prof ed galea director fire safety engineering group ......e.r.galea@gre.ac.uk rmit melbourne 29 oct...
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e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Engineering Safety, Security and Efficiency
through Fire and Evacuation Simulation
Prof Ed Galea
Director Fire Safety Engineering Group
University of Greenwich
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
FSEG: Modelling safety and security• FSEG was Founded in 1986 by Prof Galea
in response to the Manchester Airport B737 fire.
• Today it consists of 30 researchers including:
– fire engineers, CFD specialists, psychologists, mathematicians and software engineers.
• Research interests include the mathematical modelling and experimental analysis of:
– evacuation dynamics in complex spaces,
– pedestrian dynamics in complex spaces,
– combustion and fire/smoke spread,
– fire suppression,
– homeland security
• Application areas include:
– aerospace, built environment, marine and rail.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Applications of the EXODUS software
Rail Stations
Large PAX Ships Naval Ships
Royal Ascot Historic Buildings
A380 – Super Jumbo Millennium Dome Stadium AustraliaAirbus flying wing
Canary Wharf
Beijing Olympic Stadium
WTC 9/11 analysis Pentagon Shield
Statue of LibertyForensic analysis
Rhode Island
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Applications of the SMARTFIRE software
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
FSEG Data Collection
Rail Car Evacuation
Cruise ship evacuation
at sea
Assist devices for PRMUnannounced Full
Building Evacuations
public eventComplex spaces
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
EXODUS Applications
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Underground station evacuation• LuL station with fire.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
buildingEXODUS and SMARTFIRE simulation of Station Nightclub fire
•Link fire simulation directly with evacuation analysis
•Directly expose agents to developing hazard environment
•Predict fatalities and injury levels.
• Last survivor evacuates after approx 127 seconds.
• Simulation predicts :
•84 fatalities compared with 100 in actual incident.
•25 serious injuries, of which 6 are life threatening.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Evacuation Times
How long to get everyone
out?
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
WTC1: evacuation 5-25 min into simulation
• Precise evacuation time for WTC1 is not known – 102 min to collapse.
• buildingEXODUS prediction of WTC1 evacuation suggests that the
evacuation time for 8239 people was 87 min.
• If fully occupied with 25000 people, predicted evacuation time 138 min,
resulting in 7500 fatalities.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
buildingEXODUS V6.0 Lift Model
• Full building evacuation using stairs alone often
not practical.
• Use of lifts have been suggested as the panacea
for all of our evacuation problems – but how
good are lifts?
• Most lift models and design engineers do not
include human behaviour, they treat people as
compliant ball-bearings, if assigned to use the
lift, the agent will use the lift!
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Stairs or Lifts? How will people
actually behave in an emergency?
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Why is it important to consider human
behaviour associated with lift usage
• Under what conditions will people wait for the lift?
• WTC survivor from the 77th floor of North Tower who
evacuated on 9/11 said:
“Let me add too that, at the 44th floor there was what they call
an inter-zone elevator bank, we were led off the stairwell at
the 44th floor and shown to that elevator where there are
hundreds of people milling and I looked at that and I
turned around to my team and I said ‘no, I am not waiting
for an elevator in a building on fire. Let’s go’ and I walked
back to the stairwell and they did too and then we proceeded
down”
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
• buildingEXODUS lift model includes kinematics of lift
operation AND human behaviour associated with lifts.
• The agents behaviour in utilising a lift is complex and
consists of:
– Deciding to utilise the lifts or the stairs.
– If using the lifts:
• Which lift bank to use
• When arriving at the lift bank, deciding whether or not to wait.
• If waiting, where to wait.
• How long to wait for.
• When lift arrives, boarding behaviour.
– Much of the behaviour used in the model is based on data collected
by FSEG from an international survey involving 468 people from 23
countries.
buildingEXODUS V6.0 Lift Model – Behavioural features
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
International survey: 468 people from 23 countries
Direction
As wait time
increases, %
electing to wait for
lift decreases
exponentiallyAs floor height
increases, % that
would wait for the lift
for a given time
increases
Less than 10%
prepared to wait
more than 15 mins
across all floors
Majority only
prepared to wait
up to 5 mins
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
WTC Evacuation using Lifts• So could the 25,500 people in the WTC have been evacuated
if they used lifts?
