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Fourier Analyses. Time series. Sampling interval. Total period. Question: How perturbations with different frequencies contribute to the turbulent kinetic energy?. Decomposition illustration. Fourier Transform. a. What is a Fourier Series?.
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Fourier Analyses Time series Sampling interval Total period Question: How perturbations with different frequencies contribute to the turbulent kinetic energy?
Fourier Transform a. What is a Fourier Series? Decompose a single using a series of sine and cos waves Time-Amplitude domain Frequency-Amplitude domain 10Hz 50Hz 3Hz
Discrete Fourier Transform Observations: N Sampling interval: Period First harmonic frequency: nth harmonic frequency: All frequency: time at kth observation:
Time-Amplitude domain Frequency-Amplitude domain If time series F(k) is known, then, the coefficient c(n) can be found as: Forward Transform:
Example: Index (k): 0 1 2 3 4 5 6 7 Time (UTC): 1200 1215 1230 1245 1300 1315 1330 1345 Q(g/kg): 8 9 9 6 10 3 5 6 n 0 1 2 3 4 5 6 7 c(n) 7.0 0.28-1.03i 0.5 -0.78-0.03i 1.0 -0.78+0.03i 0.5 0.28+1.03i For frequencies greater than 4, the Fourier transform is just the complex Conjugate of the frequencies less than 4.
c(0) =7.0 c(1)=0.28-1.03i c(2)=0.5 c(3)=-0.78-0.03i c(4)=1.0 c(5)=-0.78+0.03i c(6)=0.5 c(7)=0.28+1.03i
Aliasing We have ten observations (10 samples) in a second and two different sinusoids that could have produced the samples. Red sinusoidhas 9 cycles spanning 10samples, so the frequency Blue sinusoidhas 1 cycle spanning 10samples, so the frequency Which one is right? Two-point rule Two data points are required per period to determine a wave. 4 observations: 2 waves 2 observations: 1 wave If sampling rate is , the highest wave frequency can be resolved is , which is called Nyquist frequency
Folding Folding occurs at Nyquist frequency. What problem does folding cause? What will cause aliasing or folding? • The sensor can respond to frequencies higher than the rate that the sensor is sampled. • The true signal has frequencies higher than the sampling rate.
Fast Fourier Transform (FFT) FFT is nothing more than a discrete Fourier transform that has been restructured to take advantage of the binary computation processes of digital computer. As a result, everything is the same but faster! Relationship between decimal and binary numbers 0 1 2 3 4 5 6 7 8 9 10 0 1 10 11 100 101 110 111 1000 1001 1010 The decimal numbers n and k can be represented by If N=8, then, j=0, 1, 2, 3 7: binary 1 1 1; 5: binary 101; 3: binary 11
Energy Spectrum Note that n starts from 1, because the mean (n=0) does not contribute any information about the variation of the signal. For frequencies higher than Nyquist frequency, values are identically equal to those at the lower frequencies. They are folded back and added to the lower frequencies. Discrete spectral intensity (or energy)
Example Index (k): 0 1 2 3 4 5 6 7 Time (UTC): 1200 1215 1230 1245 1300 1315 1330 1345 Q(g/kg): 8 9 9 6 10 3 5 6