numerical forecasting of fire dynamics (plenary yic eccomas)

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?

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


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


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


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)


� 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


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


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


Medium fire (~mattress fire)

Slow fire(ie, large wood slab)


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


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


Two independent fires

Good convergence!

Effect of sensor type - Assimilation iter.


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


near sofa


near window


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


Different initial

guesses

All three invariants converge. Soot the slowest

Good convergence!

Effect of sensor density9 sensors


Improvement with CFD assimilation window


General Forecasting Method


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

numerical forecasting of fire dynamics (plenary yic eccomas)

Documents