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Computational fluid dynamics simulation on the longwall gob breathing 被引量:5
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作者 Samuel A.Lolon Jürgen F.Brune +3 位作者 Gregory E.Bogin Jr. John W.Grubb Saqib A.Saki Aditya Juganda 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2017年第2期185-189,共5页
In longwall mines, atmospheric pressure fluctuations can disturb the pressure balance between the gob and the ventilated working area, resulting in a phenomenon known as ‘‘gob breathing". Gob breathing triggers... In longwall mines, atmospheric pressure fluctuations can disturb the pressure balance between the gob and the ventilated working area, resulting in a phenomenon known as ‘‘gob breathing". Gob breathing triggers gas flows across the gob and the working areas and may result in a condition where an oxygen deficient mixture or a methane accumulation in the gob flows into the face area. Computational Fluid Dynamics(CFDs) modeling was carried out to analyze this phenomenon and its impact on the development of an explosive mixture in a bleeder-ventilated panel scheme. Simulation results indicate that the outgassing and ingassing across the gob and the formation of Explosive Gas Zones(EGZs) are directly affected by atmospheric pressure changes. In the location where methane zones interface with mine air, EGZ fringes may form along the face and in the bleeder entries. These findings help assess the methane ignition and explosion risks associated with fluctuating atmospheric pressures. 展开更多
关键词 CFDs Gob breathing barometric pressure Explosive gas zone Longwall mine Methane explosion
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A Novel Particle Filtering Method for Estimation of Pulse Pressure Variation during Spontaneous Breathing
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《Chinese Journal of Biomedical Engineering(English Edition)》 CSCD 2016年第3期99-99,共1页
The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly ... The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechani-cally ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on sponta-neously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). The performance assessment results of the proposed algorithm on real ABP signals from spontaneously breath-ing subjects were reported. 展开更多
关键词 ABP A Novel Particle Filtering Method for Estimation of Pulse pressure Variation during Spontaneous breathing
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