Conference Record of the 1998 IEEE International Symposium
on Electrical Insulation, 7-10 June 1998, Washington, DC, USA.
Vol. 2., pp. 607--610.
Models of Streamers Growth with
"Physical" Time and Fractal Characteristics
of Streamer Structures
D. I. Karpov, A. L. Kupershtokh
Lavrentyev Institute of Hydrodynamics SB RAS
Novosibirsk, 630090, RUSSIA
Abstract
The results of computer simulation of the streamer growth in
three-dimensional space are presented. Various stochastic growth
criteria and different methods of including the ``physical'' time
into the models are discussed. All the models with ``physical''
time were tested. An implicit finite-difference method for solving
the system of equations for the graph was developed. A great
number of numerical experiments was carried out for different
magnitude of applied voltage and conductivity of streamer
branches. Fractal dimension of simulated discharge patterns was
examined.
Introduction
The streamer growth occurs owing to the formation of new
conductive phase regions in dielectrics. Modern computer
simulations of this phenomenon are based on the idea of space
discretization. New linear segments of streamer channels join
sequently neighbor sites of some spatial lattice to the streamer
structure. The streamer shape is represented by a connected graph
consisting of conductive bonds.
All growth criteria considered below are based on two main
assumptions. Firstly, the growth is stochastic in time. Secondly,
the probability of streamer growth is proportional to some
function of local electric field r(E) depending on dielectric
properties of a substance. Thus, the generation of new bonds is
governed by some stochastic growth criteria in each time step.
The electric field potential was obtained by solving the Poisson
equation. Charge transport along the streamer branches was
computed according to Ohm's law for each conductive element.
"Physical" Time and Growth Criteria
A sequence of time intervals for each growth step calculated in a
proper way following certain assumptions can be named the
"physical" time. Apparently, there are only two ways to
introduce the "physical" time into stochastic models of streamer
growth at electric breakdown. One of them is that the time step
t is taken at first. Then, all bonds that have time to arise
according to the some stochastic criteria are accepted. Another
way of looking at it is to consider the delay time of appearance
of the first new bond as the "physical" time interval of this
growth step. Based on such classification, all the models can be
divided into two groups. First group consists of models in which
only one new bond is added in a time step (single-element models).
The models of second group allow generation of several bonds per
time step and can be named multi-element models.
In single-element models, we actually have to take both the new
bond and the "physical" time interval in a stochastic way.
Niemeyer--Pietronero--Wiesmann Model
In the model proposed by Niemeyer, Pietronero, and Wiesmann (NPW)
in 1984 [1], the probability of streamer growth was
related to local electric field. In each step of growth procedure,
one of the candidate lattice sites i was added to the streamer
structure according to following probability distribution
p(Ei ) = r(Ei ) / $\sum _{i=1}^n$
r(Ei ).
For the first time this model allowed one to simulate the
formation of branching streamer structures having fractal
dimension.
Field Fluctuation Model
The field fluctuation criterion (FFC) proposed in
[2-4] allows the generation of several new
conductive bonds per each time step t.
At each lattice site where the local field
Ei > E* - di ,
(1)
a new region of a conductive phase arises. A random value
d
is assumed to take into account inhomogeneities in dielectrics,
thermal and other fluctuations, including local microfields acting
on the molecules.
The following probability distribution for fluctuations
d
is used
j(d) =
exp(-d / g) / g ,
(2)
that is d = - g ln(x).
Hereinafter x
will be a random number that
is uniformly distributed in the interval from 0 to 1. For a given
distribution function, r(E) has the form
r(E) = A eE/g ,
(3)
where
A = $\frac ${1}{t}
exp(-E* / g) ,
where A and g are constants that depend on dielectric
properties of liquid. The quantity of E* should be
always chosen greater then E. After this,
t should be
accordingly changed to ensure A=const.
To each random value
di
corresponds the time of appearance
of the i-th bond
ti =
- $\frac ${ln(1-exp(-di / g))}
{r(Ei)}.
(4)
The distribution of times
ti
is
j (ti ) =
r(Ei ) exp(-r(Ei )
ti ).
(5)
Thus, condition (1) is exactly equivalent to the inequality
ti <
t.
Biller's Model
In 1993 Biller [5] proposed a growth criterion based on the idea of
introducing the delay time of generation of each candidate bond,
ti=
- ln(xi ) / r(Ei ),
by analogy with the statistical
time lag of breakdown. In the step of growth, only one bond that has
the shortest delay time was added to the streamer structure.
