Up: Laboratory observation and modeling
Two CR models of highly ionized calcium
have been benchmarked in this work by comparing their
predictions to the line intensities, measured with an absolutely
calibrated grazing incidence spectrometer in two tokamak
experiments. The CR models are
based on ab initio HULLAC and CHIANTI atomic data,
which are the state of the art in atomic structure
calculations.
The HULLAC models
are in a good agreement with most measured lines of lithium-like
to fluorine-like calcium. CHIANTI predictions for
several Ca XVI and Ca XIV lines are inconsistent
with our measurements and HULLAC calculations.
It is shown that for the tokamak, as well as for the solar flare plasma conditions,
collisional-radiative models which include n =2 and n = 3 configurations are
adequate to predict L-shell line intensities.
At these plasma conditions, radiative or collisional
cascades from n =4 , 5 levels are generally insignificant,
and quasi-steady state approach applies well.
With the present state of atomic calculations,
likely explanations for large discrepancies with experimental data are
transient or kinetic effects, not the atomic data quality.
Total ionization and recombination rates which are used by
Mazzotta et al. ([1998]) for a new calcium fractional
abundances calculation, have been used for a calcium L-shell
spectrum simulation.
A synthetic line-integrated spectrum
which includes over two hundred lines in the range 50 - 360 Å,
predicted by HULLAC, closely reproduces the spectrum
recorded at the TEXT tokamak. Density predictions based on line ratios
of beryllium-, boron-, carbon-, nitrogen-, and oxygen-like
calcium are compared with independent density measurements.
Good agreement is found in the cases with minimal experimental uncertainties.
All computational data are available in electronic form
upon request.
Acknowledgements
The authors would like to acknowledge the TEXT and
FTU tokamak teams.
This work was supported by U.S. DoE Grant DE-FG02-86ER53214 at JHU
and Contract No. W-7405-ENG-48 at LLNL.
Up: Laboratory observation and modeling
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