Predicting sprinkler spray dispersion in FireFOAM

Abstract

Accurate representation of fire sprinkler spray enables quantitative engineering analysis of fire suppression performance. Increasingly, fire sprinkler systems are evaluated using computer fire models in which sprinkler spray is simulated with Lagrangian particles. However, limited guidance exists as to how to predict the behavior of complex, spatio-stochastic fire sprinkler spray or how to accurately represent the dispersion of spray in terms of Lagrangian particles. The current work predicts the dispersion of the fire sprinkler spray generated by a canonical axisymmetric sprinkler using a Lagrangian particle tracking model within FireFOAM, an open source computational fluid dynamics fire model. In this work, the initial fire sprinkler spray is described with a new spray injection model characterized by a multivariate probability distribution function related to spatially resolved breakup radius, volume flux, drop size, and drop velocity. This function is stochastically sampled to generate Lagrangian particles representative of the near-field spray and the dispersion of these Lagrangian particles is in turn simulated in FireFOAM to predict the far-field spray. Our new model is compared to a baseline FireFOAM spray injection model which incorporated fewer spray details. Modeled results are compared to highly resolved far-field measurements of axisymmetric sprinkler sprays generated by the Spatially-Resolved Spray Scanning System (4S).