Abstract
Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.
Highlights
Research in field of turbulence has important applications in several fields of engineering
On the other hand Von Karman type models are dependent on the mean value of fluctuating wind speed component and integral length scales that vary with altitude
Several computer simulations and wind tunnel test results have proved that Von Karman model better predicts the turbulence statistics in atmosphere for representative integral length scales (Roberto et al, 2016)
Summary
Research in field of turbulence has important applications in several fields of engineering. Empirical and statistical turbulence models proposed by Kolmogorov (1991), Von Karman (1948), Solari (1987), Simiu (1974), Harris (1971), Davenport (1961), Teunissen (1980), Olesen et al (1984) are based on friction velocity in atmospheric boundary layer. An experimental analysis for in-situ measured wind speed was performed by Soltys et al (2012) using a GILL R3-100 anemometer positioned at ~ 20 m from above ground level (AGL) They found fluctuating component of wind can be quantified by statistical correlation for analyzing spatial-temporal properties in frequency domain by means of wind power spectra density functions relevant to stability of structures. A correlation between turbulence and wind speed fluctuations was established based on the shallow pool experiment data measured for a flat surface above a valley
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