Initial fire spread is composed of the processes of ignition, flame spread, and burning rate. The effects of a material's thermal characteristics and burning behaviors on flame spread are important. However, many zone and field models of compartment fire can not predict spread on objects accurately enough due to the neglect of these behaviors in their fire growth sub-models. As a result, a model dedicated to the early stage of fire growth is needed to provide the accuracy necessary for competent assessment of the response of safety systems, as well as satisfying the requirement for a comprehensive risk assessment. This study is undertaken to investigate the use of formulations outlined by previous researchers by review of the theory of flame spread models. A computer model is proposed that can determine the impact of the material properties with emphasis on practical engineering analyses. Through this computer program, we can obtain the pyrolysis zone, the flame height, the burnout time, the burnout portion, the mass loss rate, total heat release rate, and mean flame velocity of a material at specific time. The effort in this study has been focused on developing a relatively simple model for fire spread on a vertically oriented material which contains the most common aspect of fire growth theory such as the transit burning rate, material properties, burner affection, flame spread rate and burnout. This study used Vc++ as a program development platform which has an easy to use interface and reasonable execution times. The model is a combination of two sub-models. One is to simulate the flame spread on horizontal surface. The other is to simulate it on a vertical surface. In two sub-models, the spread process model is two-dimensioned yet symmetric. By using empirical physical equations and correlations, this model predicted flame spread by solving a set of closed coupled correlations simultaneously. Each sub-model contains several functions: ignition, mass loss rate calculation, burning area and the surface temperature calculation. The results of this proposed computer model are compared with experimental studies involving a limited number of comparisons of experimental data for early stage vertical flame spread. The model calculations and experimental measurements of the mass loss rate, heat release rate, and radiation flux were found to be in good agreement. Recommendations are made for further development of the more complex initial stage fire growth model.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: continuous queries; streaming time-series; similarity queries; pattern matching. Includes bibliographical references (p. 58-61).

No description available.

A fire growth calculation method has been developed that couples a computational fluid dynamics (CFD) model with bench scale cone calorimeter test data for predicting the rate of flame spread on compartment contents such as furniture. The commercial CFD code TASCflow has been applied to solve time averaged conservation equations using an algebraic multigrid solver with mass weighted skewed upstream differencing for advection. Closure models include k-epsilon for turbulence, eddy breakup for combustion following a single step irreversible reaction with Arrhenius rate constant, finite difference radiation transfer, and conjugate heat transfer. Radiation properties are determined from concentrations of soot, CO2 and H2O using the narrow band model of Grosshandler and exponential wide band curve fit model of Modak. The growth in pyrolyzing area is predicted by treating flame spread as a series of piloted ignitions based on coupled gas-fluid boundary conditions. The mass loss rate from a given surface element follows the bench scale test data for input to the combustion prediction. The fire growth model has been tested against foam-fabric mattresses and chairs burned in the furniture calorimeter. In general, agreement between model and experiment for peak heat release rate (HRR), time to peak HRR, and total energy lost is within pm 20%. Used as a proxy for the flame spread velocity, the slope of the HRR curve predicted by model agreed with experiment within pm 20% for all but one case.

