Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-19T10:17:37.225Z Has data issue: false hasContentIssue false

The MDF technique for the analysis of tokamak edge plasma fluctuations

Published online by Cambridge University Press:  21 November 2013

M. Lafouti
Affiliation:
Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
M. Ghoranneviss
Affiliation:
Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
S. Meshkani
Affiliation:
Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
A. Salar Elahi*
Affiliation:
Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
*
Email address for correspondence: [email protected]

Abstract

Tokamak edge plasma was analyzed by applying the multifractal detrend fluctuation analysis (MF-DFA) technique. This method has found wide application in the analysis of correlations and characterization of scaling behavior of the time-series data in physiology, finance, and natural sciences. The time evolution of the ion saturation current (Is), the floating potential fluctuation (Vf), the poloidal electric field (Ep), and the radial particle flux (Γr) has been measured by using a set of Langmuir probes consisting of four tips on the probe head. The generalized Hurst exponents (h(q)), local fluctuation function (Fq(s)), the Rényi exponents (τ(q)) as well as the multifractal spectrum fh) have been calculated by applying the MF-DFA method to Is, Vf, and the magnetohydrodynamic (MHD) fluctuation signal. Furthermore, we perform the shuffling and the phase randomization techniques to detect the sources of multifractality. The nonlinearity shape of τ(q) reveals a multifractal behavior of the time-series data. The results show that in the presence of biasing, Is, Vf, Ep, and Γr reduce about 25%, 90%, 70%, and 50%, respectively, compared with the situation with no biasing. Also, they reduce about 15%, 90%, 35%, and 25%, respectively, after resonant helical magnetic field (RHF) application. In the presence of biasing or RHF, the amplitude of the power spectrum of Is, Vf, Γr, and MHD activity reduce remarkably in all the ranges of frequency, while their h(q) increase. The values of h(q) have been restricted between 0.6 and 0.68. These results are evidence of the existence of long-range correlations in the plasma edge turbulence. They also show the self-similar nature of the plasma edge fluctuations. Biasing or RHF reduces the amount of Fq(s). The multifractal spectrum width of Is, Vf, and MHD fluctuation amplitude reduce about 60%, 70%, and 42%, respectively, by applying biasing. In the presence of RHF, their width reduces about 60%, 85%, and 75%, respectively. It means that biasing and RHF reduce the degree of multifractality.

