Dr Guillermo Rein
School of Engineering
University of Edinburgh
Numerical forecasting of fire dynamics:
Tomorrow's infrastructure protection
Plenary keynote
Based on the PhD thesis of W Jahn
Seeing the Future
This is the 48 h tide forecast for the 26th and 27th of March 2012 in
Aveiro produced on the 25th
This accurate forecasts allow local finishing vessels to program their
route and operations ahead of time.
The Tides of Aveiro, Portugal
May 22nd 2011 forecast made on May 21st
2011 Icelandic ash cloud
Forecasting Fire Dynamics
Paradigm shift in Emergency Response
�Knowledge of future fire conditions – spread, smoke, structural collapse
�Additional layer of essential information to the Fire Service currently non existing
�Technology for Smart Buildings
�First adopters of technology expected in critical infrastructure and high profile buildings
�Predictions need to arrive faster than the event develops (lead time>0)
Lead time: period after the forecast when it is still accurate and valid
Fire Test at BRE commissioned by Arup 2009
4x4x2.4m – small premise in shopping mall
190s – could this be forecasted ahead of time?
285s – could this be forecasted ahead of time?
316s – could this be forecasted ahead of time?
Coupled Fire Mechanisms
GAS PHASE – flame/smoke
� Turbulence
� Combustion
� Radiation
� Buoyancy
SOLID PHASE - fuel
� Heat conduction
� Pyrolysis/degradation
� Flame Spread
Fire dynamics are
governed by
coupled non-linear
processes.
NOTE: Combustion is only
one of the many
important mechanisms
in fire dynamics (typical
misconception)
Firepower – Fuel� Heat release rate (HRR) is the power of the fire (energy
release per unit time)
)()()( tAmhtmhtQ cc′′∆=∆= &&&
Heat Release Rate (kW) - evolves with time
Heat of combustion (kJ/kg-fuel) ~ fuel property
Burning rate (kg/s) - evolves with time
Burning rate per unit area (m2) ~ fuel property
Burning area (m2) - evolves with timeA
m
m
h
Q
c
′′
∆
&
&
&WPI
Burning rate (per unit area)
ph
qm
∆′′
=′′&
&
from Quintiere, Principles of Fire Behaviour
Heat of Combustion
from Introduction to fire Dynamics, Drysdale, Wiley
Lower order modelling: Two-Zone Model(after Zukoski, 1978)
Upper layer
Lower layer
Plum
e
Leak
Mass balance
upper layer
Flow rate of smoke in plume
Firepower
growth
Mass balance
lower layer
1D in space and transient in time. Simple model formulated on the fact that the
volume of a fire compartment is naturally split in two layers, hot smoke up and
cold air down. Smoke layers starts t the ceiling level and descends towards the
floor. Mass transfer between both layers is by the fire plume.
Jahn et al., Fire Safety Journal, 2011
Higher order model:
Computational Fluid Dynamics
3D in space and transient in time
State of the art is FDS - Fire Dynamics Simulator:
� LES code
� Mixture fraction
� Radiation
� Solid heating
� Open source, freely available
� Developed by NIST (USA)
� Computational time of a ~10 min fire in a typical
single office compartment takes in the order of
weeks to solve in a modern desktop PC.
� The most successful fire CFD code currently in use
Impossible and HPC
Two most common responses we got from experts when we first started to research the topic:
1. “That is Impossible, you are wasting your time” (note to Young Researchers: this reaction
indicated you are doing something right)
2. “No need to research, just take the best CFD model in town and run it as fast as possible using parallel computing, grid and HPC”
Round-Robin of Fire Modelling
�How accurate is the
state-of-the-art?
�International pool of
experts provided a
priori (blind)
predictions of a large-
scale fire experiment,
2006 Dalmarnock
Rein et al., Fire Safety Journal 44, 2009
High Density Instrumentation
8 Lasers
CCTV
ENLARGE ENLARGE ENLARGE ENLARGE
DeflectionGauges 20 Heat
Flux Gauges
270 Thermocouple
10 Smoke Detectors
14 Velocity Probes
10 CCTV
Abecassis-Empis et al., Experimental Thermal and Fluid Science 32, 2008.
