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

量化建筑物风荷载计算流体动力学预测的不确定性

基本信息

批准号：
1635137
负责人：
Catherine Gorle
金额：
$ 36.25万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1635137&HistoricalAwards=false
关键词：
Quantifying Uncertainties Computational Fluid Dynamics

项目摘要

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.

风暴是美国最昂贵的自然危害之一，使用更先进的弹性设计方法可以大大减少风引起的损害。分析风对结构的影响时，最初面临的挑战之一是确定所得的压力负荷在表面上。先进的计算流体动力学（CFD）模拟是执行此分析的非常有价值的工具，但是由于缺乏对预测准确性的信心，它们在设计实践中的频繁使用受到阻碍。这源于以下事实：几个仿真参数（例如局部风特征）不确定，并且可能对模型结果产生强大的影响。此外，模拟需要使用不完美的模型来表示风流中的湍流。要使用模型进行弹性设计，至关重要的是量化这些不确定性对预测压力负荷的影响。这项研究将建立一个不确定性量化框架，该框架为具有量化置信区间的建筑物的风负载提供了CFD预测，从而可以对弹性设计解决方案进行更准确的评估。该框架将有益于建模工具，这些工具需要有关结构压力负载的输入，例如基于性能的设计和风引起的振动模型。该框架还可以利用来调查与可持续城市设计相关的其他流动现象，这些现象受类似的不确定性（例如户外空气质量和收获可再生能源的收获）管辖。因此，该框架具有推动优化建筑和城市设计的巨大潜力，并支持实现有效的政策来创建有弹性和可持续的城市环境。不确定性量化框架将适用于低获取性，计算性较小的，雷诺（Reynolds）的尼诺（Reynolds），耐用的navier-stokes simulation oil navier-stokes simulation contimutions oil simulation，又有更高的成本，更成本，更成本，更高。在两种类型的模拟中，对建筑物的风压预测的不确定性主要来自两个来源：在流入边界条件下，代表与模型选择（例如湍流或子网格模型和壁模型）相关的流入边界条件中的不确定性。因此，研究的目标是首先建立方法来量化大码和雷诺平均模拟中的这两种类型的不确定性，并随后建立一个可以量化流入和湍流模型不确定性的综合效果的框架。该框架的结果将使用两个不同测试用例的可用测试数据进行验证：一个低层和高层矩形建筑物。研究结果将以三种方式提高知识：（1）它将提高人们对不同大气边界层流入参数定义的重要性的理解，从而确定应最准确地复制哪些参数以获得可靠的结果；（2）它将开发一种方法来量化压力载荷预测中的湍流或子网格模型误差，相应的分析将增加对湍流悬崖体流的物理和建模的基本理解；（3）通过评估流入和模型不确定性，可以确定对不确定性的主要贡献，这将使您可以优先进行进一步的研究以减少预测中的不确定性。考虑到大码和雷诺平均模拟之间的计算成本有很大的差异，相应置信区间的比较还将提供有关这两个模型的适应性 - 效果的基本信息。