The probabilistic safety assessment of engineering systems involving high-consequence low-probability events is stochastic in nature due to uncertainties inherent in time to an event. The event could be a failure, repair, maintenance or degradation associated with system ageing. Accurate reliability prediction accounting for these uncertainties is a precursor to considerably good risk assessment model. Stochastic Markov reliability models have been constructed to quantify basic events in a static fault tree analysis as part of the safety assessment process. The models assume that a system transits through various states and that the time spent in a state is statistically random. The system failure probability estimates of these models assuming constant transition rate are extensively utilized in the industry to obtain failure frequency of catastrophic events. An example is core damage frequency in a nuclear power plant where the initiating event is loss of cooling system. However, the assumption of constant state transition rates for analysis of safety critical systems is debatable due to the fact that these rates do not properly account for variability in the time to an event. An ill-consequence of such an assumption is conservative reliability prediction leading to addition of unnecessary redundancies in modified versions of prototype designs, excess spare inventory and an expensive maintenance policy with shorter maintenance intervals. The reason for this discrepancy is that a constant transition rate is always associated with an exponential distribution for the time spent in a state. The subject matter of this thesis is to develop sophisticated mathematical models to improve predictive capabilities that accurately represent reliability of an engineering system. The generalization of the Markov process called the semi-Markov process is a well known stochastic process, yet it is not well explored in the reliability analysis of nuclear power plant systems. The continuous-time, discrete-state semi-Markov process model is a stochastic process model that describes the state transitions through a system of integral equations which can be solved using the trapezoidal rule. The primary objective is to determine the probability of being in each state. This process model ensures that time spent in the states can be represented by a suitable non-exponential distribution thus capturing the variability in the time to event. When exponential distribution is assumed for all the state transitions, the model reduces to the standard Markov model. This thesis illustrates the proposed concepts using basic examples and then develops advanced case studies for nuclear cooling systems, piping systems, digital instrumentation and control (I&C) systems, fire modelling and system maintenance. The first case study on nuclear component cooling water system (NCCW) shows that the proposed technique can be used to solve a fault tree involving redundant repairable components to yield initiating event probability quantifying the loss of cooling system. The time-to-failure of the pump train is assumed to be a Weibull distribution and the resulting system failure probability is validated using a Monte Carlo simulation of the corresponding reliability block diagram. Nuclear piping systems develop flaws, leaks and ruptures due to various underlying damage mechanisms. This thesis presents a general model for evaluating rupture frequencies of such repairable piping systems. The proposed model is able to incorporate the effect of aging related degradation of piping systems. Time dependent rupture frequencies are computed and the influence of inspection intervals on the piping rupture probability is investigated. There is an increasing interest worldwide in the installation of digital instrumentation and control systems in nuclear power plants. The main feedwater valve (MFV) controller system is used for regulating the water level in a steam generator. An existing Markov model in the literature is extended to a semi-Markov model to accurately predict the controller system reliability. The proposed model considers variability in the time to output from the computer to the controller with intrinsic software and mechanical failures. State-of-the-art time-to-flashover fire models used in the nuclear industry are either based on conservative analytical equations or computationally intensive simulation models. The proposed semi-Markov based case study describes an innovative fire growth model that allows prediction of fire development and containment including time to flashover. The model considers variability in time when transiting from one stage of the fire to the other. The proposed model is a reusable framework that can be of importance to product design engineers and fire safety regulators. Operational unavailability is at risk of being over-estimated because of assuming a constant degradation rate in a slowly ageing system. In the last case study, it is justified that variability in time to degradation has a remarkable effect on the choice of an effective maintenance policy. The proposed model is able to accurately predict the optimal maintenance interval assuming a non-exponential time to degradation. Further, the model reduces to a binary state Markov model equivalent to a classic probabilistic risk assessment model if the degradation and maintenance states are eliminated. In summary, variability in time to an event is not properly captured in existing Markov type reliability models though they are stochastic and account for uncertainties. The proposed semi-Markov process models are easy to implement, faster than intensive simulations and accurately model the reliability of engineering systems.

