CCWI2017: F3 'Exploring How Pipe-Level Energy Metrics Relate to Hydraulic Parameters Using Multivariate Analysis'

PCA in this study builds upon a correlation matrix of 11 hydraulic parameters including pipe hydraulic data and pipe-level energy metrics by using Eigen values and Eigen vectors to compress the high- dimensional space of the data into a two-dimensional space. This makes the visualization of the data possible, including all hydraulic parameters simultaneously. The graphical presentation of the hydraulic parameters (mono-plots) and the data set (bi-plots) help which parameters can lead into inefficiency in pipes in the whole dataset. The results show that the metric energy lost to friction in a pipe along with average unit headloss, average flow rate and proximity to major components have a high influence in distinguishing poorly performing pipes from the others. Average pressure and the metric for energy needed by the user for each pipe tend to track closely, despite a lower statistical importance than previous parameters. Diameter and pipe roughness tend to stand alone with poor representations on the two principal component axes.