Spectrum Analysis of Light Aircraft Wing Vibration Using FFT in Simulated Wind Tunnel Tests

Project info

• Field of studyAerospace and Aviation
• LevelMaster's thesis (S2)
• Method typeSignal Processing
• Estimated build time± 5 working days
• Toolsfft, findpeaks, pwelch (Signal Processing Toolbox)
• PriceUS$28 - US$172

Research goal

Identify the dominant frequency components of wing vibration using FFT and assess the effect of flow turbulence on the spectrum.

Variables

Example result

• 15.137Peak 1 (Hz)
• 0.946Peak 1 amplitude
• 39.063Peak 2 (Hz)
• 0.448Peak 2 amplitude
• 7.707Spectral SNR (dB)
• 215.668Centroid (Hz)

The spectrum of Wing vibration shows two dominant peaks at 15.1 Hz and 39.1 Hz with a spectral signal-to-noise ratio of 7.7 dB, and Flutter frequency and Flow turbulence carry the same frequency components.

Computed from illustrative synthetic data, not from a real study.

About Spectrum Analysis with FFT

The FFT converts a time-domain signal into a frequency spectrum so dominant frequency components can be identified. It is used for vibration, biomedical, audio, and sensor data analysis in engineering theses.

Steps

1. 1Define the sampling frequency and signal length
2. 2Remove the DC component and apply a window if needed
3. 3Compute the FFT and take the one-sided amplitude spectrum
4. 4Build the frequency axis
5. 5Detect peaks with findpeaks
6. 6Compare with a Welch PSD estimate and plot

Assumptions to check

• The signal is sampled above twice its highest frequency (Nyquist)
• The signal is roughly stationary over the analysis window
• Data length is sufficient for the desired frequency resolution

Reading and reporting the results

Report peak frequencies, their amplitudes, and the frequency resolution (fs/N). Explain spectral leakage if present and the window used.

Example MATLAB code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

rng(1);
fs = 1000;
flutter_frequency = (0:1/fs:1-1/fs)';                       % time vector
wing_vibration = sin(2*pi*50*flutter_frequency) + 0.5*sin(2*pi*120*flutter_frequency) + 0.4*randn(size(flutter_frequency));
N = numel(wing_vibration);

yd = wing_vibration - mean(wing_vibration);                        % remove DC
Yf = fft(yd .* hann(N));
P2 = abs(Yf / sum(hann(N)) * 2);
P1 = P2(1:N/2+1);
P1(2:end-1) = 2 * P1(2:end-1);
f = fs * (0:N/2) / N;

[pks, locs] = findpeaks(P1, 'MinPeakHeight', 0.2);
for k = 1:numel(pks)
    fprintf('Peak at %.1f Hz, amplitude %.3f\n', f(locs(k)), pks(k));
end
fprintf('Frequency resolution: %.2f Hz\n', fs / N);

[pxx, fp] = pwelch(yd, hamming(256), 128, 1024, fs);
figure;
subplot(3,1,1); plot(flutter_frequency(1:300), wing_vibration(1:300)); xlabel('Time (s)'); ylabel('Signal');
subplot(3,1,2); plot(f, P1); xlabel('Frequency (Hz)'); ylabel('Amplitude');
subplot(3,1,3); plot(fp, 10*log10(pxx)); xlabel('Frequency (Hz)'); ylabel('PSD (dB/Hz)');

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions