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After fire is discovered in an underground coal mine, a decision must be made to mitigate fire consequences. The decision should be made based on existing conditions, with the goal of increasing the probability of fire extinguishing without compromising the health and safety of the firefighting personnel. However, the determination of fire conditions can be difficult due to coarse in-situ measurements, fire hazards, and the large domains of interest. Additionally, CFD and network models used for predicting fire conditions are computationally expensive with long simulation processing times for informing real-time decision making. A new generalized procedure to design artificial neural networks (ANNs) capable of making predictions of fire conditions, performing hazard/risk assessment, and providing useful information to the firefighters is presented and applied to different underground coal mine fire scenarios. The feed-forward ANNs were developed to classify fires so as to provide the best firefighting decision and determine useful information in real time, such as response time and fire size. The networks were trained to make predictions on different mine locations and to use only available and measurable information in underground coal mines as inputs. The data used for training and testing the networks was generated using high-fidelity CFD and network fire simulations. Additionally, this research presents the applicability of optical fiber sensing technology for continuous, distributed, and real-time sensing. This new technology could be used for collection of input parameters during ongoing fires, leading to improvement of the prediction performance of the ANNs developed. Finally, a new approach to simulate firefighting foam flow through gob areas is proposed and tested using experimental results obtained from a scaled down experimental setup. / Doctor of Philosophy / Mine fires still represent a serious hazard in underground coal mines. The MSHA incident database shows that around 600 mine fire incidents and 33 fatalities were reported in the U.S. during the last two decades. Most fatalities and injuries that occurred in the aforementioned incidents can be attributed to lack of knowledge on existing fire conditions, leading to poor subjective decisions during fire response. Unfortunately, the in-situ determination or prediction of fire conditions are not easy tasks due to fire hazards, mine entries extensions, and simulation processing times. For this reason, this work presents new data-driven models capable of predicting and evaluating fire conditions. Its goal is to recommend the most suitable firefighting decision, as well as determine fire characteristics and response time to increase the probability of fire extinguishing without compromising mine personnel health and safety. These data-driven models are composed of artificial neural networks (ANNs), allowing for performing predictions in real time and using only available information in underground coal mines. The data used for training and testing these ANNs was generated from fire simulations. Additionally, this research proposes a new technology, such as optical fiber sensing for continuous, distributed, and real time sensing. Optical fiber sensing could contribute with more precise ANNs inputs collection, leading to a better performance prediction. Finally, an alternative way to simulate firefighting foam through gob areas for fire mitigation was proposed and tested using results obtained from experiments. This work represents a significant advancement in underground coal mine fire characterization and response.

Currently, techniques used to determine whether or not there is a concealed fire in an inaccessible area of a coal mine are not definitive. Inaccessible areas of coal mines include: 1. A mined-out area, such as a long-wall gob. 2. A mine area, or entire mine, that has been sealed to extinguish a fire. 3. The interior of pillars in a mine. 4. Abandoned mines. Mined-out areas — gobs — are particularly problematic. The standard practice is to obtain measurements for certain gas concentrations from an inaccessible area, and to apply certain rules to the obtained concentrations in order to try to decipher whether or not there is a fire in the area. Unfortunately, none of the gas measurements, and the associated rules that are applied, are free of potential problems. Therefore, there is always some degree of uncertainty in any decision that is based on the current methods. A more definitive method of determining whether or not a concealed fire exists would be valuable; perhaps avoiding unnecessary exposure of miners to risks, and unnecessary exposure of mining companies to economic loss. This study details the inadequacies of the current methods for determining the presence of a fire in an inaccessible area of a coal mine, and proposes two novel methods for overcoming the current inadequacies. The first method that was studied involves looking for the presence of the radioisotope carbon-fourteen in the carbon monoxide in the return airways of coal mines. For the vast majority of coal mines, if there is no fire anywhere in the coal mine, carbon monoxide should not have any carbon-fourteen in it. If there is a fire, the carbon monoxide should have carbon-fourteen in it. This method is based on the Boudouard Reaction, which documents a reaction between carbon, carbon monoxide, and carbon dioxide that only occurs at temperatures that only occur with a fire. Because of the very small amounts of carbon-fourteen in carbon dioxide in the atmosphere, and the small amount of