We wrote the numerical code in C-language.
The code uses a 12 levels plus continuum hydrogen atom model.
All transition, included continua,
are treated explicitly, which means that radiative rates of all transitions
are computed in each iteration. We assume all lines to be in CRD which is reasonable
in flares due to high .
The code was tested using
a five-levels-plus-continuum model of hydrogen for the temperature structures of
the quiet solar atmosphere model VAL3C (Vernazza et al. 1981)
and solar flare atmosphere models F1, F2 (Machado et al. 1980).
We used the approximate formula for the charge conservation equation that
includes ionization from other elements.
The differences for hydrogen level populations and electron densities didn't exceed 15%.
To investigate the influence of the macroscopic velocity field on
emergent intensities we proceed as follows.
We took the temperature structure of the chromospheric flare model F1 and F2
(Machado et al. 1980) and computed the
five-level-plus-continuum models of hydrogen with no velocities. The optical
depth scale for the
line center was
subsequently used to define the velocity structure. We adopt two different
approaches to define velocities.
layer models | |||
V0 | ( ![]() ![]() | ||
(km s-1) | (0.01,0.1) | (0.1,1.0) | (1.0,10.0) |
10 | u10 | m10 | l10 |
30 | u30 | m30 | l30 |
50 | u50 | m50 | l50 |
gradient models | |||
V0 | ![]() | ||
(km s-1) | 0.032 | 0.32 | 3.2 |
10 | u10 | m10 | l10 |
30 | u30 | m30 | l30 |
50 | u50 | m50 | l50 |
Figure 1: Velocities of layer and gradient models with the same parameter
V0 as a function of line center optical depth
Figure 2: The line profiles and population departures
for layer models with F1 temperature structure.
The population departures are plotted for the models with velocity parameter
Figure 3: The line profiles and population departures
for gradient models with F1 temperature structure.
The population departures are plotted for the models with velocity parameter
Figure 4: The line profiles and population departures
for layer models with F2 temperature structure.
The population departures are plotted for the models with velocity parameter
Figure 5: The line profiles and population departures
for gradient models with F2 temperature structure.
The population departures are plotted for the models with velocity parameter
For layer models we chose three regions where we let the material move.
These are: the upper part of the flare chromosphere with
, the middle part with
and the lower part with
. These values of
were substituted for layer-model parameters
and
. The gradient model parameter
,
which corresponds to the height where the velocity is equal to V0, was
set to be in the center of each region of the layer models with respect to
the logarithmic scale. So the value of
was 0.032, 0.32 and
3.2, respectively. To summarize, we have chosen three regions for both
types of models denoted upper, middle and lower, with a
uniform velocity for layer models and with a velocity increase with height
for gradient models.
Once having defined the position of the moving material we chose the
velocity V0 to be 10, 30 and
for both types of models. Note that in the center of each selected region
the velocities for both types of models with the same parameter V0 are
identical. The relationship is shown in Fig. 1 (click here).
Values for each particular model are summarized in Table 1 (click here).
Hydrodynamic simulations have shown that the density of the moving material is higher than its surrounding (Fisher et al. 1985). As the static form of the momentum equation leads to lower densities in the region with velocities, we used the hydrostatic equilibrium equation instead (see Sect. 2.3 (click here) for details).
We started to compute models with the lowest velocity and
proceeded to higher velocities. As a starting guess (the level populations
and electron density) for models with high
velocities we used the results from the models of the same type
with a lower velocity.
This appeared to be very useful for faster convergence of some models.
The convergence (see Sect. 2.4 (click here))
below 10-4 was reached within about 100 iterations for layer models
and within about 150 for gradient models.