Extended Self Similarity of Passive Scalar in Rayleigh-Bénard Convection Flow Based on Wavelet Transform
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摘要: 采用子波分析方法,对实验中测得的Rayleigh-Bnard对流温度信号 (被动标量)的标度律,从以下两个方面进行了研究:第一方面,直接采用扩展的结构函数(ESS)的公式对温度信号进行了分析,研究结果表明,采用该方法后的标度区域明显比不采用扩展结构函数的标度区域要宽,得到的标度指数与其它实验中得到的温度信号标度指数ξ(q)一致;第二个方面,将A.Arneodo等人对湍流中速度信号提出的基于子波分析的扩展标度公式,推广应用于温度信号,给出了一个描述温度信号的、基于子波分析的扩展标度公式,研究结果表明,提出的建立在子波系数极大模求和基础上的扩展标度公式,也能够比较准确地提取温度信号的标度指数ξ(q) 。
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关键词:
- Rayleigh-Bnard对流 /
- 子波分析 /
- 标度律 /
- 温度信号
Abstract: Wavetet transform was used to analyze the scaling rule of temperature data (passive scalar) in Rayleigh-B nard convection flow from two aspects. The first one was to utilize the method of extended self similarity, presented first by Benzi et al, to study the scaling exponent of temperature data. The obtained results show that the inertial range is much wider than that one determined directly from the conventional structure function, and find the obtained scaling exponent agrees well with the one obtained from the temperature data in an experiment of wind tunnel. The second one was that,by extending the formula which was proposed by A. Armeodo et al for extracting the scaling exponent (q) of velocity data to temperature data, a newly defined formula which is also based to wavelet transform, and can determine the scaling exponent ξ(q) of temperature data was proposed. The obtained results demonstrate that by using the method which is named as WTNN (wavelet transfrom maximum modulus) ξ(q) correctly can be extracted. -
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