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In recent years, there have been reports of gas leakage accidents during the transportation of gas tankers in tunnels, potentially leading to destructive explosions. However, the overall distribution of gas concentration in the entire tunnel has not been experimentally investigated. This study presents a series of brine-water experiments to explore the characteristics of buoyant gas flow in tunnel leakage accidents. The gas distribution is categorized into a stably stratified longitudinal current and a source region. Gas concentration in the tunnel is correlated with the dimensionless reduced gravity, which increases with the source buoyancy flux but decreases with the source release velocity. A model is proposed to estimate the maximum gas concentration of the longitudinal current. The spatial range of the explosive region is influenced by the source buoyancy flux and the leakage velocity. However, the propagation velocity of the longitudinal current is primarily controlled by the source buoyancy flux, with the source momentum having a negligible effect. The thickness of the longitudinal gas flow, δ, linearly decreases from the source region to the tunnel ends. A dimensionless parameter, λ, is introduced to characterize δ. When λ < 1.25, δ rapidly increases with λ and then remains approximately constant when λ exceeds 1.25. This study contributes to a better understanding of buoyant gas flow in tunnel leakage accidents. [ABSTRACT FROM AUTHOR]

Four-dimensional flow magnetic resonance imaging (4D flow MRI) offers a powerful tool for visualizing fluid flows, critical for both diagnosing cardiovascular diseases and analyzing engineering fluid dynamics. Despite its potential in medical research, the clinical applicability of 4D flow MRI often faces challenges due to inherent noise. To mitigate this, we introduce the split-and-overlap singular value decomposition (SOSVD) filter, a distinctive noise reduction approach. Unlike traditional singular value decomposition methods, the SOSVD filter partitions the primary data matrix into overlapping subdomains and then applies singular value decomposition to each subdomain, preserving only the dominant mode for noise attenuation. Evaluations on simulated and experimental flow data within a square duct revealed a significant decrease in root mean square noise metrics. Moreover, when applied to in vivo aortic data, the SOSVD filter enhanced various flow determinants, including divergence, velocity gradients, streamlines, and velocity coherence. Thus, the SOSVD method presents a promising avenue for augmenting noise reduction in 4D flow MRI, potentially elevating diagnostic accuracy and enriching cardiovascular disease research. [ABSTRACT FROM AUTHOR]