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A methodology for quantifying flow patterns in a water-table apparatus for naturally ventilated buildings

机译:一种用于量化用于自然通风建筑物的水台装置中的流动模式的方法

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Wind-driven ventilation in buildings is an effective way of diluting the indoor air for maintaining thermal comfort and acceptable indoor air quality. Visualization of the wind-induced flow patterns in a building using a water-table apparatus is a relatively convenient method that produces instantaneous results. The present study focused on the quantification of the flow patterns generated in and around buildings using water-table experiments. The flow patterns were analyzed photographically to derive different ventilation metrics, such as dead spots, ventilation efficiency, etc., which would reveal quantitative information regarding the flow in the different zones of the building. Experiments were conducted using different configurations in order to verify the results obtained with the photographic method. The method used in the present study could be useful in the prediction of flow patterns for complex geometries, which would assist in undertaking early decisions for the proper orientation of a building and its openings. Abbreviations: ACH: Air Changes per Hour (h−1); AS: Room with windows on adjacent walls configuration; AVE: Absolute Ventilation Efficiency; BHK: Bedroom, Hall, Kitchen; C: dye concentration at a point after t, s (g/l); CFD: Computational Fluid Dynamics; Ci: average inside concentration(g/l); Cmax: maximum dye concentration at a point (g/l); Co: initial concentration at a point (g/l); Cs: concentration in the outdoor supply air (g/l); d: characteristic length (mm or m); D: Dining room; dC: change in concentration (g/l); DS: Dead Spots; Ea: Absolute Ventilation Efficiency; fps: Frames per seconds; IAQ: Indoor Air Quality; k reactivity rate (h−1); K: Kitchen; L: Living Room; MATLAB: Matrix Laboratory; OS: Room with windows on opposite walls configuration; P: Dimensionless penetration factor; P1: Passage 1; P2: Passage 2; P3: Passage 3; Pix: Pixel value (px); PNG Portable network graphics; R1: Room 1; R2: Room 2; R3: Room 3; Re: Reynolds Number; RGB: Red, Green, Blue; S: indoor source emission rate (μg/h); SS: Room with single-sided window configuration; T1: Toilet 1; T2: Toilet 2; T3: Toilet 3; U: Utility room; V: Room volume, m3; v: velocity (m/s); VLC: VideoLAN Client
机译:建筑物的风力驱动通风是稀释室内空气以保持热舒适性和可接受的室内空气质量的有效方式。使用水位设备的建筑物中的风引起的流动模式的可视化是一种相对方便的方法,产生瞬间结果。本研究专注于使用水表实验的建筑物中产生的流动模式的量化。摄影地分析了流动模式以导出不同的通风度量,例如死点,通风效率等,这将揭示关于建筑物的不同区域中的流量的定量信息。使用不同的配置进行实验,以验证用摄影方法获得的结果。本研究中使用的方法可用于预测复杂几何形状的流动模式,这将有助于为建筑物及其开口的适当取向进行早期决定。缩写:ACH:每小时空气变化(H-1);如:靠近墙壁配置的窗户的房间; AVE:绝对通风效率; BHK:卧室,大厅,厨房; C:T,S(G / L)后的点状点染料浓度; CFD:计算流体动力学; CI:平均内部浓度(g / l); CMAX:点处的最大染料浓度(g / l); CO:初始浓度在点(g / l); CS:室外供应空气中的浓度(G / L); D:特征长度(mm或m); D:餐厅; DC:浓度变化(G / L); DS:死点; EA:绝对通风效率; FPS:每秒框架; IAQ:室内空气质量; K反应率(H-1); K:厨房; L:客厅; MATLAB:矩阵实验室; OS:带窗户的房间相对的墙壁配置; P:无量纲渗透因子; P1:第1段; P2:第2段; P3:第3段; PIX:像素值(PX); PNG便携式网络图形; R1:1房间; R2:2房间; R3:3房间; Re:Reynolds号码; RGB:红色,绿色,蓝色; S:室内源发射率(μg/ h); SS:房间有单面窗口配置; T1:厕所1; T2:厕所2; T3:厕所3; U:杂物室; V:房间体积,M3; v:速度(m / s); VLC:Videolan客户端

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