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.

风暴是美国损失最惨重的自然灾害之一，使用更先进的弹性设计方法可以显着减少风引起的损害。分析风对结构的影响时面临的首要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) 模拟是执行此分析的非常有价值的工具，但由于对预测准确性缺乏信心，它们在设计实践中的频繁使用受到阻碍。这是因为一些模拟参数（例如当地风特性）是不确定的，并且会对模型结果产生很大影响。此外，模拟需要使用不完善的模型来表示风流中的湍流。为了能够使用模型进行弹性设计，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，以量化的置信区间提供建筑物风荷载的 CFD 预测，从而能够更准确地评估弹性设计解决方案。该框架将有利于需要输入有关结构压力载荷的建模工具，例如基于性能的设计和风致振动模型。该框架还可用于研究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，该框架具有推进优化建筑和城市设计的巨大潜力，并支持实现创建有弹性和可持续的城市环境的有效政策。不确定性量化框架将适用于低保真度、计算成本低廉的情况。、雷诺平均纳维-斯托克斯模拟，或高保真、成本更高的大涡流模拟。在这两种类型的模拟中，建筑物风压预测的不确定性主要来自两个来源：代表传入大气边界层的流入边界条件的任意不确定性以及与模型选择（例如湍流或亚网格模型）相关的认知不确定性和墙体模型。因此，研究的目标是首先建立量化大涡和雷诺平均模拟中这两种类型的不确定性的方法，然后建立一个可以量化流入和湍流模型不确定性的综合影响的框架。该框架的结果将使用两个不同测试案例的可用测试数据进行验证：低层和高层矩形建筑。研究成果将从三个方面增进知识：（1）将提高对不同大气边界层流入参数定义重要性的理解，从而确定应最准确地再现哪些参数以获得可靠的结果；（2）它将开发一种量化压力载荷预测中的湍流或亚网格模型误差的方法，相应的分析将增加对湍流钝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，这将有助于优先考虑进一步研究以减少预测的不确定性。考虑到大涡模拟和雷诺平均模拟之间计算成本的巨大差异，各自置信区间的比较也将提供有关两种模型的适用性的重要信息。