New Development in Computational Physics of Turbulence

本项目取得的成果包括：1。湍流统计的通用性：(1)利用世界上分辨率最高的DNS数据，研究了湍流的小尺度统计。分析表明，DNS不支持湍流间歇性现象学理论中常用的假设。(2) DNS在迄今为止最大的计算域内提供了一个支持Loitsyansky关于自由衰减湍流中大规模统计量普遍性的假设的证据。待处理信息的约简：(1)利用1-(1)的数据对能量从大尺度到小尺度的传递进行统计分析，明确了统计量对粗粒化方法和尺度的依赖性。-(2)利用1-(1)的数据对湍流相干模和非相干模的统计量进行小波分析，表明相干模数与总模数之比随雷诺数的增加而减小。壁面有界湍流：提出了壁面有界湍流DNS的新数值格式，并与传统格式进行了比较。从分析边界层湍流的精度和性价比两方面考虑，提出了一种较有前途的方案。在我们对物理的理解的基础上，检验了湍流通道中惯性子范围-DNS的可行性。在我们的研究水平上，只有通过DNS才能取得1和2的成果，这为我们发展湍流理论和建模提供了良好的基础。3中的发现对于壁面有界湍流的非常大规模的DNS应该是有用的。它们有助于通过计算方法来理解湍流。

The achievements of this project include the following.1.Universality in the Statistics of Turbulence :(1) We studied the small scale statistics of turbulence by using the data of DNS with the world's highest resolution. The analysis showed that the DNS does not support assumptions commonly used in phenomenological theories of intermittency of turbulence.(2) An evidence supporting the Loitsyansky hypothesis on universality of large scale statistics in freely decaying turbulence was presented by DNS in a computation domain which is the largest in the DNSs so far made.2.Reduction of Information to be treated :(1) The analysis of the statistics of the energy transfer from large to small scales by using the data of 1-(1)clarified the dependence of the statistics on the method and scale of the coarse graining.-(2) The wavelet-analysis of the statistics of the coherent and incoherent modes of turbulence by using the data of 1-(1)showed that the ratio of the numbers of the coherent modes to that of the total modes decreases with the Reynolds number.3.Wall bounded turbulence :New numerical schemes for DNS of wall bounded turbulence were proposed, and compared with conventional ones. A scheme was shown to be promising in view of the accuracy and cost performance for DNS of boundary layer turbulence. The feasibility of -DNS of the inertial subrange in a turbulent channel flow was examined on the basis of our understanding of the physics.The achievements of 1 and 2 were possible only by DNS at the level of our study, and provide us with sound basis for developing turbulence theory and modeling. The finding in 3 should be useful for very large scale DNS of wall bounded turbulence. They contribute to the understanding of turbulence by computational approaches.