Before/After
Average Compartment Temperature
Abecassis-Empis et al., Experimental Thermal and Fluid Science 32, 2008
Predictions of Firepower
Rein et al., Fire Safety Journal 44, 2009
Predictions of smoke layer
temperature
Predictions of smoke layer position
Round-Robin Lessons
� The state-of-the-art of fire modelling is
neither accurate nor fast enough for
forecasting
� Brute force forecasting provides excessive
uncertainty
� Firepower growth Q is an essential
variable, all others derive from it
Rein et al., Fire Safety Journal 44, 2009
Data Assimilationused in weather forecast
MODEL SENSORS
FUSION
ANALYSISForecasts
(with positive lead time)
Cowlard et al., Fire Technology, 2011
Forecast of average
temperature
• Current lead time is 3
days
• 10 years ago was 2 days
Inverse Modelling
Inverse modelling is like
imitating Sherlock Holmes
Steps in Data Assimilation:
� Identify the governing parameters – the invariants
� Quantify via sensor data the value of the invariants
� Run forecast with those parameters
� Weather: quantify initial conditions (IVP)
� Climate: quantify boundary conditions (BVP)
� Fire: none of the above
� The source term, firepower, drives fire
dynamics. Qmust be estimate first
Time
Fir
ep
ow
er
Sudden change of
conditions
(eg, window breakage)
Data Assimilation Concept
Cowlard et al., Fire Technology, 2011
Invariant values valid
New invariant values
sensormodel
222 ttSmhtQ c απ =′′∆= && )(
� It is a common observation that fires grow as t2
(radial spread at ~ constant rate)
� Invariant (αααα) is the governing unknown of the problem
�Other invariants related to ventilation, smoke, etc can be added as well
Source term - Invariants
heat of
combustion spread rate
burning
rate
α growth parameter
~ constant
�Forward fire model is
�A cost function is minimized:
which measures the distance between the observation yi
and the output of the forward model yi(α).
�α*= argmin(J(α)) = the invariant values sought
Inverse Modelling
Jahn et al., Fire Safety Journal, 2011
Minimization Technique
�For a handful of invariants, gradient techniques are much faster than heuristics
�Non-linear (NL) gradient technique typically needs >100 iterations
�Because each iteration requires to run the fire model at least once fi NL is not as practical for forecasting
�Solution: linearize
�Finding: yi(α) tends to be reasonably linear in compartment fires
�Linearizing around guess α0 and replacing into
cost function, gradient becomes
�Leads to a linear system
Tangent Linear Model (TLM)
Jahn et al., Fire Safety Journal, 2011
� Compartment 4 x 5 x 2.5 m
� Mattress fire
� Sensors: Temperature, ~uniform grid 10 per m3
� Synthetic data generated by CFD model (FDS)
� Invariants: spread rate, entrainment & transport time
Zone Model - Typical Fire Scenario
Jahn et al., Fire Safety Journal, 2011
This is not the most realistic
scenario, specially the high density
sensor array. But first attempts
ought to be conducted on simple
cases before moving towards
complex cases
Jahn et al., Fire Safety Journal, 2011
Medium fire (~mattress fire)Assimilate 5 Data points 9 Data points 13 Data points
HR
R (
kW)
Lay
er H
eig
ht
(m
)T
emp
erat
ure
(C
)
Upper layer
Lower layer
Plum
e
Leak
Jahn et al., Fire Safety Journal, 2011
Medium fire (~mattress fire)
Slow fire(ie, large wood slab)
Jahn et al., Fire Safety Journal, 2011
Medium fire(ie, large mattress)
Lea
d t
ime
(s)
Lea
d t
ime
(s)
Lea
d t
ime
(s)
Fast fire(ie, large polyurethane foam slab)
Lead Time defined as forecast <10% error in upper layer temperature
� Same compartment (4 x 5 x 2.5 m, mattress fire)
� Forward model is LES code FDSv5
� Invariants: spread rate, fuel flow (=burning rate) and
soot yield
� Sensors: Temperature and smoke at ceiling height
� Synthetic data by fine grid FDS (5 cm)
CFD forecast
Jahn et al., Adv Software Eng, 2012
� Speed up by coarse grid (25 cm)§
NOTE: Course grids cannot resolve
turbulence and other flow process of
importance. But forecasting ought to find a
compromise between speed and accuracy.
