Optimization of digital storage of random analogue data

Bessem, J. M.; Maanen, H.R.E. van

doi:10.1088/0957-0233/5/11/002

Cited by 1 publication

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“…Two different spectra were used as inputs for the simulations. The first data-set to which the algorithm was applied was 'turbulence' generated according to the Bessem power spectrum [3,4,9,19] with cut-off frequencies f 1 = 1 Hz and f 2 = 50 Hz. The record lasted 2000 s (f s = 1000 Hz) which was randomly sampled with a t 0 † of 6 ms.…”

Section: Simulated Turbulence Signalsmentioning

confidence: 99%

Estimation of turbulence power spectra from randomly sampled data by curve-fit to the autocorrelation function applied to laser-Doppler anemometry

Maanen¹,

Oldenziel

1998

Meas. Sci. Technol.

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The development of a novel slotting technique for the estimation of the autocorrelation function (ACF) of randomly sampled data has eliminated the major disadvantages of the Mayo algorithm. The reduced variance of the estimators in the vicinity of τ = 0 and the elimination of estimators greater than 1 has opened the possibility to approximate the ACF with a curve-fit, leading to a noise-free estimator of the power spectrum. In this paper we describe an analytical function, based on known properties of turbulence power spectra, which is suited to the approximation of the ACF from randomly sampled laser-Doppler anemometry (LDA) data with noise. Using simulated LDA data, based on both simulated and hot-wire measured turbulence signals, it is shown that the method estimates the power spectrum with an error of less than 25% over six decades of power. Application to various LDA data sets shows that the analytical function, derived from the spectral properties, is sufficiently flexible to describe ACFs from data sets of pipe flow, mixing layer flow, flow in stirred tanks and flows around airfoils. An extension of the technique could be in automatic incorporation of periodic components and compensation for data sets with a high velocity bias (turbulence intensities > 40%).

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Section: Simulated Turbulence Signalsmentioning

confidence: 99%