Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Strategies for Improved Fire Detection Response Times in Aircraft Cargo Compartments(2020) Wood, Jennifer Marie; Milke, James A.; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Prompt fire detection in cargo compartments on board transport aircraft is an important safety feature. Concern has been expressed for the activation time of contemporary detection technologies installed on aircraft. This project will deliver a continuation of research on the issues that have been identified relative to fire detection improvements in cargo compartments on aircraft, with a particular emphasis on freighters. Gas sensors and dual wavelength detectors were demonstrated in a previous phase to be responsive to fires in the previous experiment program. Detectors placed inside a Unit Loading Device (ULD) responded quickly to the array of fire sources. Thus, a further exploration of these observations is conducted including wireless technology along with an analysis of the effects of leakage rates on fire signatures inside ULDs. One primary goal is to assess the differences in fire detection time for detectors located within ULD versus those located on the ceiling of the cargo compartment for fires which originate in a ULD. The results indicated the detector location with the shortest activation time is inside of the ULD. Within the ULD, the wireless detector outperformed both air sampling detectors, however, the results could vary if threshold levels were more restrictive.Item Smoke Characterization of Incipient Fire Sources for FDS Modeling(2008-08-29) Brookman, Matthew James; Mowrer, Frederick; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis describes the experimental and analytical methods used to characterize the heat and smoke release rates of eight different incipient fire sources. These characterizations are part of a larger effort to evaluate the current smoke detection prediction capabilities of the Fire Dynamics Simulator (FDS) version 5.1.0. FDS is a computational fluid dynamics model of fire development based on the concept of large eddy simulation; the FDS model is under ongoing development at the Building and Fire Research Laboratory of the National Institute of Standards and Technology. The experimental aspect of this thesis includes developing a repeatable test protocol and characterizing each of the fuel sources. The experimental data produced from this phase is then input into FDS and the results of these simulations are compared to these experimental data. FDS has provided a range of accuracy near 5 % of the input values for smoke characteristics. The lag times associated with the output data can largely be attributed to the uncorrected experimental data. The time scaled inputs for FDS are based on the time that the instrumentation within the exhaust duct detected the smoke release from the material and the transport time required to move the smoke from the specimen to the instrumentation is not compensated for. Some variations in detection and data acquisition are expected.Item Development of Video Image Detection Algorithm for Smoke Plumes(2007-07-03) Kouchinsky, Alan John; Milke, James A; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The objective of this work is to develop a predictive activation time algorithm for smoke plumes for axonX's SigniFire video image detection (VID) system from recent tests in a large volume space performed at the University of Maryland's (UMD) Cole Field House. The SigniFire system was able to detect a smoke plume from distances of 37.8 m (124 ft) to 54.9 m (180 ft) typically before the smoke reached the ceiling. The goal is to establish an understanding of the significant parameters affecting activation time based on observations and trends from the video image data. As a result of the understanding, insight into a predictive algorithm is developed, which is the first step toward future use of the VID system for a performance based design.