Multivariate directed weighted complex network for characterizing 3D wind speed signals in indoor and outdoor environments

Characterizing the time series measured from different flow environments is of great importance in diverse research fields. In this paper, we first systematically record three groups of 3D wind speed data from indoor and outdoor environments separately. Then, we employ a modality transition-based approach for mapping the experimental multivariate data into a directed weighted complex network. For each generated network, we extract weighted shortest path and closeness centrality to quantitatively characterize the topological properties. The results show that the generated weighted complex networks associated with indoor and outdoor environments exhibit distinct topological structures and the network characteristics, i.e., weighted shortest path and closeness centrality are very sensitive to the changes of airflow conditions. These interesting findings suggest that the proposed multivariate directed weighted complex network not only allows quantitatively distinguishing different airflow behaviors, but also yields deep insights into the nonlinear dynamical mechanisms underlying the wind speed time series.