Note that in many weather simulations,
for example, Scotland is one single grid cell
CFD forecast
Jahn et al., Adv Software Eng, 2012
TLM vs. BLGF
Comparison of Tangent Linear
Method (TLM) and the quasi-
Newton method Broyden–
Fletcher–Goldfarb–Shanno (BLGF)
shows superior performance of
the TLM, both in accuracy and in
computation time for the problem
at hand
Jahn et al., Adv Software Eng, 2012
Two independent fires
Good convergence!
Effect of sensor type - Assimilation iter.
Jahn et al., Adv Software Eng, 2012
Poor convergence!
Adding ceiling smoke sensorUsing ceiling temperature sensors only
Good convergence!
Unknown location and size of fuel source
Near realtime with CFD forecast
� Our CFD forecast do not reach positive lead times yet
(current is ~ -4.5 min) because CFD is still too slow
compared to real time event.
� Our works has focused on minimizing the number of
iterations for convergence of invariant because:
� Each iteration involves a number of parallel CFD runs.
� A typical CFD run in FDS of a 5 min fire in a single office
compartment with a modern PC desktop takes weeks to solve
with a grid of 5 cm. With a course grid of 25 cm, it takes
10 min.
� High Performance Computing techniques can now
accelerate further the inverse problem and reach real
time CFD forecast (positive lead times)
�Data from Dalmarnock Fire Test One
�High density sensor array for temperature
�Forward model is FDSv5
Next challenge: CFD forecast using
sensor data from a real fire
Jahn et al., IAFSS, 2011
Rack 1 230 cm high –
near sofa
Rack 1 160 cm high –
near sofa
Rack 19 230 cm high –
near window
Rack 19 160 cm high –
near window
CFD forecast of real fire
Results show local sensor data can be used to forecast the
firepower in the whole compartment
Conclusions
� Sensor data from temperature and smoke field used to back calculate the fire growth
� Methodology is general and independent of the forward model
� Fundamental step towards the development of forecasting technologies
� Invariants accurately estimated in <1 min of fire time
� Positive lead times with zone model (~90 s)
� Near realtime CFD forecast (~-4.5 min)
� Coarse grids accelerate forecast up to 100 times without loss of accuracy due to the assimilation of sensor data
� High Performance Computing techniques can now accelerate further the inverse problem and reach real time CFD forecast (positive lead times)
"So easy it seemed, Once found,
which yet unfounded most would have
thought, Impossible!"
John Milton (1608 - 1674), English poet
New perspective
Paleofuture: forecast made in 1900 of the fire-
fighting in the year 2000
Villemard, 1910, National Library of France
Thanks!
Jahn et al, IAFSS, 2011
Jahn et al, Adv Software Eng, 2012
Jahn et al, Fire Safety Journal, 2011
Cowlard et al, Fire Technology, 2011
Based on the thesis of my PhD student Wolfram Jah
Inverse modelling to forecast enclosure fire dynamics
University of Edinburgh, 2010. is in open access at
http://hdl.handle.net/1842/3418
Research funded by BRE, EU Alban Scholarship and FireGrid
Three Invariants
Jahn et al., Adv Software Eng, 2012
Different initial
guesses
All three invariants converge. Soot the slowest
Good convergence!
Effect of sensor density9 sensors
Jahn et al., Adv Software Eng, 2012
Improvement with CFD assimilation window
Jahn et al., Adv Software Eng, 2012
General Forecasting Method
Jahn et al., Adv Software Eng, 2012
Flame Spread vs. Angle
Upward spread up to 20 times faster than downward spread
upward vertical spread
downward vertical spread
Test conducted by Aled Beswick BEng 2009
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