Precisely this quantity
t =
min\limits_{i}\{ti\}
was takenas "physical" time interval.
Scaled Stochastic Time Models
Distribution function(5)of new bond generation in time t is
the same for both the FFC and Biller's models. The probability of
the event that the new bond between streamer structure and some
lattice site i does not arise in time t ,
is equal to
p(Ei ,t) = $\int $\limits_{t}^{$\infty $}
r(Ei ) exp(-r(Ei ) t) dt =
exp(-r(Ei ) t).
Let there be n dielectric sites near the structure. Then the
probability that no bonds arise in time t is equal to
P(t) = $\prod $\limits_{i=1}^{n}
p(Ei ,t) = exp(-Snt),
where Sn =
Si=1n r(Ei ) . The
corresponding
distribution function of the time of appearance of the first new
bond is
f(t) = Snexp(-Snt) .
It has been proposed [6] to use the random value
tS =
- ln(x) /
Si=1n r(Ei ) --- the
"scaled stochastic time" --- as the "physical" time interval.
At the same time, a new bond in the single-element model can be
selected in several ways:
- for instance, by adding, as in [5],
the bond that has minimum delay time
ti =
- ln(xi ) / r(Ei )
to the conductive structure (see in [6]);
- using the NPW criterion;
- using the FFC. A bond that has the shortest of the generation times
ti
calculated from Eq. (4) is chosen.
All these models can be named Scaled Stochastic Time Models (SSTM).
New Multi-Element Models
In the present work, other two models with "physical" time were
proposed within the framework of the definition of multi-element
models.
- The times
ti =
- ln(xi ) / r(Ei ) is computed for
all candidate bonds as in Biller's model. All the bonds having
ti < t
arise.
- Several time steps
tSj =
-ln(xj ) /
Si=1n
r(Ei ) are
sequentially performed as in SSTM as long as
Sj=1m
tSj < t.
Discussion
In [7], the choice of the new bond was performed according
to the NPW criterion but the time interval was calculated from the
formula
t* =
1 / Si=1n
r(Ei ) .
This expression is the mean value of the scaled stochastic time step
< tS > .
Although the choice of the bond was stochastic as in the
NPW model, the time was deterministic. In fact, the temporal scale
is not uniform in these models. For example, the natural course of
time is broken for prebreakdown current. Therefore, the main
shortcoming of both these models is that the "physical" time
and, consequently, the statistical time lag are really absent in
them.
The remaining growth criteria with the "physical" time that were
considered above (FFC, SSTM, Biller's, and modifications of them)
use the same distribution (5) of the stochastic time and,
therefore, describe correctly, in particular, the statistical time
lag of breakdown. Thus, these models are essentially equivalent,
provided that the function r(E) = A e-E/g
is the same.
The basic difference of single-element models from multi-element
models is that they consider the competition between candidate
bonds in different ways. The competition is neglected in
multi-element models, and the first bond that appears is assumed
to suppress the growth of other bonds in single-element models.
All multi-element models allow one to introduce a constant time
interval for all steps of the growth. This fact is important for
gas-dynamics flow simulation.
The time step is decreased because of the competition among the
great number of branches during simulation of the streamer
propagation by single-element models. It is reasonable to continue
the computations using one of the multi-element models in this
case.
The FFC criterion of conductive phase generation satisfies a
specific form (3) of the function r(E), which is considered to
describe dissociation and ionization in a strong electric field.
All other models allow us to use any other dependences r(E)
corresponding to various physical mechanisms of streamer inception
and propagation. For example, one can use the well-known
dependence
r(E) = (E/E0 )h /
t0
or the tunnel effect
r(E) = exp(-C/E ) /
t0 .
Figure 1.
Patterns of streamer structures obtained with FFC.
s = 1: V = 5(a), 10(b), and 15(c) ;
s = 10: V = 15(d).
Figure 2.
Patterns of streamer structures obtained with Biller's
model. s = 1: V = 5(a), 10(b), and 15(c) ;
s = 10: V = 15(d).
