...
首页> 外文期刊>Journal of Wind Engineering and Industrial Aerodynamics: The Journal of the International Association for Wind Engineering >Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer
【24h】

Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer

机译:优化大气边界层大涡模拟的湍流流入条件

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Large-eddy simulations (LES) of the atmospheric boundary layer (ABL) require the specification of a turbulent inflow condition with appropriate turbulence intensities and length scales. When using a synthetic turbulence generator, the statistics obtained downstream of the inlet might deviate considerably from the intended values. In the present work we propose a fully automated approach to modify the input parameters for the turbulence generator such that the desired turbulence statistics are obtained at the downstream location of interest. The method employs a gradient-based optimization in combination with the divergence-free version of the digital filter method developed by Xie and Castro [1, 2]. A sensitivity analysis showed that the spanwise and vertical Reynolds stresses and length scales are the most influential input parameters. Hence, the optimization adjusts these parameters until the desired turbulence statistics are obtained downstream in the domain. The results demonstrate the promising capabilities of the method: the mean velocity profile is correctly maintained using an appropriate wall function, while the optimization results in Reynolds stresses, integral length-scales and turbulence spectra that compare well to ABL wind tunnel measurements.
机译:大气边界层(ABL)的大涡流模拟(LES)要求具有适当的湍流强度和长度尺度的湍流流入条件。当使用合成湍流发生器时,入口下游获得的统计数据可能与预期值相比偏离。在本工作中,我们提出了一种完全自动化的方法来修改湍流发生器的输入参数,使得在感兴趣的下游位置获得所需的湍流统计。该方法采用基于梯度的优化与Xie和Castro [1,2]开发的数字滤波器方法的无分离版本。灵敏度分析表明,跨涡流和垂直雷诺应力和长度尺度是最有影响力的输入参数。因此,优化调整这些参数,直到在域下游获得所需的湍流统计。结果证明了该方法的有希望的能力:使用适当的墙壁功能正确地维持平均速度曲线,而优化导致雷诺应力,积分长度和湍流光谱比较良好的对ABL风洞测量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号