Risk assessment, city planning, and emergency response are a few examples of potential applications of numerical simulations of scalar dispersion in urban environments. The complex flow fields and scalar dispersion are determined by the building layout and prevailing meteorological conditions that are highly uncertain. While the fidelity of a numerical model is important in providing an accurate prediction of flow and scalar fields, propagating the uncertain input through numerical models is imperative in those applications. However, it is uncommon to quantify input uncertainties due to expensive computational cost of high fidelity simulations such as large eddy simulations (LES) and Reynolds-averaged Navier–Stokes (RANS). In this work, the uncertain meteorological quantities viz., wind speed and its direction from field measurements are taken as inputs to RANS simulations that use realizable \(k-\varepsilon\) turbulence model, to investigate their effects on passive scalar dispersion in central London. The mean wind and scalar quantities from RANS are initially validated with wind-tunnel data and compared to large eddy simulations (LES). For comparison with field measurements, the deduced probability density function (pdf) for wind speed and direction from the field are used as inputs for RANS simulations. For a 3-min averaged concentration at a specific receiver location, LES with unsteady wind inputs showed better performance than LES with mean wind input and RANS whereas for 30-min averaged concentration at various receiver locations, performance measures indicated that RANS is better than LES. The latter certainly suggests the importance of considering such uncertainties. The flow variability in every street is quantified using RANS simulations. This demonstrated that approximations used in a fast, low-order street network model may not be necessarily valid for every street of heterogeneous urban canopies, which in turn affects scalar prediction.

风险评估、城市规划和应急响应是标量扩散数值模拟在城市环境中的潜在应用的几个例子。复杂的流场和标量分散是由高度不确定的建筑物布局和主要气象条件决定的。虽然数值模型的保真度对于提供流场和标量场的准确预测很重要，但在这些应用中必须通过数值模型传播不确定输入。然而，由于大涡模拟 (LES) 和雷诺平均纳维-斯托克斯 (RANS) 等高保真模拟的昂贵计算成本，量化输入不确定性并不常见。在这项工作中，不确定的气象量即，\(k-\varepsilon\)湍流模型，以研究它们对伦敦市中心被动标量扩散的影响。来自 RANS 的平均风和标量最初使用风洞数据进行验证，并与大涡模拟 (LES) 进行比较。为了与现场测量进行比较，从现场推导出的风速和风向的概率密度函数 (pdf) 被用作 RANS 模拟的输入。对于特定接收器位置的 3 分钟平均浓度，具有不稳定风输入的 LES 表现出比具有平均风输入和 RANS 的 LES 更好的性能，而对于不同接收器位置的 30 分钟平均浓度，性能测量表明 RANS 优于 LES . 后者肯定表明考虑此类不确定性的重要性。每条街道的流量变化都使用 RANS 模拟进行量化。