• The building had 20 express lifts (55 person capacity)
servicing the Sky Lobbies and 60 local lifts (12 person
capacity).• buildingEXODUS suggests:
• 1 hr 23 mins to clear
tower
• 40% faster than stairs
• 58% only use stairs
• 39% only use lifts
• it would have been
possible to get everyone
out before the building
collapsed.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Wayfinding
How do people find their way
out?
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
WTC WAYFINDING• WTC1/025/0002:
P “honestly I didn’t know where the evacuation stairwells where….. they say, … look for the exit signs when you go in a place, they really mean that because, y’know unless something’s happened before, you’re not go to be able to find it.
• WTC1/057/0002:
P “… we couldn’t at that point find the exit. Our stairwell had ended and there were no guide posts to go anywhere….so a number of people started searching for some place to go for another stairwell to go down from the 44th floor. Eventually someone found it so we continued down.”
• WTC1/087/BDAG
P “…we actually walked past the fire escape, kinda had to turn around and double back until we found the fire escape…
• Many people were unable to find the stairs, even though they had been in the building for months.
• Many people failed to see the emergency signage
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Sign Recognition Experiments• Experimentally examine how occupants interact with signage
in both normal movement and evacuation.
• Attempt to determine likelihood that those who can see a sign,
recognise the sign, correctly interpret the information and
follow the information.
•68 test subjects
•41 naïve subjects
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Sign Recognition - T intersection
• Female, naïve subject, 2 sec decision time, makes correct decision. From questionnaire subject said they saw and followed the sign.
• Female, naïve subject, 9 sec decision region, makes incorrect decision. From questionnaire did not see sign
• For T intersection, 61% of naïve subjects failed to “see” the sign.
• Of those who “registered presence of sign”, 100% followed instructions
• Average decision time for those who see sign 2.6 s, those who do not see sign 5.6s
•Correct choice •Incorrect choice
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Sign Recognition – Improved affordance• It is suggested that poor identification of signs is due to poor
affordance associated with signs.
• Can improve affordance of a sign in several ways:
• make larger – improve sensory and cognitive affordance
• introduce lights – improve sensory affordance
• introduce green lights – improve cognitive affordance
• introduce flashing lights - improve sensory affordance
• introduce running lights - improve cognitive affordance
• FSEG in collaboration with UK company EVACLITE
(www.evaclite.com) have come up with a sign addressing the
poor affordance issue by introducing running, flashing, green
lights to the sign.
• Sign is activated on alarm – Active Dynamic Signage System
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Sign Recognition – Improved affordance• Using the ADSS, the wayfinding experiments were repeated
in July 2012 with 48 unfamiliar participants
• 85% (41/48) of people ‘see’ the dynamic sign – an increase in
detection rate of 120%.
• 100% of people who see the dynamic sign follow the sign.
• The vast majority of people interpret the flashing arrow
correctly and find the new design useful.
Detected
sign?
DSS Conventional
sign
Yes 1.8 s 2.6 s
No 5.7 s 5.6 s
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Intelligent Signage Systems• As part of EU FP7 project GETAWAY, the ADSS concept is
being expanded to include Intelligent Active Dynamic Signage
System - IADSS.
• In addition to making the exit sign more noticeable:
• Signage system also indicates that an emergency exit
route is no longer considered viable
• ADSS is controlled via simulation and human intervention
to identify the optimal exit route given the current situation.
• Optimal route can be determined by faster than real time
buildingEXODUS simulation taking into consideration:
• Current population distribution and
• Spread of fire hazards (heat, smoke and toxic gases)
• Optimal route can also be determined by human operator
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
EXODUS Evacuation simulation
resultsADSS control
commandSensor
readings
The GETAWAY Intelligent Active Dynamic Signage System
CAE
DE EXODUS
Station CCTV
Live video input
FDS & ADSS control unitSensors DSS
Real time people counting data
Simulation request
Station Supervisor
Situational information collection Intelligent decision making
ADSS
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Intelligent Signage Systems• ADSS not only has application for fire applications but also
for terrorist situations.