The probabilistic safety assessment of engineering systems involving high-consequence low-probability events is stochastic in nature due to uncertainties inherent in time to an event. The event could be a failure, repair, maintenance or degradation associated with system ageing. Accurate reliability prediction accounting for these uncertainties is a precursor to considerably good risk assessment model. Stochastic Markov reliability models have been constructed to quantify basic events in a static fault tree analysis as part of the safety assessment process. The models assume that a system transits through various states and that the time spent in a state is statistically random. The system failure probability estimates of these models assuming constant transition rate are extensively utilized in the industry to obtain failure frequency of catastrophic events. An example is core damage frequency in a nuclear power plant where the initiating event is loss of cooling system. However, the assumption of constant state transition rates for analysis of safety critical systems is debatable due to the fact that these rates do not properly account for variability in the time to an event. An ill-consequence of such an assumption is conservative reliability prediction leading to addition of unnecessary redundancies in modified versions of prototype designs, excess spare inventory and an expensive maintenance policy with shorter maintenance intervals. The reason for this discrepancy is that a constant transition rate is always associated with an exponential distribution for the time spent in a state. The subject matter of this thesis is to develop sophisticated mathematical models to improve predictive capabilities that accurately represent reliability of an engineering system. The generalization of the Markov process called the semi-Markov process is a well known stochastic process, yet it is not well explored in the reliability analysis of nuclear power plant systems. The continuous-time, discrete-state semi-Markov process model is a stochastic process model that describes the state transitions through a system of integral equations which can be solved using the trapezoidal rule. The primary objective is to determine the probability of being in each state. This process model ensures that time spent in the states can be represented by a suitable non-exponential distribution thus capturing the variability in the time to event. When exponential distribution is assumed for all the state transitions, the model reduces to the standard Markov model. This thesis illustrates the proposed concepts using basic examples and then develops advanced case studies for nuclear cooling systems, piping systems, digital instrumentation and control (I&C) systems, fire modelling and system maintenance. The first case study on nuclear component cooling water system (NCCW) shows that the proposed technique can be used to solve a fault tree involving redundant repairable components to yield initiating event probability quantifying the loss of cooling system. The time-to-failure of the pump train is assumed to be a Weibull distribution and the resulting system failure probability is validated using a Monte Carlo simulation of the corresponding reliability block diagram. Nuclear piping systems develop flaws, leaks and ruptures due to various underlying damage mechanisms. This thesis presents a general model for evaluating rupture frequencies of such repairable piping systems. The proposed model is able to incorporate the effect of aging related degradation of piping systems. Time dependent rupture frequencies are computed and the influence of inspection intervals on the piping rupture probability is investigated. There is an increasing interest worldwide in the installation of digital instrumentation and control systems in nuclear power plants. The main feedwater valve (MFV) controller system is used for regulating the water level in a steam generator. An existing Markov model in the literature is extended to a semi-Markov model to accurately predict the controller system reliability. The proposed model considers variability in the time to output from the computer to the controller with intrinsic software and mechanical failures. State-of-the-art time-to-flashover fire models used in the nuclear industry are either based on conservative analytical equations or computationally intensive simulation models. The proposed semi-Markov based case study describes an innovative fire growth model that allows prediction of fire development and containment including time to flashover. The model considers variability in time when transiting from one stage of the fire to the other. The proposed model is a reusable framework that can be of importance to product design engineers and fire safety regulators. Operational unavailability is at risk of being over-estimated because of assuming a constant degradation rate in a slowly ageing system. In the last case study, it is justified that variability in time to degradation has a remarkable effect on the choice of an effective maintenance policy. The proposed model is able to accurately predict the optimal maintenance interval assuming a non-exponential time to degradation. Further, the model reduces to a binary state Markov model equivalent to a classic probabilistic risk assessment model if the degradation and maintenance states are eliminated. In summary, variability in time to an event is not properly captured in existing Markov type reliability models though they are stochastic and account for uncertainties. The proposed semi-Markov process models are easy to implement, faster than intensive simulations and accurately model the reliability of engineering systems.

Recent technological advances in sensor networks and mobile devices give rise to new challenges in processing of live streams. In particular, time-series sequence matching, namely, the similarity matching of live streams against a set of predefined pattern sequence queries, is an important technology for a broad range of domains that include monitoring the spread of hazardous waste and administering network traffic. In this thesis, I use the time critical application of monitoring of fire growth in an intelligent building as my motivating example. Various measures and algorithms have been established in the current literature for similarity of static time-series data. Matching continuous data poses the following new challenges: 1) fluctuations in stream characteristics, 2) real-time requirements of the application, 3) limited system resources, and, 4) noisy data. Thus the matching techniques proposed for static time-series are mostly not applicable for live stream matching. In this thesis, I propose a new generic framework, henceforth referred to as the n-Snippet Indices Framework (in short, SNIF), for discovering the similarity between a live stream and pattern sequences. The framework is composed of two key phases: (1.) Off-line preprocessing phase: where the pattern sequences are processed offline and stored into an approximate 2-level index structure; and (2.) On-line live stream matching phase: streaming time-series (or the live stream) is on-the-fly matched against the indexed pattern sequences. I introduce the concept of n-Snippets for numeric data as the unit for matching. The insight is to match small snippets of the live stream against prefixes of the patterns and maintain them in succession. Longer the pattern prefixes identified to be similar to the live stream, better the confirmation of the match. Thus, the live stream matching is performed in two levels of matching: bag matching for matching snippets and order checking for maintaining the lengths of the match. I propose four variations of matching algorithms that allow the user the capability to choose between the two conflicting characteristics of result accuracy versus response time. The effectiveness of SNIF to detect patterns has been thoroughly tested through extensive experimental evaluations using the continuous query engine CAPE as platform. The evaluations made use of real datasets from multiple domains, including fire monitoring, chlorine monitoring and sensor networks. Moreover, SNIF is demonstrated to be tolerant to noisy datasets.

