Stochastic and statistical characteristics of artificially generated turbulent flow following karman spectrum in a wind-tunnel experiment

Wind gusts at the pedestrian level around buildings are caused by both effects of turbulent flow generated by surrounding buildings and the turbulent characteristic of the approaching flow. Although numerous researchers have stochastically investigated the contribution of buildings to gusts, the probabilistic characteristics of approaching flow have not been studied adequately. Therefore, the aim of this study was to investigate the statistical quantities such as high-order statistics, extreme wind speeds, and probability density functions (PDFs) of an artificially generated flow according to typical empirical equations. The approaching flow was generated by wind-tunnel experiments. In addition, we propose a PDF based on the Gram–Charlier series (GCS) to describe approaching flow. The determination of high-order statistics showed that these can be used as indices to validate whether the GCS can be applied to the PDFs of the approaching flow. Moreover, the current approaching flow was described effectively by the PDFs based on the GCS by considering the mean, standard deviation, skewness, and kurtosis. Furthermore, the mean, skewness, and kurtosis were correlated strongly with the percentile velocity components. This study demonstrates the importance of considering stochastic information of approaching flow when characterizing urban wind environments.