• Using CCTV security staff identify regions were the hazard
(gunmen) is located and direct people away from the region by
activating appropriate signs
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
ADSS Showing Negated Route• ADSS extended to indicate that an evacuation route is no
longer considered viable.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
ADSS Negated Sign Concept• Survey involving 451 people from 10 countries
• 4 potential designs shown to each participant, without providing an
explanation of what the sign meant.
• Participants asked to write what they thought the sign meant.
Non-Fire
Correct
Fire
Correct
Total
Correct
93%
(193 total)
93%
(238 total)
93%
(431 total)
85%
(196 total)
83%
(240 total)
84%
(436 total)
72%
(182 total)
79%
(227 total)
76%
(409 total)
56%
(191 total)
63%
(239 total)
59%
(430 total)
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Exit A
Platform 2
Exit B
Exit CExit D
� T1 – 139 participants
Configuration and Signage Systems Tested
Exit A
Platform 2
Exit B
Exit CExit D
� Participants distributed across boxes 1-7 on platform
� T2 – 152 participants
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Trial 1 – 100% of participants
use their nearest exit. Signage
only accounted for 26.8% of
participant exit selection
Trial 2 – 63% of participants
use the indicated exit. 57% used
their nearest exit, compared with
100% in Trial1.
Comparing Trial 1 and Trial 2
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
� TS3 – 64 participants, distributed in box 1 and left blank area
TS3 – Configuration and Signage Systems Tested
Exit A
Platform 2
Exit BExit C
Exit D
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
• 66% of participants by-pass exits 1, 2 and 3 and utilise target exit.
• 34% of participants chose to use their nearest exit compared to 100%
TS3 2014, Trial 2
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
IADSS
EXODUS required 33 sec to perform all 7 evacuation scenarios
Ranking algorithm identified
optimal exit strategy and was able
to activate signage prior to alarm
activation
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
TS3 Questionnaire Analysis Results
Statement 1
“This sign assisted me in selecting an exit to
use/ which exit NOT to use/which exit not to
use and which exit to use.”
Level of agreementAgree /
Strongly Agree
Disagree /
Strongly DisagreeTotal
94% 3% 79
74% 19% 53
83% 13% 80
70% 25% 77
Weighted Average 81% 14% 289
� Participants’ level of agreement that the sign assisted them in
identifying an exit to use (TS3.2 and TS3.3).
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Exit Flows
Still Lots to Learn
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Exit Flows• The characterisation of exit flows is one of the
fundamental concepts in building design.
• Key parameter in building safety regulation all over the
world, values accepted and used for many years.
• In the UK for example, the unit flow through an exit is
defined as 1.33 people/m/sec.
• This is taken as a fundamental value to be applied in all
situations.
• But how fundamental is this parameter?
• How is it affected by:• Environmental conditions e.g. ambient temperature
• Occupant encumbrance e.g. people exiting a rail station or
airport with luggage?
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
COLD v WARM
COLD WARMFlow (ppm)
247.1
[242.0 – 252.0]
Flow (ppm)
278.0
[268.0 – 286.0]
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Impact of EnvironmentPeak period flow (ppm) Average
(ppm)
Average
(p/m/s)Time interval (sec)
5-10 10-15 15-20 20-25 25-30 30-35
Cold (ppm) 278 247 242 259 226 216 246 1.71
Warm (ppm) 360 316 292 244 236 220 278 1.93
• COLD: Max temp 9oC, light
snow on occasions
• WARM: Max temp 19oC, sunny.
• Average WARM exit flows
13% faster than the COLD exit
flows.
• Trials repeated 3 times, data
represents average of the three.
• Different populations for COLD
and WARM trials, but very
similar demographics.
• Initial packing density identical.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Impact of Encumbrance• Participants were given a range of different types of
encumbrance ranging from handbags to pushchairs and
bikes.
