Quantifying Uncertainties in Computational Fluid Dynamics Predictions for Wind Loads on Buildings

风暴是美国最昂贵的自然灾害之一，使用更先进的弹性设计方法可以显着减少风灾造成的损失。在分析风对结构的影响时，首要的挑战之一是确定表面上产生的压力载荷。先进的计算流体动力学（CFD）模拟是进行这种分析的非常有价值的工具，但它们在设计实践中的频繁使用受到对预测准确性缺乏信心的阻碍。这是因为几个模拟参数，如当地的风特性，是不确定的，可以有很大的影响模型的结果。此外，模拟需要使用不完美的模型来表示风流中的湍流。为了能够将模型用于弹性设计，量化这些不确定性对预测压力载荷的影响至关重要。这项研究将建立一个不确定性量化框架，为建筑物上的风荷载提供量化置信区间的CFD预测，从而能够更准确地评估弹性设计解决方案。这一框架将有利于建模工具，需要输入有关结构上的压力载荷，如基于性能的设计和风致振动模型。 该框架还可以用来研究与可持续城市设计相关的其他流动现象，这些现象受到类似不确定性的影响，例如室外空气质量和可再生能源的收获。因此，该框架具有显着的潜力，以推进优化的建筑物和城市的设计，并支持实现有效的政策，创造弹性和可持续的城市环境。不确定性量化框架将适用于使用低保真度，计算成本低，雷诺兹平均的Navier-Stokes模拟，或高保真，更昂贵，大涡模拟。在这两种类型的模拟，在建筑物上的风压预测的不确定性主要来自两个来源：任意的不确定性，在流入边界条件代表传入的大气边界层和认知的不确定性相关的模型选择，如湍流或亚网格模型和墙壁模型。因此，研究的目标是首先建立方法来量化这两种类型的不确定性，在大涡和湍流平均模拟，并随后建立一个框架，可以量化的流入和湍流模型的不确定性的综合影响。该框架的结果将与现有的测试数据进行验证两个不同的测试用例：低层和高层矩形建筑。研究结果将在三个方面促进知识的发展：（1）它将提高对不同大气边界层入流参数定义的重要性的理解，从而确定哪些参数应最准确地重现以获得可靠的结果;（2）将开发一种方法，以量化压力载荷预测中的湍流或次网格模型误差，相应的分析将增加对湍流海崖体流动的物理和建模的基本理解;（3）通过评估入流和模型的不确定性，可以确定对不确定性的主要贡献，这将使得能够优先进行进一步的研究，以减少预测中的不确定性。考虑到大涡模拟和Cololds平均模拟在计算成本上的巨大差异，对各自置信区间的比较也将提供关于两种模式适用性的重要信息。

Windstorms are among the costliest natural hazards in the United States, and using more advanced resilient design methods could significantly reduce wind-induced damage. One of the first challenges when analyzing the impact of wind on a structure is to determine the resulting pressure load on the surface. Advanced computational fluid dynamics (CFD) simulations are very valuable tools to perform this analysis, but their frequent use in design practice is hindered by a lack of confidence in the accuracy of the predictions. This originates from the fact that several simulation parameters, such as the local wind characteristics, are uncertain and can have a strong influence on the model outcome. In addition, the simulations require the use of imperfect models to represent the turbulence in the wind flow. To enable using the models for resilient design, it is crucial to quantify the effect of these uncertainties on the predicted pressure loads. This research will establish an uncertainty quantification framework that provides CFD predictions for wind loads on buildings with quantified confidence intervals, thereby enabling a more accurate evaluation of resilient design solutions. This framework will benefit modeling tools that require input regarding the pressure loads on structures, such as performance-based design and wind-induced vibration models. The framework also can be leveraged to investigate other flow phenomena relevant to sustainable urban design that are governed by similar uncertainties, such as outdoor air quality and the harvesting of renewable energy resources. Thus, the framework has significant potential to advance the design of optimized buildings and cities, and to support the realization of effective policies for creating resilient and sustainable urban environments.The uncertainty quantification framework will be applicable for use with either low-fidelity, computationally inexpensive, Reynolds-averaged Navier-Stokes simulations, or with high-fidelity, more costly, large-eddy simulations. In both types of simulations, the uncertainty in the prediction of the wind pressure on buildings primarily arises from two sources: aleatoric uncertainty in the inflow boundary conditions representing the incoming atmospheric boundary layer and epistemic uncertainty related to model choices such as the turbulence or subgrid model and wall model. The objectives of the research are therefore to first establish methods to quantify both these types of uncertainties in the large-eddy and Reynolds-averaged simulations, and to subsequently establish a framework that can quantify the combined effect of the inflow and turbulence model uncertainties. The results of this framework will be validated with available test data for two different test cases: a low-rise and a high-rise rectangular building. The research outcomes will advance knowledge in three ways: (1) it will improve understanding of the importance of the definition of the different atmospheric boundary layer inflow parameters, thereby identifying which parameters should be most accurately reproduced to obtain reliable results; (2) it will develop a method to quantify turbulence or subgrid model errors in predictions for pressure loads, and the corresponding analysis will increase fundamental understanding of the physics and modeling of turbulent bluff body flows; and (3) by evaluating both inflow and model uncertainties, the dominant contribution to the uncertainty can be identified, which will enable prioritizing further research to reduce the uncertainty in the predictions. Taking into account the considerable difference in computational cost between large-eddy and Reynolds-averaged simulations, the comparison of the respective confidence intervals will also provide essential information on the fitness-for-purpose of both models.