Improvement of a mine fire simulation program — incorporation of smoke rollback into MFIRE 3.0

Zhou, Lihong; Smith, Alex C.

doi:10.1177/0734904111418483

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…Sufficient research has been done to test each improved feature and included into a couple of previous publications (Zhou, 2009; Zhou and Luo, 2010, 2011; Zhou and Smith, 2012). …”

Section: Discussionmentioning

confidence: 99%

“…However, the ability to predict smoke rollback can greatly improve the chances for safe miner evacuation and mine fire control and firefighting. In Zhou and Smith's (2012) research, a smoke rollback identification equation was incorporated into MFIRE 3.0, making it possible to recognize smoke rollback and calculate the smoke rollback distance. The main program of MFIRE calls the newly added smoke rollback function to calculate the critical velocity and compares the actual velocity to the critical velocity.…”

Section: Smoke Rollback Identification and Predictionmentioning

confidence: 99%

“…More details about this study can be found in Zhou and Smith's (2012). The smoke rollback identification and rollback length estimation have been included in MFIRE 3.1.…”

Section: Smoke Rollback Identification and Predictionmentioning

confidence: 99%

“…Zhou and Luo's improvements on the fire modeling were programmed into a new version of MFIRE termed as MFIRE 2.30 (Zhou and Luo, 2011). After the release of MFIRE 3.0 by NIOSH in 2012, Zhou and Smith (2012) made the improvement to MFIRE3.0 by adding a module to identify and predict smoke rollback phenomena. Unlike all the MFIRE versions prior to 2.20, MFIRE 2.30 and MFIRE 3.0 were both written with Visual Studio C++ language.…”

Section: Introductionmentioning

confidence: 99%

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New improvements to MFIRE to enhance fire-modeling capabilities

Zhou¹,

Smith²,

Yuan³

2016

Mining Engineering

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NIOSH's mine fire simulation program, MFIRE, is widely accepted as a standard for assessing and predicting the impact of a fire on the mine ventilation system and the spread of fire contaminants in coal and metal/nonmetal mines, which has been used by U.S. and international companies to simulate fires for planning and response purposes. MFIRE is a dynamic, transient-state, mine ventilation network simulation program that performs normal planning calculations. It can also be used to analyze ventilation networks under thermal and mechanical influence such as changes in ventilation parameters, external influences such as changes in temperature, and internal influences such as a fire. The program output can be used to analyze the effects of these influences on the ventilation system. Since its original development by Michigan Technological University for the Bureau of Mines in the 1970s, several updates have been released over the years. In 2012, NIOSH completed a major redesign and restructuring of the program with the release of MFIRE 3.0. MFIRE's outdated FORTRAN programming language was replaced with an object-oriented C++ language and packaged into a dynamic link library (DLL). However, the MFIRE 3.0 release made no attempt to change or improve the fire modeling algorithms inherited from its previous version, MFIRE 2.20. This paper reports on improvements that have been made to the fire modeling capabilities of MFIRE 3.0 since its release. These improvements include the addition of fire source models of the t-squared fire and heat release rate curve data file, the addition of a moving fire source for conveyor belt fire simulations, improvement of the fire location algorithm, and the identification and prediction of smoke rollback phenomena. All the improvements discussed in this paper will be termed as MFIRE 3.1 and released by NIOSH in the near future.

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“…Sufficient research has been done to test each improved feature and included into a couple of previous publications (Zhou, 2009; Zhou and Luo, 2010, 2011; Zhou and Smith, 2012). …”

Section: Discussionmentioning

confidence: 99%

Section: Smoke Rollback Identification and Predictionmentioning

confidence: 99%

“…More details about this study can be found in Zhou and Smith's (2012). The smoke rollback identification and rollback length estimation have been included in MFIRE 3.1.…”

Section: Smoke Rollback Identification and Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New improvements to MFIRE to enhance fire-modeling capabilities

Zhou¹,

Smith²,

Yuan³

2016

Mining Engineering

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“…MFIRE 3.0 has been integrated into several major mine ventilation network simulation programs such as VnetPC, ICAMPS, VentSim, and VUMA. A new mine fire simulation feature was also added to MFIRE to predict smoke rollback in a mine fire [13]. …”

Section: Mfire Simulationmentioning

confidence: 99%

Modeling carbon monoxide spread in underground mine fires

Yuan

Zhou

Smith

2016

Applied Thermal Engineering

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Carbon monoxide (CO) poisoning is a leading cause of mine fire fatalities in underground mines. To reduce the hazard of CO poisoning in underground mines, it is important to accurately predict the spread of CO in underground mine entries when a fire occurs. This paper presents a study on modeling CO spread in underground mine fires using both the Fire Dynamics Simulator (FDS) and the MFIRE programs. The FDS model simulating part of the mine ventilation network was calibrated using CO concentration data from full-scale mine fire tests. The model was then used to investigate the effect of airflow leakage on CO concentration reduction in the mine entries. The inflow of fresh air at the leakage location was found to cause significant CO reduction. MFIRE simulation was conducted to predict the CO spread in the entire mine ventilation network using both a constant heat release rate and a dynamic fire source created from FDS. The results from both FDS and MFIRE simulations are compared and the implications of the improved MFIRE capability are discussed.

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