Unsteady aerodynamics of wings in complex environments

大气边界层从来不是静止的。这是因为风经常改变方向和大小，所有这些都与复杂的城市景观相互作用。不断变化的气象条件，可能引发非平稳现象，改变城市流动和湍流。它们对建筑物、小型车辆(自行车、无人机)的空气动力学和扩散(例如，污染物的点源释放)有重大影响。在这个项目中，我们确定了对非平稳现象及其不确定性的理解是提高当前预测能力的关键。一个案例研究是对应急反应的有效预测。这种预报通常有很高的不确定性，因为震源的细节是有限的，而且气象条件是由附近的观测站或预报网格点确定的。这个项目将利用计算方法和设施的最新进展来进行统计分析，以解决CB危险预报中最具挑战性的两个问题：1.流动和扩散预报的不确定性如何受到气象条件和简化建筑几何形状的变化的影响？2.应该如何处理气象数据(例如通过使用机器学习)来定义流动和湍流的输入。为了改善对近源扩散的预测？需要回答这些问题，以便能够评估当前的应急响应工具是否“适合用途”，并定义准确的实时高保真扩散模拟所需的信息和数据准备要求。

The atmospheric boundary layer is never stationary. This is because the wind often changes direction and magnitude, and all these interact with the complex urban landscape. Changing meteorological conditions which could trigger non-stationary phenomena and change urban flow, and turbulence. These have significant impact on aerodynamics of buildings, small vehicle (bikes, UAVs) and dispersion (e.g. point source release of pollutants). In this project, we have identified the understanding of non-stationary phenomena and theconsequent uncertainties as a key to improve the current predictive capability. One case study is efficient prediction for emergency response. Such predictions typically have high uncertainties because details of the source are limited and because that the meteorological conditions are determined from a single nearby observation station or forecast grid point.This project will exploit recent advances in computational methods and facilities to enable statistical analyses to be conducted which address two of the most challenging issues in CB hazard prediction:1. How is uncertainty in flow and dispersion prediction affected by changes in the meteorological conditions and simplified building geometries?2. How should meteorological data be processed (such as by using machine learning) to define the inputs for flow and turbulences, and to improve the prediction of near-source dispersion?Answers to these are required so that an assessment can be made as to whether current emergency response tools are 'fit-for-purpose', and to define the information and data preparation requirements necessary for accurate real-time high-fidelity dispersion simulations.