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

PFI:AIR - TT：优化灭火系统性能的新型平台

• 批准号: 1701154
• 负责人: James Milke
• 金额: $ 20万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701154&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Novel; Platform

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.

该PFI：AIR技术翻译项目的重点是将喷雾测量和分析方面的最新发现转化为改进灭火系统设计和提高公共安全。 灭火知识的差距可能导致设计保守，不仅限制了灭火系统的性能，而且限制了建筑物的整体性能的形式，功能和成本。拟议的平台提供了一个前所未有的能力来评估喷头的性能，通过整合喷雾测量和分析的最新发现与建筑师和工程师使用的越来越流行的建筑信息建模（BIM）环境，以协调各种各样的设计，工程，施工，甚至检查活动。 建筑信息建模已经用于传达消防安装细节，并评估水力计算，确保在发生火灾时以足够的压力将水输送到喷头。然而，这些模型达不到实际喷雾分散和与建筑环境的相关相互作用的功能性能预测。拟议的BIM插件将拥有足够的保真度，为基于性能的设计的自动喷水灭火系统，提供严格的分析和系统优化所需的工具。与现有的计算流体动力学框架不同，将这种喷雾分析功能集成到BIM框架中，开辟了一个独特的方便、快速和精确的动态功能分析的新世界。 此外，在流行的、可用的、方便的工程平台（如BIM）上实现这种喷雾分析能力，预计将促进利益相关者的广泛采用，建立市场可行性，并探索全方位的用例。应该指出的是，即使在使用100年后，有关消防喷淋系统的基本工程问题仍然没有答案。虽然新的设计是常规构思，没有预测能力可用于回答喷雾成分的基本问题（即什么是喷雾？）和分散（即它去哪里？），这阻碍了创新。虽然已知喷头喷洒模式细节（例如，动量、体积通量和液滴大小的空间随机分布）控制灭火性能，但是对于给定的喷头类型，设计过程中的喷洒描述的保真度不超过全局覆盖区域规范。相比之下，所提出的技术试图捕获所有喷雾细节以完整地描述其组成。具体而言，所提出的技术提供了一个复杂的空间随机初始喷雾表示，由每个喷头模型唯一地生成，由激光诊断捕获，其特征在于在概率分析框架方面，并记录到喷头数据库中。 该虚拟喷雾表示用于通知集成到BIM框架中的新型计算高效轨迹分析，以提供建筑物表面上喷雾图案的高保真超实时预测。这些喷雾模式为自动喷水灭火系统设计人员提供了有用的信息，以确定灭火系统的安装细节。 除了从这个项目预期的这些技术进步，本科生和研究生将获得宝贵的创业和技术开发经验，在由工程师，计算机科学家，大学生和教师组成的团队工作。 工业和大学合作者团队将利用大学最近开发的专利技术，同时接触包括建筑设计师，工程师和建筑师在内的潜在客户，以促进采用新的灭火设计技术。