Computer Simulation
Computer simulation of streamer growth was carried out in three
dimensions. M = 26 permissible directions
(including diagonals) of
channel propagation was used at each site of a cubic lattice to
diminish the anisotropy of the growing structure. Thus, the
charges flow according to Ohm's law along each bond of a connected
graph forming the frame of the conductive structure. The charge
transport and the electric field potential distribution are
governed by the continuity and Poisson equations
L(j) = - 4 p Q/h3 ,
$\frac{dQ}{dt}$ = - $\Div $(Im) ,
Im =
- sm s
D jm / lm ,
where L is the finite-difference Laplace operator,
h is the
lattice bond length,
$\Div $(Im ) =
Sm=1M
Im
is the sum
over all currents Im flowing out of some
vertex of the graph,
M is the number of neighboring graph vertices,
Djm is the
electric potential difference along the bond, lm
is the bond length, s is its cross section, and
sm is its conductivity.
We used time-implicit finite-difference method for the equation of
charge transport to obtain the equation for computation of
electric potential
L(jn+1)h3 +
4 p t s
\Div(sm
Djmn+1 / lm) =
- 4 p Qni,j,k ,
where t is a step in time and
n is its number. Charge
transport is computed using new values of the electric-field
potential jn+1
Qn+1i,j,k - Qni,j,k =
s t
Sm=1M
sm
Djmn+1 / lm
to ensure charge conservation.
Figure 3.
FFC (a and c) and Biller's (b and d) models. Curves 1,
2, and 3 refer to V = 5, 10, and 15 (s = 1);
curve 4 refers to V = 15 (s = 10).
The calculation method developed is stable over a wide range of
parameter
4 p s t s / h2
up to 105.
The simulation was carried out on lattices with sizes up to
90x90x90. The conductivity s was assumed to be
constant and equal for all branches of the streamer structure. Streamer
propagation velocity was ensured equal for all bonds including
both types of diagonals, provided that the electric potential
difference between their ends was the same. This statement was
tested specially.
Results
It is more convenient to investigate and compare geometric
characteristics of streamer structures by simulation of electric
breakdown in a spherically symmetric field.
For example, typical streamer structures obtained with
multi-element FFC model (Fig. 1) and single-element Biller's model
(Fig. 2) are shown. A denser streamer structure is formed when the
applied voltage V increases, provided the conductivity is the
same (Figs. 1,a--c and 2,a--c). Increase in s is equivalent to
reduction of V (Figs. 1d and 2d). There is an area that is
filled densely with streamer channels in the vicinity of the
central electrode. These patterns coincide within stochastic
variations. The time dependences of the growth velocity
(Fig. 3a,b) and the total charge of structure (Fig. 3c,d)
practically coincide for all the models with "physical" time
provided that the function r(E) is the same.
An increase in s
causes a strong rise of the growth velocity (curves 4 in
Fig. 3a,b) owing to the reduction of the charge relaxation time in
the channels. The maximum electric field strength in front of the
streamer tips is roughly constant during the growth and depends
mainly on V and s .
Figures 4 and 5 show the graphs of the logarithm of the conductive
bond density averaged over 15 streamer patterns versus the
logarithm of the distance r from the centre. There is no fixed
value of the fractal dimension for the streamer structure at
finite s . The differential fractal dimension
Dr introduced in
[6] depends on r .
Dr
is close to the space dimension
near the central electrode and reduces when the radius increases,
particularly in the growth region. The area with
Dr » 3
expands as voltage increases (Fig. 4a,b). The electric field there is
strong so that the streamer channels can propagate rapidly without
charge relaxation in them.
Figure 4.
Differential fractal dimensions of streamer structures
for FFC model. V = 10(a) and 30(b)
(s = 1).
Some discrepancies between single-element and multi-element models
exist in the high-field region in the vicinity of the central
electrode. There multi-element models give a higher density of
streamer channels (Fig. 5b). These differences practically vanish
in the case where the time step t
in multi-element models is small, and, hence, the competition of
neighboring bonds becomes insignificant.
Figure 5.
Differential fractal dimensions of streamer structures.
(a) Biller`s model, (b) FFC (·) and Biller's (+) models,
V = 5, s = 1.
The same simulations were performed with SST models and new
multi-element modification of Biller's model. These results agree
with those described above.
Conclusion
The comparison of differential fractal dimensions obtained with
the growth criteria discussed above shows a little discrepancy
between single-element and multi-element models. Except for this
circumstance, all the models with "physical" time are
essentially equivalent. All of them describe correctly the
propagation velocity of streamer tips, incomplete discharges, and
the distribution of the statistical time lag. The simulation
results are qualitatively similar to the well-known experimental
data on nanosecond breakdown in dielectric liquids.
Acknowledgements
This work was supported by Russian Foundation for Basic Research
under grant No. 97-02-18416.
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