[Table of Contents]

Plasma and Fusion Research

Volume 9, 1302137 (2014)

Letters

A Statistical Approach for Predicting Thermal Diffusivity Profiles in Fusion Plasmas as a Transport Model
Masayuki YOKOYAMA
National Institute for Fusion Science, Toki 509-5292, Japan
(Received 18 July 2014 / Accepted 11 August 2014 / Published 31 October 2014)

Abstract

A statistical approach is proposed to predict thermal diffusivity profiles as a transport “model” in fusion plasmas. It can provide regression expressions for the ion and electron heat diffusivities (χi and χe), separately, to construct their radial profiles. An approach that this letter is proposing outstrips the conventional scaling laws for the global confinement time (τE) since it also deals with profiles (temperature, density, heating depositions etc.). This approach has become possible with the analysis database accumulated by the extensive application of the integrated transport analysis suite to experiment data. In this letter, TASK3D-a [M. Yokoyama et al., Plasma Fusion Res. 9, 3402017 (2014)] analysis database for high-ion-temperature (high-Ti) plasmas [H. Takahashi et al., Nucl. Fusion 53, 073034 (2013)] in the LHD (Large Helical Device) [O. Kaneko et al., Nucl. Fusion 53, 104015 (2013)] is used as an example to describe an approach.

Keywords

multiple ordinary least squares (OLS) regression analysis, TASK3D-a, LHD analysis database, energy transport

DOI: 10.1585/pfr.9.1302137

Full Text

PDF format (500Kbytes) PDF Download

References

  • [1] ITER Physics Expert Groups on Confinement and Transport and Confinement Modelling and Database et al., Nucl. Fusion 39, 2175 (1999).
  • [2] H. Yamada et al., Nucl. Fusion 45, 1684 (2005).
  • [3] http://www.iter.org/
  • [4] O. Meneghini et al., Phys. Plasmas 21, 060702 (2014).
  • [5] S.P. Hirshman and J.C. Whitson, Phys. Fluids 26, 3553 (1983).
  • [6] S. Murakami et al., Trans. Fusion Technol. 27, 256 (1995).
  • [7] M. Yokoyama et al., Plasma Fusion Res. 9, 3402017 (2014).
  • [8] M. Yoshinuma et al., Fusion Sci. Technol. 58, 375 (2010).
  • [9] I. Yamada et al., Fusion Sci. Technol. 58, 345 (2010).
  • [10] K. Tanaka et al., Fusion Sci. Technol. 58, 70 (2010).
  • [11] H. Lee et al., Plasma Phys. Control. Fusion 55, 014011 (2013).
  • [12] I.T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, 2nd ed. (Springer, NY, 2002).
  • [13] K.R. Gabriel, Biometrika 58, 453 (1971).
  • [14] J.O. Rawlings et al., Applied Regression Analysis: A Research Tool, Second Edition (Springer, NY, 1998).
  • [15] J. Miyazawa et al., Fusion Eng. Des. 86, 2879 (2011).
  • [16] C.D. Beidler and W.N.G. Hitchon, Plasma Phys. Control. Fusion 36, 317 (1994).
  • [17] K. Nagaoka et al., to be presented in the 25th IAEA Fusion Energy Conference, St. Petersburg, Oct. 2014.

This paper may be cited as follows:

Masayuki YOKOYAMA, Plasma Fusion Res. 9, 1302137 (2014).