基于小波分析的缩减湍流数据采集量的新方法

由于湍流实验的数据采集量的大小通常没有准则,实验中为了降低偶然事件的影响通常都使用较大的数据量,从而在数据的采集、存储和后处理过程中都造成资源和时间的浪费。笔者根据小波分析所具有的良好的时频双局域性的特点,在文献4,5]的基础上,配合统计学方法,在理论上提出了以小波方法缩减湍流实验数据采集量和某些流场计算方法计算量的方法。以使用小波方法分析湍流边界层的湍动能方法为例,证明了这种方法的合理性和可行性。

A new method for reducing sample size of turbulent experiment based on wavelet

For the lack of principle of turbulent experimental sample size,turbulent experimenters usually take overabundant sample size to minimize the influence of random events,which arouses waste of storage space and process time when sampling,recording,and postprocessing the sample data.Since wavelet method has excellent performance in both time domain and frequency domain,on the base of results of ,adopting statistic methods,this paper proposed a method to reduce data amount requirement of turbulent experiments and some flow field compute methods.With an example of analyzing the turbulent kinetic energy in a turbulent boundary layer,this method is proved to be reasonable and practicable.