PFI:AIR - TT: A Novel Platform for Optimizing Fire Suppression System Performance

University Of Maryland, College Park, College Park MD

Investigators

Abstract

This PFI: AIR Technology Translation project focuses on translating recent discoveries in spray measurement and analysis to improve fire suppression system designs and enhance public safety. Fire suppression knowledge gaps can result in design conservatism that not only limits fire suppression system performance but also limits the overall building performance in form, function, and cost. The proposed platform provides an unprecedented capability to evaluate sprinkler performance by integrating recent discoveries in spray measurements and analysis with the increasingly popular Building Information Modeling (BIM) environment used by architects and engineers to coordinate a wide variety of design, engineering, construction, and even inspection activities. Building Information Modeling is already used to communicate fire protection installation details and to evaluate hydraulic calculations ensuring water is delivered at sufficient pressure to the sprinkler head in the event of a fire. However, these models fall short of functional performance predictions of the actual spray dispersion and associated interactions with the built environment. The proposed BIM plugin would possess sufficient fidelity for performance-based design of sprinkler systems by providing the tools needed for rigorous analysis and system optimization. Distinct from the available computational fluid dynamics framework, integrating this spray analysis capability within the BIM framework opens a new world of dynamic functional analysis that is uniquely convenient, fast, and precise. Further, implementation of this spray analysis capability on a popular, usable, convenient engineering platform such as BIM is expected to facilitate widespread stakeholder adoption, establishment of market viability, and exploration of the full spectrum of use cases. It should be noted that basic engineering questions regarding fire sprinkler systems remain unanswered even after 100 years of use. While new designs are routinely conceived, no predictive capability is available to answer essential questions of spray composition (i.e. what is the spray?) and dispersion (i.e. where does it go?), which impedes innovation. While the sprinkler spray pattern details (e.g. spatio-stochastic distributions of momentum, volume flux, and drop size) are known to govern fire suppression performance, the fidelity of the spray descriptions in the design process do not reach beyond global coverage area specifications for a given sprinkler type. In contrast, this proposed technology seeks to capture all of the spray details to completely describe its composition. Specifically, the proposed technology provides a complex spatio-stochastic initial spray representation, generated uniquely by each sprinkler model, captured by laser diagnostics, characterized in terms of a probabilistic analytical framework, and recorded into a sprinkler database. This virtual spray representation is used to inform a novel computational efficient trajectory analysis integrated into the BIM framework to provide high fidelity super-real time predictions of spray patterns on building surfaces. These spray patterns provide useful information for sprinkler system designers to determine installation details for the suppression system. In addition to these technical advancements expected from this project, undergraduate and graduate students will receive valuable entrepreneurship and technology development experience working on a team consisting of engineers, computer scientists, university students, and faculty. The team of industrial and university collaborators will work with recently patented technology developed at the university while reaching out to potential customers including building designers, engineers, and architects to promote adoption of the new fire suppression design technology.