Fire Protection Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2772
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Item COMPUTATIONAL FLUID DYNAMICS ANALYSIS OF SPATIALLY-RESOLVED SPRAY SCANNING SYSTEM (4S) SPRAY PATTERNS(2023) Bors, Jeffrey; Trouve, Arnaud C; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In computational fluid dynamics (CFD) fire models, sprinkler sprays are represented in complex numerical simulations using Lagrangian particles. These CFD sprays are typically characterized using a combination of experimental data, literature correlations, and estimation. The Spatially-Resolved Spray Scanning System (4S) machine provides high resolution data to characterize sprays for use in CFD analysis, however a quantitative analysis on the effect of this high resolution data with FDS in realistic fire scenarios has not been completed before. 4S spray data is analyzed and compared to a basic spray estimated from literature correlations with and without the presence of fire to analyze trends. In all environments, the basic nozzle overestimated water flux closer to the center of the nozzle and underestimated water flux farther from the center. Differences between the basic and 4S nozzle ranged from 1% to 240% in the enclosure fire scenario. Investigation into the differences showed the polar water distribution to be the most impactful parameter provided by the 4S. Local azimuthal trends were shown to be significant, but non-impactful in the enclosure fire simulation. Global azimuthal trends were apparent but not significant.Item A STUDY OF THE FIRE DYNAMICS SIMULATOR (FDS)- CREATING LIFE-LIKE MOVIES AND STUDYING THE ACCURACY OF THE LAGRANGIAN PARTICLE MODEL(2022) Hussain, Zishanul Haque; Trouve, Arnaud; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fire Dynamic Simulator (FDS) is a computational fluid dynamics (CFD) model of firedrivenfluid flow. It was first released publicly in February 2000. Using SmokeView or Pyrosim to view the results of FDS simulations provides a powerful non-immersive virtual reality experience. It can be used in fire engineering, fire safety training, and fire investigation. By providing a more engaging and interactive user experience, nonimmersive VR can help improve understanding and develop effective fire safety and prevention strategies. On the other hand, FDS is a powerful tool for modeling the physics of fire behavior in buildings and other structures. It has been shown to produce accurate descriptions of fire behavior under a variety of different conditions. This study touches on very divergent, yet very critical, aspects of the applications of FDS. First, generating life-like simulations of fire and smoke characterized by different growth rates and surroundings (a non-immersive virtual reality application). Human behaviour experiments at Morgan State University will use the simulation videos to assess the accuracy of human estimates of fire growth rates and understand how situational factors impact human response. The second part of the study focuses on the Lagrangian particle representation of water droplets in FDS simulations of fire suppression. This study id is going to look at the fire suppression model in which fire suppression is defined by surface wetting or the mass of water falling in the fire surface. The Lagrangian liquid water droplets tracked by FDS represent a larger number of actual droplets. The number of ‘super drops’ can affect the accuracy of the simulations. The particle insertion rate has a default value and controls the mass of the 'super drop'. FDS allows altering the particle insertion rate and hence the mass of the 'super drop. The goal is to find out how changing particle injection rate and mesh grid size impacts the accuracy of the simulation of water sprays.Item Feasibility Analysis of Coupling FDS Modeling with Machine Learning for Situational Awareness in Aircraft Hangars(2022) Davis, Alison Marie; Milke, James A; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Situational awareness is a critical factor in maintaining the safety of firefighters and can be largely improved in buildings using distributed sensors that provide real-time data. A two-phase approach is used to increase situational awareness in aircraft hangars. Phase I consists of modeling a hangar with an open door in Fire Dynamics Simulator (FDS), with a high density of smoke, temperature, CO and CO2 sensors located at the ceiling. Fuels of interest including Douglas fir, polyethylene, paper, JP-8, and propane are modeled in six potential fire locations, with five locations along the centerline of the hangar and one in the corner of the hangar. Additionally, wind and beams at the ceiling are added to the simulation to determine the impact on the products of combustion that the sensors pick up. Phase II uses the data acquired from the FDS simulations to inform and build machine