Applying large-scale PIV to water monitor discharge experiment

Abstract Industrial fires are undeniably the most dangerous type of fires for firefighters, and the master stream from water monitor is one of the safest means to deploy defensive or offensive strategies against such fires. However, few studies pay attention to the efficacy of the master stream. To address this issue, we extracted and analyzed numerical data from physical master streams. The proposed methods comprising of digital image processing (DIP) and large-scale particle image velocimetry (LS-PIV), which were conducted in the full-scale discharge in order to obtain coordinate trajectory pattern as well as the instantaneous and mean vector-velocity fields. The flow vectors in the different zones of the master stream were compared using KAZE feature detection. This study thus offers more extensive and detailed experimental data to validate computational fluid dynamics (CFD) simulations and opens an avenue for future fire-safety research.