• Roller bags and baby buggy were weighted down with a
15kg sand bag.
• Smaller luggage items were packed with bubble wrap in
order to pack them out.
• Three levels of encumbrance were investigated involving
set proportions of the population carrying some form of
encumbrance. These were 0%, 40% and 60%.
• Each trial repeated 3 times.
• Efforts were made to ensure that the number and type of
encumbrance was the same through repeat trials.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Types of Encumbrance
Type Available 40% 60%
Handbag 66 34 49
Satchel 36 15 29
briefcase 24 11 19
rucksack 22 9 13
Small
roller bag
3 2 3
Large
roller bag
3 2 3
bike 3 1 3
Baby
buggy
3 2 3
160 76 122
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
0% v 40% v 60% Encumbrance
0% Baggage
Flow (ppm)
278.0
[268.0 – 286.0]
Flow (ppm) 233.7
[226.3 – 243.4]
40% Baggage 60% Baggage
Flow (ppm) 251.4
[246.9 – 255.4]
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Impact of Encumbrance
• As encumbrance level increases, exit flow decreases.
• Compared to the case where the participants have no encumbrance, exit flow
decreases by 10% when approx 40% of participants are encumbered and 16% when
60% are encumbered.
• Increasing encumbrance level from 40% to 60% results in a 7% decrease in flow.
• The natural variation in the average exit flow rate increases as the level of
encumbrance increases from 40% to 60%.
Flow (p/min)
Unit Flow (p/m/s)
0% Encumbrance 40% Encumbrance 60% Encumbrance
Flow (unit flow) 278 (1.93) 251.4 (1.75) 233.7 (1.62)
% Difference -- 9.6 15.9
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Use of Bollards• Bollard Arrays are intended to protect critical infrastructure from
hostile vehicles or “car bombs”
• Had a bollard array been present around the entrance to Glasgow
airport it would have prevented the vehicle from approaching the
airport terminal.
Glasgow airport
30 June 2007
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Use of Bollards• Today, security bollards are a common sight in London and
other cities around the world.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
4.5m exit, No BA, 1m, 2m and 3m BA
1m BA
Unit Flow (p/m/s)
1.76 (-9%)
2m BA
Unit Flow (p/m/s)
1.75 (-9%)
3m BA
Unit Flow (p/m/s)
1.79 (-6%)
No BA
Unit Flow (p/m/s)
1.92
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Modelling VS Experiment: 4.5m exit, 1m BA
Time (s) 3 Trial Average
(ppl/m/min)
100 Sim Average
(ppl/m/min)
Difference
(%)
5-10 112.9 107.7 -4.6%
10-15 104.9 109.2 +4.1%
15-20 110.2 106.8 -3.1%
20-25 98.7 108.3 +9.8%
25-30 100.4 100.0 -0.4%
Average 105.4 106.4 +0.9%
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Research: Urban Scale
Evacuation and Crowd
Dynamics
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Large crowd simulation and visualisation
• Trafalgar Square
demonstration:
125,000+ people
simulation
• Love Parade Disaster
reconstruction: 100,000
people simulation.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Assembly and bus boarding process
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
EXODUS Coupled to Flood Model• As part of a proof of concept project, EXODUS was coupled to a flood
model.
• Rising water levels predicted by the flood model could be passed onto
EXODUS and represented within the evacuation model.
• Agents within EXODUS are then given behaviours to move away from
rising water levels.
• When water level rises above head height, agent is assumed to have
drowned.
• Model is still in early phase of development but demonstrates that it is
possible to couple advanced egress models to advanced CFD based flood
prediction models.
• EXODUS capabilities to read map geometries, to utilise multiple
computers for rapid computation and to utilise hybrid space concept
enable it to be used to model vast regions in relatively short times.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
EXODUS Coupled to Flood Model• 10,000 agents, city section of 1 km x 1 km – 20 minutes of simulation.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Large Scale Disaster Planning and Management• As part of EU FP7 project IDIRA EXODUS is being configured
for use in large scale urban disaster applications.
• This could be for applications in floods, Tsunami, earthquakes,
forest fires, etc.