carbon monoxide usually present in a coal mine atmosphere, there does not appear to be any way, currently, to implement this method. Instrumentation that may allow implementation of this method, in the future, is discussed. The second method, that was studied, involves introducing a select, gaseous, organic compound into an inaccessible area; and then using a gas chromatograph to test for the presence of definitive fire decomposition products of the initial organic compound in the atmosphere that is exiting the inaccessible area. Laboratory tests, conducted as part of this study, established the concept of this novel method of using select, organic compounds for definitively determining whether or not a concealed fire exists in an inaccessible part of a coal mine. Based on an initial screening of 5 different compounds, two compounds have been selected for use as 'fire indicator gases' with the acronym of 'FIGs.' These two compounds are: 1. C6-Perfluoroketone (CF3CF2C(=O)CF(CF3)2 ) 2. 1,1 Difluoroethane (CH3CHF2) This study provides suggestions as to how to look for other potential FIGs, and how to improve the testing of potential FIGs. Examples of all four of the types of inaccessible areas listed above are discussed, particularly from the viewpoint of how FIGs could be utilized in each case, and how FIGs could provide better information in each case. In addition, as a by-product of the experiments conducted for this work, this study identifies at least six gases that might be used simultaneously as tracer gases for complex ventilation studies in a mine, or elsewhere. / Ph. D.

Proper Orthogonal Decomposition (POD), combined with the Method of Snapshots and Galerkin projection, is a popular method for the model order reduction of nonlinear PDEs. The POD requires the left singular vectors from the singular value decomposition (SVD) of an n-by-m "snapshot matrix" S, each column of which represents the computed state of the system at a given time. However, the direct computation of this decomposition can be computationally expensive, particularly for snapshot matrices that are too large to fit in memory. Hierarchical Approximate POD (HAPOD) (Himpe 2016) is a recent method for the approximate truncated SVD that requires only a single pass over S, is easily parallelizable, and can be computationally cheaper than direct SVD, all while guaranteeing the requested accuracy for the resulting basis. This method processes the columns of S in blocks based on a predefined rooted tree of processors, concatenating the outputs from each stage to form the inputs for the next. However, depending on the selected parameter values and the properties of S, the performance of HAPOD may be no better than that of direct SVD. In this work, we numerically explore the parameter values and snapshot matrix properties for which HAPOD is computationally advantageous over the full SVD and compare its performance to that of a parallelized incremental SVD method (Brand 2002, Brand 2003, and Arrighi2015). In particular, in addition to the two major processor tree structures detailed in the initial publication of HAPOD (Himpe2016), we explore the viability of a new structure designed with an MPI implementation in mind. / Master of Science / Singular Value Decomposition (SVD) provides a way to represent numeric data that breaks the data up into its most important components, as well as measuring how significant each part is. This decomposition is widely used to assist in finding patterns in data and making decisions accordingly, or to obtain simple, yet accurate, representations of complex physical processes. Examples of useful data to decompose include the velocity of water flowing past an obstacle in a river, a large collection of images, or user ratings for a large number of movies. However, computing the SVD directly can be computationally expensive, and usually requires repeated access to the entire dataset. As these data sets can be very large, up to hundreds of gigabytes or even several terabytes, storing all of the data in memory at once may be infeasible. Thus, repeated access to the entire dataset requires that the files be read repeatedly from the hard disk, which can make the required computations exceptionally slow. Fortunately, for many applications, only the most important parts of the data are needed, and the rest can be discarded. As a result, several methods have surfaced that can pick out the most important parts of the data while accessing the original data only once, piece by piece, and can be much faster than computing the SVD directly. In addition, the recent bottleneck in individual computer processor speeds has motivated a need for methods that can efficiently run on a large number of processors in parallel. Hierarchical Approximate POD (HAPOD) [1] is a recently-developed method that can efficiently pick out the most important parts of the data while only accessing the original data once, and which is very easy to run in parallel. However, depending on a user-defined algorithm parameter (weight), HAPOD may return more information than is needed to satisfy the requested accuracy, which determines how much data can be discarded. It turns out that the input weights that result in less extra data also result in slower computations and the eventual need for more data to be stored in memory at once. This thesis explores how to choose this input weight to best balance the amount of extra information used with the speed of the method, and also explores how the properties of the data, such as the size of the data or the distribution of levels of significance of each part, impact the effectiveness of HAPOD.