learning models that utilize supervised learning techniques to identify the location of the fire, the magnitude of the fire and the composition of the fuel that is burning. It is determined that temperature and smoke are the key products of combustion needed for these analyses. The location of the fire is identified within a circular area with a 5 m radius by using temperature measurements, thus reducing the amount of input data needed for the machine learning models. The magnitude of the fire is predicted using temperature as inputs to a heat release rate (HRR) model using a fully connected, three-layer, feed forward neural network. The composition of the fuel is predicted using a linear support vector machine that supports multi-class classification, using products of temperature and smoke obscuration as inputs. The location model is 80% accurate, the HRR model is 85% accurate and the fuel composition model varies between 62% and 91% accuracy depending on the classification goals. These results prove the feasibility of machine learning applications in an aircraft hangar setting.Item IDENTIFYING SMOKE DETECTION BIASES WITHIN DIFFERING ROOM CONFIGURATIONS FOR ZONE AND COMPUTATIONAL FLUID DYNAMIC MODELS(2022) Lee, Adam; Milke, James A; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research project aims to identify room configuration conditions in which FDS, a CFD model, and CFAST, a zone model, may differ in detector activation time. A total of four configurations, with varying aspect ratios, were explored. Additionally, a range of four ceiling heights were also modeled. Furthermore, a total of three statistically significant models were developed to relate the differences between detection times within CFAST and FDS. It was found that FDS and CFAST discrepancies were a result of the compartment volume to doorway area ratios. Larger volumes compared to the doorway area resulted in better agreement between FDS and CFAST. Additionally, for larger ceilings in FDS, larger variability in activation times were present. Furthermore, for higher ceilings, FDSs’ ability to account for thermal buoyancy within the smoke plume resulted in quicker activation within FDS.Item Feasibility Analysis and FDS Modeling of Water Mist Fire Suppression Systems for Protection of Aircraft Hangars(2021) Steranka, Karolyn; Milke, James; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Concern about PFAS containing foam fire suppression agents’ negative environmental impact motivated the U.S. Air Force to perform a two-phase feasibility analysis of water mist systems for protection of aircraft hangars. Phase I involved a feasibility analysis of COTS water mist technologies based on manufacturer specifications, literature, and previous test data. Phase I identified seven manufacturers who have developed systems with potential for successful protection of aircraft hangars. Phase II used FDS to model two low pressure and one high pressure system identified in Phase I. Phase II completed an analysis and validation simulations of the Lagrangian particle, extinction, and evaporation model in FDS. Following validation simulations each nozzle was tested in a full-scale hangar configuration for protection of a JP-8 spill fire. The results found the high-pressure mist system was able to extinguish the fire and earlier activation times lead to less damage to the aircraft and hangar compartment.Item VERIFICATION TESTS OF MASS CONSERVATION FOR FIREFOAM AND DEVELOPMENT OF A USER'S GUIDE(2019) Wu, Shiyun; Trouvé, Arnaud; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The objective of this study is to develop basic verification tests for FireFOAM, a large eddy simulation (LES) solver developed by FM Global for fire applications, and based on the general-purpose Computational Fluid Dynamics (CFD) solver called OpenFOAM. These tests will be eventually included in an upcoming User Guide for FireFOAM users. We focus here on a series of tests developed to evaluate global species mass conservation statements. The series includes a two-dimensional helium plume case, a three-dimensional helium plume case and a three-dimensional pool fire case. The two-dimensional helium plume case focuses on the effects of changing the temporal discretization scheme in FireFOAM. The three-dimensional helium plume case focuses on the effects of changing the spatial discretization scheme used to describe the convection terms in the governing equations. Finally, the three-dimensional pool fire case focuses on the effects of changing the number of outer loops used to provide coupling between the governing equations that are solved sequentially. The results of the tests provide valuable insight for FireFOAM users who need to make numerical choices on the temporal discretization scheme, the spatial discretization scheme and the number of outer loops with little guidance on the impact of these choices.