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i

NEW IMPROVEMENTS TO MFIRE TO ENHANCE FIRE MODELING CAPABILITIES

Filetype[PDF-246.86 KB]


  • English

  • Details:

    • Alternative Title:
      Min Eng
    • Description:
      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 MFIRE's outdated FORTRAN programming language was replaced with an object-oriented C++ language and packaged into a dynamic link library (DLL).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.
    • Pubmed ID:
      27375301
    • Pubmed Central ID:
      PMC4928856
    • Document Type:
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