by - ifv · 2016-04-08 · c) 0 20 40 60 80 100 120 assimilated data data observed after...
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Forecasting Enclosure Fires
By
Ghent University – UGent, Dept. Flow, Heat and Combustion Mechanics, Belgium
Dr Tarek Beji
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Outline
1. Introduction: The Numerical Fire Forecast (NFF) concept
2. Video fire monitoring
3. Fire Modelling (two-zone modelling)
4. Data Assimilation (DA) and Inverse Fire Modelling (IFM)
5. Applications of the Fire Forecasting concept
6. Conclusions and Future work
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Inspired from the concept of Numerical Weather Predictions (NWP)
‘Forecast’ <==> Real-time predictions (as opposed to ‘design calculations’)
Objectives
•Provide guidance during fire fighting interventions
•Assist a decision support system
Methodology
Sensor readings
(e.g. thermocouples, video camera)
Fire Model
(Estimation of model invariants)
Display of future hazards
(e.g. smoke, flashover)
Data Assimilation (DA) Forecast
Previous study: The ‘FireGrid’ project at the University of Edinburgh (CFD
+ HPC resources applied to the ‘Dalmarnock’ fire tests)
1. Introduction: the concept of fire forecasting
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2. Sensor readings
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2. Sensor readings
Collaboration with the multimedia department on the video fire analysis
2.4 m
0.4 m
h XN
ρu, Tucamera
3.3 m
2.4 m
0.4 m
h XN
ρu, Tucamera
3.3 m
1) Smoke detection algorithm based on Energy Disorder (ED)
2) Flame detection algorithm
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time (s)
0 100 200 300 400 500
Smok
e he
ight
(m
)
0,0
0,5
1,0
1,5
2,0
2,5Thermocouple dataVideo data
time (s)
0 200 400 600 800 1000
Smok
e he
ight
(m
)
0,0
0,5
1,0
1,5
2,0
2,5Thermocouple dataVideo data
Smoke detection algorithm
Smoke height estimation of a sofa fire using energy disorder
Case 1
Case 2
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Flame detection algorithm
time (s)
0 100 200 300 400
Flam
e w
idth
, W
f (c
m)
0
25
50
75
100
125
150
time (s)
0 100 200 300 400
Flam
e he
ight
, L f
(cm
)
0
25
50
75
100
125
150
2/5
235.0
02.1⎟⎟⎠
⎞⎜⎜⎝
⎛ += ff WL
Q
time (s)
0 100 200 300 400
Conv
ecti
ve H
RR
, Q
c (k
W)
0
100
200
300
400
500
To be compared with oxygen calorimetry tests
Objective: Estimate the HRR from flame dimensions
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3. Fire Modelling
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3. Fire Modelling: two-zone modelling
( )[ ] ( ) ( )aupuoutfcuaupu TTcmQVTTcdtd
−−−=− ,1 λρ
( ) uoutuinpuu mmmVdtd
,, −+=ρ
Mass conservation for upper layer
Energy conservation for upper layer
ρu, Tu
pm
uoutm ,
uinm ,
Upper layer
Sub-models
• Plume entrainment model (e.g. Zukoski, McCaffrey, Heskestad)
• Model for outflow of gases through opening (derived from Bernoulli’s equation and hydrostatic principles)
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Fire Modelling: why two-zone modelling?
===> very fast calculations using 2-zone modelling
Fire Forecast = Prediction of the fire development
ahead of the event
positive lead time
Disadvantage
“loss of physics” compensated by assimilating observed data into the model.
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4. Inverse Fire Modelling
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4. Inverse Fire Modelling
Design calculations Fire Forecasting
Input:
• Geometry
•Heat Release Rate, Qf
• Heat loss factor, λc
Output (tenability conditions):
• Smoke layer height, h,
• Temperature (Tu) …etc
Input:
• Geometry
•Smoke layer height over a given period of time (i.e. assimilation window)
•Temperature
Estimation of the trend in Qf
Estimation of the heat loss factor
Forecast of h and Tu
Data assimilation Inverse modelling
Vs
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Inverse Fire Modelling
Optimization problem
Sensor readings <==>
‘Observed state’
Fire Model
Model Invariants, MI:
1. Fire growth factor
2. Heat loss factor…
Inverse Problem: What values for the MI provide the best match with the
‘observed state’ of the system provided by the sensor readings?
MATCH ?
Optimization techniques:
1. Gradient-based methods (e.g. Tangent linear technique) : Fast convergence but Initial guess need to be close enough to the solution
2. Gradient-free methods (e.g. Genetic Algorithms): Convergence is ensured but a longer calculation time is required
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5. Applications
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5. Application 1: Smoke Filling in an atrium
22.4 m 11.9 m
27 m
C
burner 2 m x 2 m
Li et al. Journal of China University of Science and Technology 29 (1999) 590-4
-Atrium dimensions = 22.4 m x 11.9 m x 27 m (Length x Width x height)
-Constant fire size 4000 kW
-Two fire locations (centre and corner)
Data Assimilation and Inverse Fire Modelling
-Model Invariants Fire size, Qc, and entrainment coeff. C
-Constants λc= 0.6
-Data Assimilated Smoke layer height, hi, and temperature, Tu.
-Number of observations N=3 (each 20 s)
<==> Assimilation window 60 s
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time (s)
0 50 100 150 200 250 300
h (m
)
0
5
10
15
20
25
30Assimilated dataData observed after assimilationForecast
time (s)
0 50 100 150 200 250 300T u
(0 C)
0
20
40
60
80
100
120
Assimilated dataData observed after assimilationForecastCASE Num. 2:
Fire at the corner
time (s)
0 50 100 150 200 250 300
h (m
)
0
5
10
15
20
25
30
Assimilated dataData observed after assimilationForecast
time (s)
0 50 100 150 200 250 300
T u (0 C
)
0
20
40
60
80
100
120
Assimilated dataData observed after assimilationForecastCASE Num. 1:
Fire at the centre
Forecast Forecast
Forecast Forecast
DA DA
DA DA
Estimated parameters
• Qc= 3303 kW
• C = 0.260
Estimated parameters
• Qc= 2656 kW
• C = 0.109
Modifying the value of λc gives different Qc and C but the same forecast.
5. Application 1: Smoke Filling in an atrium
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5. Application 2: Furniture fire in an ISO-room
Flashover Fire in an ISO-Room (Obs.: h and Tu)
0
0.5
1
1.5
2
2.5
0 100 200 300 400 500
time (s)
Hei
ght (
m)
X_N (Exp.)X_N (Pred.)h (Exp.)h (Pred.)
0
100
200
300
400
500
600
700
800
900
0 100 200 300 400 500
time (s)
Upp
er la
yer t
empe
ratu
re, T
u (C
)
exp. dataPred.
DA1 DA2
• Transient fire
• DDDAS
Forecast 1 Forecast 2
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6. Conclusions and Future Work
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Conclusions
• Presentation of the fire forecasting system
• Advances in video fire monitoring
• Aspects involved in Inverse Modelling and Fire forecasting
• Applications: Smoke filling in an atrium and furniture fire in an ISO-room
Future work
• More validation studies on video fire monitoring
• Application of the concept to multi-compartments