摘要
The effect of time of applied bias on the edge turbulent transport is analyzed by the multi-fractal detrend fluctu- ation analysis （MF-DFA） method in the IR-Tl tokamak. The generalized Hurst exponents and the multifractal spectrum are computed by this method. The MF-DFA method is applied to the fluctuation of gradient of float- ing potential time series collected by a multipurpose probe. Tile monofractality or multifractality of the time series can be detected by generalized exponent. The multifractal spectrum describes the singularity content of the process. The results show that with applying bias to the plasma at different times （t = 15 ms, 18ms and 22ms）, the degree of multifractality changes. It reaches the minimum when the bias is applied at t = 18 ms. The multifractality source of data is investigated by the surrogate method （phase randomization techniques）. The surrogate method can destroy the different types of correlations in all the sizes of fluctuations. The results show that the long-range correlation contributes more to multifractality than the fat tail distribution.
The effect of time of applied bias on the edge turbulent transport is analyzed by the multi-fractal detrend fluctu- ation analysis （MF-DFA） method in the IR-Tl tokamak. The generalized Hurst exponents and the multifractal spectrum are computed by this method. The MF-DFA method is applied to the fluctuation of gradient of float- ing potential time series collected by a multipurpose probe. Tile monofractality or multifractality of the time series can be detected by generalized exponent. The multifractal spectrum describes the singularity content of the process. The results show that with applying bias to the plasma at different times （t = 15 ms, 18ms and 22ms）, the degree of multifractality changes. It reaches the minimum when the bias is applied at t = 18 ms. The multifractality source of data is investigated by the surrogate method （phase randomization techniques）. The surrogate method can destroy the different types of correlations in all the sizes of fluctuations. The results show that the long-range correlation contributes more to multifractality than the fat tail distribution.