• Software is used to assist in planning large-scale movement of
people and for use during an incident to assist in management.
• Models of urban regions can be pre-built and stored for use during
an incident or regions of interest can be built during an incident.
• During an incident the model can be reconfigured as new
information is made available as the scenario changes.
• e.g. loss of evacuation routes, changes in status of refuge areas, etc.
• Web Application – An easy to use GUI for clients to interact with
the EXODUS simulation tool.
– OpenLayers – Client application used to display base maps
(Googlemaps/OSM) and Overlays (Population density contours)
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Tokyo Japan
21 November 2014
FOREST FIRE• Swinley forest fire was the largest in Berkshire’s history
• 5 May 2011, 300 hectares of forest
• Very close to built up areas
• 1220 people directly affected: TRL - 800 , Business Estate - 200, Pub - 200,
Residential dwellings 20
• Close to the high-security Broadmoor Hospital
A3095
Broadmoor Hospital
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Tokyo Japan
21 November 2014
FOREST FIRE• Conditions were variable on the day.
• Concerned of repercussions if wind changed.
• Spread of fire modelled using Prometheus by Tom Smith KCL
• Considered what would have happened if wind changed direction.
• How long to evacuate threatened population?
• Actual region burnt• Simulated burn region given wind change
Business
Estate TRL
Residential
Dwellings
Pub
Assembly
Area
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Evacuation ScenarioEvacuation Scenario:
Assume evacuation initiated at the start of the fire.
TRL:
• Main exit, turn right onto B3430, go to Assembly Area
• Initiated by phone call at t = 0 s, RT = 1 – 2 min
Business Estate:
• Main exit, turn right and right onto B3430, go to
Assembly Area.
• Initiated by phone call at t = 0 s, RT = 1 – 2 min
Pub:
• Exit, turn left onto B3430, go to Assembly Area.
• Initiated by police visit at t = 0 s, RT = 0.5 – 1 min
Residential Dwellings:
• Follow B3348 and then A3095 towards the pub, turn
left onto B3430, go to Assembly Area.
• Initiated by police door to door, requires 5 min to reach
first house, 5 min to get ready, 1 min to next house.
Assembly Area
TRL
Pub
Business
Estate
Residential
Dwellings
House
B3348
B3430
A3095
1.5 km
1.7 km
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Assembly Simulation •Each dot represents an area of 6m x 6m, colour represents population density
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Safety MarginsSafety of Pub
• Pub clears point (B) at 4 min 28 sec.
• Pub assembles at 18 min 35 sec
• Fire approaches A3095 at 91 min.
• Pub has safety margin of 86 minutes.
Safety of TRL
• TRL clears point (A) in 10 min 45 sec.
• TRL assembles at 35 min 22 sec.
• Fire approaches TRL at 301 min,.
• TRL has a safety margin of 290 min.
Safety of Business Estate
• BE clears point (E) at 13 min 13 sec.
• BE assembles at 51 min 44 min
• Fire approaches BE at 392 min.
• BE has a safety margin of 378 min
Safety of Residential Dwellings
• RD clear safe point (C) at 53 min 37 sec.
• RD assemble at 1 hr 11 min 08 sec
• Fire approaches A3095 at 91min.
• RD have a safety margin of 31 min.
• Fire reaches A3095 at 126 min, a critical
point deeming that section of the road is
unusable.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Assembly Times• Overall Average Assembly Time: 1 hour 11 min 08 sec
• Pub Assembly Time: 19:01; average travel distance: 819m
• TRL Assembly Time : 35:22; average travel distance: 1452m
• Business Estate Assembly Time: 52:11; average travel distance: 2338m
• Houses Assembly Time: 71:01; average travel distance: 2726m
Pub TRL Business
Estate
Houses
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Fire and Evacuation in
Domestic Dwellings
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Source: Fire Statistics Great Britain, 2011-2012, Department for Communities and Local Government
0
10
20
30
40
50
60
70
Per
centa
ge ADFs as a %age of all
fires
ADF deaths as a %age
of all fire deaths
• Proportion of fire-related deaths and injuries caused by ADFs remained between 58% and 70%.
