Game-Method for modeling and WRFFire Model Working Together
PhD student Nina Dobrinkova
Assoc.prof Stefka Fidanova
Prof. Ivan Dimov
Prof. Krassimir Atanassov
Prof. Jan Mandel
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
In this presentation:
• Will be presented the types of wildland fires
• Will be presented basics of game-method
model and some results
• Will be presented WRF-Fire model and some
results
• Will be presented a conception about how the
two models can work together
• Future work plans
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Types of wildland fires
Surface fires have rate of spread of the forest fire advancing in burning
materials like grass, shrubs, small trees. These fires are the most
common in nature.
Crown fires are the combustion of tree crowns that overlie the surface fire
and surface fuels. They are very intensive in burning and hard to fight
with.
Spotting fires refer to new ignitions ahead of the main fire front started by
firebrands lofted by the fire and transported by the wind. Spotting can
advance fire over barriers many kilometers away from the current fire
perimeter and alter fire growth patterns and behavior.
Fire acceleration is defined as the rate of increase in spread rate for a
given ignition source that all fire environmental conditions remain
constant.
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Forest fire statistics in South european
countries
1600
1400
Брой пожари
1200
1000
800
600
400
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
200
1982
80000
70000
60000
50000
40000
30000
20000
10000
0
1980
Number of Fires
Number of fires in the Southern Member States
0
1970
1975
1980
1985
1990
1995
2000
2005
Година
1971 to 2006
Year
The number of fires since 1980
according to statistics done for the
southern
member states
has
increased rapidly in the last few years
• considerable increase of the number of fires
after 1990 (more than 1000 in year 2000)
• more than 30 times increase of the burned
area in the recent years
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Forest fire statistics - Bulgaria
Bulgaria’s statistic about forest fires
1994 to 2010
Natura 2000 zones in 2007
There is significant overlapping of the protected zones and the one which fires has occurred
in the last 16 years
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Game-Method Model
L=A(K)=A1(A1(…A1(K)…))
Where:
A1 - is the transition rule
K – initial configuration
L – final configuration
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Experimental results (1)
Our test area is divided on N x M bins.
We fill on random principle the bins with numbers from 0 to 9, which
would be our burning coefficients.
We do correctness check by averaging every bin with respect to all
simulated tests. The equation used was:
We do the same with averaging action with the burned area:
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Experimental results (2)
• We prepare a small example with 9 x 9 bins just to show how
the fire is dispersed. This is the initial area, where the 0 are
non burning material, and the other coefficients are potential
fire beds.
Beginning of burning
Second step
Third step
Forth step
WRF-Fire basics (1)
Mathematically the fire model is posed in the horizontal
(x,y) plane. The fire propagation is in semi-empirical
approach and it is assumed that the fire spreads in the
direction normal to the fireline. This is given from the the
modified Rothermel’s formula:
S=min{B0,R0 + ɸW + ɸS}, where
B0 is the spread rate against the wind;
R0 is the spread rate in the absence of wind;
ɸW is the wind correction
ɸS is the terrain correction
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
WRF-Fire basics (2)
Once the fuel is ignited, the amount of the fuel at
location (x, y) is given by:
Where :
t is the time;
ti is the ignition time;
F0 is the initial amount of fuel;
T is the time for the fuel to burn down to 1/e of the
original quantity
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
WRF-Fire basics (3)
From slides (1) and (2) we have idea about the plane,
where the fire will spread and the fuel which we want to
ignited, but we also need the heat flux, which is inserted
as the time derivative of the temperature, while the latent
heat
flux
as
concentration.
the
time
This
derivative
scheme
is
of
water
required
vapor
because
atmospheric models with explicit time stepping, such as
WRF, do not support flux boundary conditions.
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
WRF-Fire basics (4)
From the previous three slides we have the plane of the
fire, the ignited fuel, the heat flux, but we also will need
the burning region at time t.
It is represented by level set function ɸ, as the set of all
points (x, y) where ɸ (x, y, t) < 0.
The level set function satisfies a partial differential
equation for dynamic implicit surfaces:
Where
is the Eucledian norm of the gradient of ɸ.
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Simulation results (1)
The objective of this third simulation is to present the
simulation capabilities of WRF-Fire model with real input data.




Atmospheric model was run on 2 domains with 250m and
50m resolution
41 vertical levels were used
The fire module, coupled with the atmosperic domain is run
on 5m resolution with 0.3s time step
Simulated burned area and actual data from the Ministry of
Agriculture, Forest and Food showed good comparison
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Parallel performance
Cores
6
12
24
240
360
480
720
Real time
coefficients
10.59
9.21
3.91 2.75 1.64 0.99 0.61
0.44
0.37
0.31


36
60
120
Computations were performed on the Janus cluster at the
University of Colorado. The computer consists of nodes
with dual Intel X5660 processors (total 12 cores per node),
connected by QDR InfiniBand
The model runs as fast as real time on 120 cores and it is
twice faster on 360
(real time coef. = 0.99)
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Simulation results (2)
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Simulation results (3)





the heat flux (red is
high)
burned area (black)
atmospheric flow
(purple is over 10m/s)
Note the updraft caused
by the fire
Ground image from
Google Earth
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Combining the two methods together
From the meteorological point of view wind is the most important
parameter for the fire spread. It is missing from the game-method
model and is included in the simulations of WRF-Fire. That is why we
decided to combine both models and include wind in the gamemethod model. This can be done in two ways:
-With average wind value, which does not correspond to the real
wind behavior
-With random field describing the wind, where we will use Monte
Carlo simulation. We define Z(x), x ∈ R2 to be random field and we
have:
Fx1,x2,…,xn (X1,X2, ..., Xn) = P (z1 ≤ X1,X2, ...,zn ≤ Xn)
for z1 = Z (x1) and n can be any integer.
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Sub-cases
The representation of the random field of the wind we divide in two subcases:
-The wind direction and velocity are having random behavior (hard for
computational estimations)
-The wind has fixed direction and random speed velocity
We set width of the random field parameter – w
In cases:
1) Small values of width w – the wind parameter will have speed in small
interval close to the case the velocity is fixed
2) Relatively big random field of the wind with different in range values,
which is because of the non constant behavior of the wind, where we
can calibrate the model with the meteorological conditions
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Future work
In the described steps with conditions for the random field of the wind Monte
Carlo methods can be used for simulation results and tests.
We will focus on comprising between exact rotational-wind estimator (in
its two-dimensional version) and approximate estimator
The proposed scheme represents two and three –dimensional turbolence
over discretized spatial domains. We propose combination between game
method for modelling and the meteorology from WRF-Fire method.
The will be simulated by random field and Monte Carlo methods.
Our future work will be focused on learning the random field parameters in
various range for w. We will test with sensitivity analysis the influence of the
value of this parameter on the models output.
We will compare the results with cases of fixed velocity.
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011
Thank you for your attention!
Nina Dobrinkova
Institute of Information and Comunication Technologies Bulgarian Academy of Sciences,
[email protected]
IMACS MCM 29 Aug.- 2 Sept., Borovets,
2011