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首页> 外文期刊>Boundary-layer Meteorology >Using Large-Eddy Simulation and Wind-Tunnel Data to Investigate Peak-to-Mean Concentration Ratios in an Urban Environment
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Using Large-Eddy Simulation and Wind-Tunnel Data to Investigate Peak-to-Mean Concentration Ratios in an Urban Environment

机译:使用大涡模拟和风洞数据来研究城市环境中的峰值浓度比

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The estimation of short-term-averaged maximum concentration is of foremost importance, for instance, for the impact assessment of odorant sources, flammable gases and the accidental or intentional release of toxic gases. As dispersion models only give 1-h averaged concentration values, a simple formulation (the power-law function) has been widely used in practical applications to overcome this limitation. The present study investigates the potential of large-eddy simulation (LES) to assess the influence of turbulent eddies on averaged concentration over short time intervals and, thus, on dispersion within a building array, with LES results compared with wind-tunnel data. The results indicate that the LES approach underpredicts the concentration fluctuation intensities governed by the smaller eddy motions and we conclude, not surprisingly, that the particular choice of subgrid-scale model and grid size is important in describing the smallest wavelength concentration motions. However, even though the LES results are not able to predict peak-to-mean values for very short averaging times, the fit of the power-law function can be extrapolated to produce a valid relation for shorter averaging times, implying the LES technique can be used to assess the p value (the exponent) in the commonly-used power-law function. This is found to be smaller (by about one half) for sensors in the central position within the array than for those located in short streets or at intersections, and it also decreases more slowly with distance from the source. No substantial difference is found between sensors located at the canopy height H and at half the canopy height, i.e. within the canopy. In contrast, there is a significant difference for sensors located above the building height at 1.5H.
机译:例如,对短期平均最大浓度的估计可能是对气味来源,易燃气体和意外或有意释放有毒气体的影响评估的重要性。由于分散模型仅提供1-H平均浓度值,这是一种简单的配方(Power-Law功能)已被广泛用于实际应用中以克服这种限制。本研究调查了大涡模拟(LES)的潜力,以评估湍流射精对短时间间隔的平均浓度的影响,因此在建筑物阵列内的分散,与风隧道数据相比,LES结果。结果表明,LES方法欠下较小的涡动术治理的浓度波动强度,并且我们得出结论,尚不奇怪,底片标度模型和电网尺寸的特殊选择对于描述最小的波长浓度运动很重要。然而,即使LES结果无法预测非常短的平均时间的峰值达到的值,可以推断出电源律函数的拟合以产生对较短平均时间的有效关系,这意味着LES技术可以用于评估常用的幂律函数中的P值(指数)。对于在阵列内的中心位置的传感器中,该传感器的传感器比对于位于短街道或交叉点的那些,这是较小的(大约一半),并且在距离源的距离也会变得更加缓慢。在位于树冠高度H和半顶部的半顶部的传感器之间没有发现大量差异,即在树冠内。相比之下,位于1.5h的建筑物高度上方的传感器存在显着差异。

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