No description available.

The Cone Calorimeter has been used widely for various purposes as a bench - scale apparatus. Originally the retainer frame (edge frame) was designed to reduce unrepresentative edge burning of specimens. In general, the frame has been used in most Cone tests without enough understanding of its effect. It is very important to have one - dimensional (1D) conditions in order to estimate thermal properties of materials. It has been implicitly assumed that the heat conduction in the Cone Calorimeter is 1D using the current specimen preparation. However, the assumption has not been corroborated explicitly to date. The first objective of this study was to evaluate the heat transfer behavior of a Cone specimen by examining its three - dimensional (3D) heat conduction. It is essential to understand the role of wall lining materials when they are exposed to a fire from an ignition source. Full - scale test methods permit an assessment of the performance of a wall lining material. Fire growth models have been developed due to the costly expense associated with full - scale testing. The models require heat flux maps from the ignition burner flame as input data. Work to date was impeded by a lack of detailed spatial characterization of the heat flux maps due to the use of limited instrumentation. To increase the power of fire modeling, accurate and detailed heat flux maps from the ignition burner are essential. High level spatial resolution for surface temperature can be provided from an infrared camera. The second objective of this study was to develop a heat flux mapping procedure for a room test burner flame to a wall configuration with surface temperature information taken from an infrared camera. A prototype experiment is performed using the ISO 9705 test burner to demonstrate the developed heat flux mapping procedure. The results of the experiment allow the heat flux and spatial resolutions of the method to be determined and compared to the methods currently available.

An understanding of compartment fire behaviour is important for fire protection engineers. For design purposes, whether to use a prescriptive code or performance based design, life safety and property protection issues are required to be assessed. The use of design fires in computer modelling is the general method to determine fire safety. However these computer models are generally limited to the input of one design fire, with consideration of the complex interaction between fuel packages and the compartment environment being simplified. Of particular interest is the Heat Release Rate, HRR, as this is the commonly prescribed design parameter for fire modelling. If the HRR is not accurate then it can be subsequently argued that the design scenario may be flawed. Therefore the selection of the most appropriate fire design scenario is critical, and an increased level of understanding of compartment behaviour is an invaluable aid to fire engineering assumptions. This thesis details an experimental study to enhance the understanding of the impact and interaction that the size and location of pool fires within an enclosure have upon the compartment fire behaviour. Thirty four experiments were conducted in a reduced scale compartment (½ height) with dimensions of 3.6m long by 2.4m wide by 1.2m high using five typical ventilation geometries (fully open, soffit, door, window and small window). Heptane pool fires were used, located in permutations of three evenly distributed locations within the compartment (rear, centre and front) as well as larger equivalent area pans located only in the centre. This thesis describes the experimental development, setup and results of the experimental study. To assist in the classification of compartment fire behaviour during the experiments, a ‘phi’ meter was developed to measure the time dependent equivalence ratio. The phi meter was developed and configured to measure O₂, CO₂ and CO. The background development, calibration, and experimental results are reported. A review of compartment fire modelling using Fire Dynamics Simulator, has also been completed and the results discussed. The results of this experimental study were found to have significant implications for Fire Safety Engineering in that the size of the fire is not as significant as the location of the fire. The effect of a fire near the vent opening was found to have a significant impact on compartment fire behaviour with the vent located fuel source increasing the total compartment heat release rate by a factor of 1.7 to that of a centrally placed pool fire of the same total fuel area. The assumption that a fire located in the centre of the room provides for the highest heat release rate is not valid for post-flashover compartment fires. The phi meter was found to provide good agreement with the equivalence ratio calculated from total compartment mass loss rates, and the results of FDS modelling indicate that the use of the model in its current form can not be applied to complex pool fire geometries.