arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2103.07387physics.flu-dyn
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通过深度学习从粗壁测量到湍流速度场

From coarse wall measurements to turbulent velocity fields through deep learning

Alejandro Güemes, Stefano Discetti, Andrea Ianiro, Beril Sirmacek, Hossein Azizpour, Ricardo Vinuesa

摘要:

这项工作评估了超分辨率生成对抗网络(SRGANs)的适用性,作为一种方法,用于从粗壁测量中重建湍流量。该方法适用于壁场的分辨率增强和估计壁平行的速度场从粗壁测量的剪切应力和压力。通过直接数值模拟产生的摩擦雷诺数$Re_τ=180$的湍流明渠流数据库进行了分析。粗壁测量已经产生了三个不同的下采样因子$f_d=[4,8,16]$从高分辨率的字段,和壁平行的速度场已被重建在四个内部缩放壁正常的距离$y^+=[15,30,50,100]$。我们首先表明,SRGAN可以用来提高粗壁测量的分辨率。如果与直接重建从唯一的粗壁测量相比,SRGAN提供更好的瞬时重建,无论是在均方误差和频谱分数误差。尽管输入壁数据的分辨率较低,使得实现高度准确的预测更具挑战性,但所提出的基于SRGAN的网络产生了非常好的重建结果。此外,它表明,即使是最具挑战性的情况下,SRGAN是能够捕捉的大尺度结构,填充流。所提出的新方法具有很大的潜力,闭环控制应用依赖于非侵入式传感。

英文摘要:

This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse wall measurements. The method is applied both for the resolution enhancement of wall fields and the estimation of wall-parallel velocity fields from coarse wall measurements of shear stress and pressure. The analysis has been carried out with a database of a turbulent open-channel flow with friction Reynolds number $Re_τ=180$ generated through direct numerical simulation. Coarse wall measurements have been generated with three different downsampling factors $f_d=[4,8,16]$ from the high-resolution fields, and wall-parallel velocity fields have been reconstructed at four inner-scaled wall-normal distances $y^+=[15,30,50,100]$. We first show that SRGAN can be used to enhance the resolution of coarse wall measurements. If compared with direct reconstruction from the sole coarse wall measurements, SRGAN provides better instantaneous reconstructions, both in terms of mean-squared error and spectral-fractional error. Even though lower resolutions in the input wall data make it more challenging to achieve highly accurate predictions, the proposed SRGAN-based network yields very good reconstruction results. Furthermore, it is shown that even for the most challenging cases the SRGAN is capable of capturing the large-scale structures that populate the flow. The proposed novel methodology has great potential for closed-loop control applications relying on non-intrusive sensing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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