2012: Masters Project

After completion of my Bachelor’s Degree in Mechanical Engineering in December 2010, I worked for 6 months and then started my Master’s Degree in Mechanical Engineering while continuing working for the government.

My Masters project was within the domain of acoustics and titled: Characterisation of noise in homes affected by wind turbine noise. The project lasted a year and a conference paper with the same name was submitted and accepted to the Australasian Acoustic Society in Fremantle (2012).

A link to the journal paper can be found here: HERE

Wind Farm
Parachina Wind Farm

The tasks that were completed during this project were:

  • Initial meeting with resident to understand the problem. The problem was described as an intermittent noise depending on certain meteorological conditions that manifests itself as headaches and sleepless nights. The resident described the noise as a pounding within the head
  • Design a methodology for testing for low frequency noise within the resident’s home. The methodology must be repeatable and give consistant results.
  • Augmented physical system consisting of previous hardware to test methodology using a data acquisition device, computer, microphone and amplifier and coded a program to facilitate the sound recording. The system used a Brüel & Kjær 4958 – 20 kHz precision array microphone, 4mA microphone signal conditioner, The Krohn-Hite Model 3362 Dual Channel Filter with 50dB gain and a LabJack U3-HV
Equipment Setup
Equipment Setup
  • Write a computer program to initiate recording of the sound and also alow the resident to select an annoyance rating as well as any aditional data
Program GUI
Program GUI
Program GUI Flowchart
Program GUI Flowchart
  • Write software in matlab to perform an analysis of the data and determine if any correlation between an annoyance rating as well as sound power spectrum density exists.
pref = 20*10^-6;
%% Unfiltered Results
[PSDu,fu] = pwelch(data(:,2),fs/2,[],samples,fs);  % find spectrum
PSD1u = 10*log10(PSDu/pref^2); % power spectral density
calibdatau= 0.5411*data(:,2);
[PSD2u,f2u] = pwelch(calibdatau,fs/2,[],samples,fs);  % find spectrum
PSD3u = 10*log10(PSD2u/pref^2); % power spectral density
%dB unfiltered calibrated under PSD
%10 to 30Hz
lb = 10;
ub = 30;
  • Working in tandem, the device above was able to measure the power within the 10 to 30Hz spectrum and determine that there seems to be a relation to power within the 10 to 30Hz spectrum and annoyance and that more research within this area is needed. The methodology was tested and verified within a residence and discovered that low frequency noise from wind turbines may be a problem
Annoyance versus dB
Annoyance versus sound pressure level
Measured Modulation in the 10-30Hz band
Measured Modulation in the 10-30Hz band
Power Spectrum Density