Over the past century, great strides have been made in the advancement of mine fire knowledge since the 1909 Cherry Mine Fire Disaster, one of the worst in U.S. history. However, fire hazards remain omnipresent in underground coal mines in the U.S. and around the world. A precise fire numerical analysis (simulation) before any fire events can give a broad view of the emergency scenarios, leading to improved emergency response, and better health and safety outcomes. However, the simulation cost of precise large complex dynamical systems such as fire in underground mines makes practical and even theoretical application challenging. This work details a novel methodology to reduce fire and airflow simulation costs in order to make simulation of complex systems around fire and mine ventilation systems viable. This study will examine the development of a Reduced Order Model (ROM) to predict the flow field of an underground mine geometry using proper orthogonal decomposition (POD) to reduce the airflow simulation cost in a nonlinear model. ROM proves to be an effective tool for approximating several possible solutions near a known solution, resulting in significant time savings over calculating full solutions and suitable for ensemble calculations. In addition, a novel iterative methodology was developed based on the physics of the fluid structure, turbulent kinetic energy (TKE) of the dynamical system, and the vortex dynamics to determine the interface boundary in multiscale (3D-1D) fire simulations of underground space environments. The proposed methodology was demonstrated to be a useful technique for the determination of near and far fire fields, and could be applied across a broad range of flow simulations and mine geometries. Moreover, this research develops a methodology to analyze the tenable limits in a methane fire event in an underground coal mine for bare-faced miners, mine rescue teams, and fire brigade teams in order to improve safety and training of personnel trained to fight fires. The outcomes of this research are specific to mining although the methods outlined might have broader impacts on the other fields such as tunneling and underground spaces technology, HVAC, and fire protection engineering industries. / Ph. D. / With the rapid advancement of technology, the mine fire knowledge has progressed significantly. Atmospheric monitoring and early sensing of heating has improved; the numerical analysis has been expedited with the usage of supercomputers, and more regulations and standards have been set to increase health and safety of miners. In spite of advancements in these areas, fire hazards remain a critical hazard in underground mines. Developing an emergency plan for the safe escape and for fighting the fire is one of the most important issues during a fire event in underground space environments such as mines. A precise fire numerical analysis (simulation) before any fire events can give a broad view of the emergency situation that leads to improving the health and safety issues in the mining industry. Unfortunately, the precise simulation of the large complex dynamical system such as a fire in underground spaces is costly. This work details a cutting edge approach to reduce the fire and airflow simulation costs in order to make simulation of complex systems around fire and mine ventilation systems viable. The main focus of this proposal is to develop novel methodologies to decrease the time of the fire and airflow simulations. The developed methodologies prove to be useful techniques for the reduction of fire simulation and airflow simulation costs. In addition, this study will examine the development of a comprehensive methodology to analyze the tenable limits in a fire event in an underground coal mine in order to improve safety and training of personnel trained to fight fires. These simulations, applied to training, will result in more efficient evacuations (e.g., the decision to leave can be made quickly and with less delay), as well as safe and effective firefighting under certain situations. The target of this research is specific to mining industry although the methods outlined might have broader impacts on the other fields such as tunneling and underground spaces technology, HVAC, and fire protection engineering industries. Therefore, this research may have an immense contribution on the improvement of health and safety associated with firefighting.