Reduced predictive models for Micro-tearing modes in the pedestal

POSTER

Abstract

The experimental discovery that magnetic fluctuations observed in the tokamak pedestal seem to show a remarkable sensitivity to the toroidal mode numbers $n$ poses a very interesting and challenging problem. The theoretical challenge becomes even more acute when gyrokinetic simulations of the microtearing modes (MTM) seem to reproduce exactly the same effect. We have developed a pedestal specific model that shows that the $n$ sensitivity is likely to be a consequence of a deeper interaction between magnetic shear (that determines the mode rational surface) and the sharply varying profile of $\omega_{*e}$; it is this combination that determines the conditions for the existence and stability of the MTM. It is found that the MTMs tend to localize at the peak in the $\omega_{*e}$ profile, and are unstable only when a given rational surface aligns with this peak. This idea will be explored and tested using data from DIII-D as well as other experiments. Investigations based on this idea have provided insight into the magnetic spectrograms across several machines for several discharges and; in particular, we were able to, effectively, predict the gaps between frequency bands and toroidal mode numbers.

*Work supported by US DOE grants DE-FG02-04ER54742, DE-AC02-09CH11466 and DE-FC02-04ER54698 and IFS in University of Texas.

Authors

  • Max Curie

    • University of Texas at Austin
  • Joel Larakers

    • University of Texas at Austin
  • Michael Halfmoon

    • University of Texas at Austin
  • David Hatch

    • University of Texas at Austin
  • Ehab Hassan

    • University of Texas at Austin/Oak Ridge National Laboratory
  • M. Kotschenreuthe

    • University of Texas at Austin
  • R. Hazeltine

    • University of Texas at Austin
  • S. Mahajan

    • University of Texas at Austin
  • J. Chen

    • University of California, Los Angeles
  • D. Brower

    • University of California, Los Angeles
  • R. Groebner

    • General Atomics