Type
Papers
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Antar, G. Y., Devynck, P., Garbet, X., Luckhardtconn, R. W., Doerner, R. P. and Sze, F. C. 2001 Phys. Rev. Lett. 87, 065001.Google Scholar
Arabasiand, A. and Vicsek, T. 1991 Phys. Rev. A 44, 2730.Google Scholar
Arneodo, A., Grasseau, G. and Holschneider, M. 1988 Phys. Rev. Lett. 61, 2281.Google Scholar
Barabasi, A. and Vicsek, T. 1991 Phys. Rev. A 44, 2730.Google Scholar
Carreras, B. A., van Milligen, B. Ph., Pedrosa, M. A., Balbín, R., Hidalgo, C., Newman, D. E., Sánchez, E., Frances, M., García-Cortés, I., Bleuel, J., et al. 1998 Phys. Plasmas 5, 3632.Google Scholar
Chen, Z., Ivanov, P. Ch., Hu, K. and Stanley, H. E. 2002 Phys. Rev. E 65, 041107.Google Scholar
Diamond, P. H. and Hahm, T. S. 1995 Phys. Plasmas 2, 3640.Google Scholar
Eke, A., Herman, P., Kocsis, L. and Kozak, L. R. 2002 Physiol. Meas. 23, R138.Google Scholar
Garbet, X. and Waltz, R. E. 1998 Phys. Plasmas 5, 2836.Google Scholar
Ghorannevis, M., Shariatzadeh, S. R., Talebi Taher, A., Salem, M. K., Arvin, R., Mohammadi, S., Ghasemloo, M. and Tarkeshian, R. 2007 Sci. I. A. U. (JSIAU) 17, 63.Google Scholar
Goncalves, P., Riedi, R. and Baraniuk, R. 1998 In: Proc. 32nd ASILOMAR Conf. on Signals, Systems and Computers, Monterey, CA.Google Scholar
Gopikrishnan, P., Plerou, V., Nunes Amaral, L. A., Meyer, M. and Stanley, H. E. 1999 Phys. Rev. E 60, 5305.Google Scholar
Gu, G. F. and Zhou, W. X. 2006 Phys. Rev. E 74, 061104.Google Scholar
Hu, K., Ivanov, P. Ch., Chen, Z., Carpena, P. and Stanley, H. E. 2001 Phys. Rev. E 64, 11114.Google Scholar
Hwa, R. C., Yang, C. B., Bershadskii, S., Niemela, J. J. and Sreenivasan, K. R. 2005 Phys. Rev. E 72, 066308.Google Scholar
Kantelhardt, J. W., Rybskia, D., Zschiegnerb, S. A., Braunc, P., Koscielny-Bundea, E., Livinae, V., Havline, S., Bundea, A. and Stanley, H. E. 2003 Physica A 330, 240.CrossRefGoogle Scholar
Kantelhardt, J. W., Zschiegner, S. A., Bunde, E. K., Havlin, S., Bunde, A. and Stanley, H. E. 2002 Physica A 316, 87.Google Scholar
Krommes, J. A. 1999 Phys. Plasmas 7, 1752.Google Scholar
Krommes, J. A. and Ottaviani, M. 2000 Phys. Plasmas 7, 1752.Google Scholar
Mallat, S. 1999 A Wavelet Tour of Signal Processing. Academic Press, San Diego.Google Scholar
Mandelbrot, B. B. and van Ness, J. W. 1968 SIAM Rev. 10, 422.CrossRefGoogle Scholar
Manimaran, P., Lakshmi, P. A. and Panigrahi, P. K. 2006 J. Phys. A: Math. Gen. 39, L599.Google Scholar
Manimaran, P., Panigrahi, P. K. and Parikh, J. C. 2005 Phys. Rev. E 72, 046120.Google Scholar
Matia, K., Ashkenazy, Y. and Stanley, H. E. 2003 Europhys. Lett. 61, 422.Google Scholar
Movahed, M. S. and Hermanis, E. 2008 Physica A 387, 915.Google Scholar
Nevins, W. M. 2000 Bull. Am. Phys. Soc. 45, 158.Google Scholar
Newman, D. E., Carreras, B. A., Diamond, P. H. and Hahm, T. S. 1996 Phys. Plasmas 3, 1858.Google Scholar
Ohashi, K., Nunes Amaral, L. A., Natelson, B. H. and Yamamoto, Y. 2003 Phys. Rev. E. 68, 065204(R).Google Scholar
Ossadnik, S. M., Buldyrev, S. B., Goldberger, A. L., Havlin, S., Mantegna, R. N., Peng, C. K., Simons, M. and Stanley, H. E. 1994 Biophys. J. 67, 64.CrossRefGoogle Scholar
Oswiecimka, P., Kwapień, J. and Drozdz, S. 2006 Phys. Rev. E 74, 016103.Google Scholar
Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E. and Goldberger, A. L. 1994 Phys. Rev. E 49, 1685.Google Scholar
Plerou, V., Gopikrishnan, P., Nunes Amaral, L. A., Meyer, M. and Stanley, H. E. 1999 Phys. Rev. E 60, 6519.Google Scholar
Politzer, P. A. 2000 Phys. Rev. Lett. 84, 1192.CrossRefGoogle Scholar
Taqqu, M. S., Teverovsky, V. and Willinger, W. 1995 Fractals 3, 785.Google Scholar
Waltz, R. E., Candy, J. M. and Rosenbluth, M. N. 2002 Phys. Plasmas 9, 1938.Google Scholar