality metrics provides good insights into the discrepancies found in subjective listening experiments. Eliminating the existing discrepancies through modelling improvements will allow this work to be applied in a fully modelled approach to estimate wind turbine noise annoyance.