Compute the spectra: Difference between revisions
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To compute the spectrum of the turbulent velocity fluctuations, you need to: | To compute the spectrum of the turbulent velocity fluctuations, you need to: | ||
* Determine appropriate fft-length and overlap | * Determine appropriate [[#fftlength|fft-length]] and their overlap when averaging spectra within each data [[Segmenting datasets|segment]] | ||
* Compute the spectrum | * Compute the spectrum | ||
* Convert the spectrum from the time domain to the space domain using the mean speed past the sensor | * Convert the spectrum from the time domain to the space domain using the mean speed past the sensor | ||
Revision as of 00:32, 11 July 2022
To compute the spectrum of the turbulent velocity fluctuations, you need to:
- Determine appropriate fft-length and their overlap when averaging spectra within each data segment
- Compute the spectrum
- Convert the spectrum from the time domain to the space domain using the mean speed past the sensor
- Compute dof and confidence/significance levels of the final spectra.
Example spectra
The spectrum's lowest resolved frequency and final resolution are the inverse of the fft-length, i.e., the duration of the signal used to construct the spectrum. The spectra are often estimated by block averaging numerous spectra (FFT) estimated from smaller chunks of data within each segment. Another strategy is band-averaging spectra in the frequency domain. The fft-length i.e., the duration of data used to estimate each spectrum, is thus an important quantity that dictates the final range of frequencies resolved by the spectra.
Add an example spectra with fft shown, 2nd axis for frequencies. Another band-averaged spectra?. Remove redundant info from Segmenting datasets