• Despite reduction in number of fires, proportion of ADF-related injuries and deaths unchanged.
• While likelihood of having a domestic fire have decreased, when a domestic fire does occur, the likelihood of being injured or dying has not decreased.
HUMAN BEHAVIOUR IN DWELLING FIRES
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
HUMAN BEHAVIOUR IN DWELLING FIRES• Why study human behaviour in dwelling fires?
– While the number of UK accidental dwelling fires is falling year on year, percentage of injuries and deaths incurred in these fires has not decreased and so the risk of harm when experiencing a dwelling fire remains unchanged.
– Must determine why so many injuries and fatalities in ADFs.
• FSEG, in collaboration with Kent Fire and Rescue Service through a KTP funded by the EPSRC and TSB, are undertaking a project to understand human behaviour in domestic fires – project LIFEBID aims to:
– Better understand how people respond to a developing emergency
– Inform emergency services how best to advise the public on what to expect and do in the case of a dwelling fire
– Reduce the number of negative outcomes from dwelling fires
– Advise on strategic investment of resources to achieve best effect
– Measure the effectiveness of interventions
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
BEHAVIOURAL RESPONSE TO FIRE• As part of the LIFEBID project a survey was developed to assess
public response to a developing fire.
• Participants shown a segment of video of a developing kitchen fire and asked if they felt that they could:
– Safely enter the room to tackle the fire.
– Safely extinguish the fire.
• Participants shown video for a only a few seconds, as if they just briefly witnessed the fire and had to make a rapid decision as to what they should do.
• Participants split into three groups and shown the fire and smoke video representing:
– Early Fire Growth Stage, most people should be able to safely extinguish fire, room safe to enter
– Mid Fire Growth Stage, unlikely most people would be able to safely extinguish, room is verging on dangerous to enter.
– Late Fire Growth Stage, most people would not be to safely extinguish fire, room is not safe to enter.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
BEHAVIOURAL RESPONSE TO FIRE
•Early Stage: most people would be able to safely extinguish the fire.
•Mid Stage: some people would be able to safely
extinguish the fire.
•Late Stage: most people would not be to safely extinguish the fire.
Do you believe that you could
safely extinguish the fire?
Stage of fire
development
No Not Sure Yes
- Early/47 9% 11% 81%
- Mid/47 17% 36% 47%
- Late/47 47% 23% 30%
• Could people be reasonably expected to safely extinguish the fire in:
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
LIFEBID FIRE BEHAVIOUR STUDY• Males more likely to tackle large fires
MALES Do you believe that you could safely extinguish
the fire?
Stage of fire
development
No Not Sure Yes
- Early/20 15% 20% 65%
- Mid/25 12% 32% 56%
- Late/32 34% 31% 34%
FEMALES Do you believe that you could safely extinguish
the fire?
Stage of fire
development
No Not Sure Yes
- Early/27 4% 4% 93%
- Mid/22 23% 41% 36%
- Late/15 73% 7% 20%
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
• LIFEBID has moved onto the second stage of the study.
• Anyone who has experienced a fire in the past 12 months in
their home – however small – can take part in the online
survey.
• The aim of the survey is to understand people’s actions and
decisions during a fire in the home.
• Anyone who has experienced a fire in a domestic
environment can complete the survey, not just those living in
the UK.
