In this paper, the turbulence characteristics were numerically investigatedin an asymmetric turbulent channel flow and the computational results were compared with therelevant experimental data. It shows that the resu...In this paper, the turbulence characteristics were numerically investigatedin an asymmetric turbulent channel flow and the computational results were compared with therelevant experimental data. It shows that the results are consistent with the experiments and thereexist Counter-Gradient Momentum Transport (CGMT) phenomena in the central region near the smoothwall, and this region is as large as 6 percent of the channel width. In addition, a region, in whichCounter-Gradient-Transport (CGT) phenomena occur more evidently, is found close to the rough wall.These results can help to gain a deeper insight into the mechanism of CGT phenomena.展开更多
In this paper orthogonal wavelet transformations are applied to decompose experimental velocity signals in fully develo-ped channel flows with varying pressure gradient into scales. We analyze the time series from tur...In this paper orthogonal wavelet transformations are applied to decompose experimental velocity signals in fully develo-ped channel flows with varying pressure gradient into scales. We analyze the time series from turbulent data, to obtain the statistical characteristics, correlations between the adjacent scales and the principal scale of coherent structures in different scales by wavelet transformations. The results show that, in the counter gradient transport （CGT） region, skewness factors and flatness factors deviate strongly from the corresponding values of Gaussian distribution on certain scales. PDFs on each scale confirm this observation. Scale-scale correlations show further that the fluctuations on some certain special scales are more intermittent than nearby. Principal scale of coherent structure is coincident with the scales on which the statistical properties depart from Gaussian distribution. These features are the same for different families of wavelets, and it also shows some different features in the region between favorable pressure gradient and adverse pressure gradient.展开更多
文摘In this paper, the turbulence characteristics were numerically investigatedin an asymmetric turbulent channel flow and the computational results were compared with therelevant experimental data. It shows that the results are consistent with the experiments and thereexist Counter-Gradient Momentum Transport (CGMT) phenomena in the central region near the smoothwall, and this region is as large as 6 percent of the channel width. In addition, a region, in whichCounter-Gradient-Transport (CGT) phenomena occur more evidently, is found close to the rough wall.These results can help to gain a deeper insight into the mechanism of CGT phenomena.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11102114,11172179)the Innovation Program of Shanghai Municipal Education Commission(Grant No.13YZ124)
文摘In this paper orthogonal wavelet transformations are applied to decompose experimental velocity signals in fully develo-ped channel flows with varying pressure gradient into scales. We analyze the time series from turbulent data, to obtain the statistical characteristics, correlations between the adjacent scales and the principal scale of coherent structures in different scales by wavelet transformations. The results show that, in the counter gradient transport （CGT） region, skewness factors and flatness factors deviate strongly from the corresponding values of Gaussian distribution on certain scales. PDFs on each scale confirm this observation. Scale-scale correlations show further that the fluctuations on some certain special scales are more intermittent than nearby. Principal scale of coherent structure is coincident with the scales on which the statistical properties depart from Gaussian distribution. These features are the same for different families of wavelets, and it also shows some different features in the region between favorable pressure gradient and adverse pressure gradient.