• Details can be found on the FSEG web site
at: http://fseg.gre.ac.uk/fire/lifebid.html
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
maritimeEXODUS
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
• Royal Caribbean vessel Jewel of the Seas• Carries approx 2500 pax and 842 crew across 12 pax decks• The vessel has:
• 18 assembly stations in 4 areas spread over 2 decks• 7 MVZ
• The public spaces include:• Accommodation areas,
• Retail areas,• Restaurants, • Bars, • Theatre,• Cinema,
• The Assembly exercise was carried out on the first leg of its Baltic Sea trip, between Harwich (UK) and Copenhagen (Denmark)
• The assembly exercise took place on 31st July 2010 at 10:00
• 2292 paxs on board
• 1950 paxs with tags assembled
• Assembly required 29 mins
Full-Scale Cruise Ship Evacuation at Sea
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
maritimeEXODUS predictions
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Simulated Assembly for 200 heel • 20o Heel results in a 23% increase in the average total assembly time for
the IMO day case scenario i.e. increase from 17.2 min to 20.7 min
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
airEXODUS and
SMARTFIRE
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Rupture Scenarios with Wind
• Exit Scenario L1, R1, R3, R4
R4 not used due
to spread of fire
R3 not viable
after 90 s
�297 passengers and crew
�Local flashover occurs at 55 s, much
sooner than the required evacuation time;
� 55 fatalities occur
� Evacuation time 270.1s
Fuselage rupture
due to crash
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
SMARTFIRE GPU
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
SMARTFIRE – GPU IMPLEMENTATION
� Currently SMARTFIRE run times can be reduced using parallel version on multi-core CPUs and networked PCs.
� Present development of SMARTFIRE-CUDA produces solver speed-ups of 40-50 times faster than the present version of serial SMARTFIRE.
� These reduced time frames lead to greater possibilities in using CFD, e.g. fully 2 way coupled fire and evacuation modelling.
� In the future it is planned to extend the SMARTFIRE-CUDA to also run on networks or clusters of GPU equipped PCs to deliver vastly reduced simulation times.
� Next generation SMARTFIRE will be able to use Graphics Processing Units (GPUs), e.g. NVidia Graphics Cards and Tesla Cards.
� GPUs are orders of magnitude faster for certain computational operations (mostly SIMD).
� Are generally “cheap” (< $1000) and give high Gflops/$.
� Suitable card may already be present on your PC!
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Standard one-way coupling of
SMARTFIRE and EXODUS
is expanded to include two-
way coupling through
enhanced performance of
GPU version of SMARTFIRE
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Two-way Coupling of SMARTFIRE
GPU and buildingEXODUS
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
VIRTUAL TRAINING
ENVIRONMENTS
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
AUGGMED – EU HORIZON 2020 Programme� The AUGGMED platform will generate non-linear scenarios for special forces dealing with
terrorist situations in crowded public spaces
� Engine makes use of high fidelity simulation of crowd behaviour (EXODUS), fire/toxic gases
(SMARTFIRE), explosions, etc.
buildingEXODUS
SMARTFIRE
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
AUGGMED ENVIRONMENT
SMARTFIRE
UNITY3D
buildingEXODUS
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
AUGGMED ENVIRONMENT
AUGGMED environment will allow users to:• Select a particular simulated blue team member
and assume control of that agent
• All actions of selected agent dictated
by real player rather than EXODUS
• In VR user is within the VR environment
playing the scenario sitting at a desk
• In MR user is located in the targeted installation
viewing the real structure through head
mounted display and viewing virtual people
(civilians, red team members) as participants
in the scenario
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Damage Control Simulation Environment – DSTO
• FSEG in collaboration with DSTO are developing DCSE to:
– assess effectiveness of current procedures. – develop new procedures,– Train crew, and– On-board live damage control decision support.
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Modular View of Integrated Survivability Architecture
• Eventual architecture will link many key ship survivability and simulation capabilities into the DCSE.
• The Phase 1 prototype will include Blast Damage, Fire, Crew Behaviour within the proposed simulation environment.
maritimeEXODUS
SMARTFIRE
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
Logical Architecture of the DCSE
e.r.galea@gre.ac.uk http://fseg.gre.ac.ukRMIT Melbourne
29 Oct 2015
CONCLUDING COMMENTS• Safe evacuation is challenging and requires careful planning, it doesn't
just happen.
• Use of reliable modelling tools in conjunction with good dataenable fewer arbitrary assumptions to be imposed, allowingconditions to be modelled rather than assumed.
• Simulation can be used to assist in planning to ensure:
– efficient throughput,
– comfort,
– safety and
– security.
• Finally, while it may be appealing to make simplifying assumptions
concerning human behaviour it is important to remember:
There’s nowt so queer as folk
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