IMO (SLF 47/48) Passenger Ship Safety
2.1.4 Total Risk (Safety Level)
2.1.4.4 Evacuation and Rescue Analysis
domain of the ship. In other words, if damage(j)occurred, what is the proportion of systems supporting the functionality (or what proportion of the functionality) is available. This is currently under development both within SAFEDOR and through industry-funded projects. On the basis of the influence of this development on the risk evaluation per se (from the point of view of LSA availability), this can be ac- commodated by introducing the following definition for tf ail|j in Eq. (2.6).
tf ail|j(N)· tf ail(N)
1−p
lsa systems f aildamagej
(2.21)
Lifeboats
MES
Assembly Stations
Fig. 2.38 Abandonment studies using Evi
crew assistance. This new concept makes evacuation analysis much more rele- vant offering real “means” for enhancing passenger evacuation performance as well as incentivising passenger ship owners to improve emergency procedures.
Stemming from these developments, evacuation analysis in emergency situations through numerical simulations can now be undertaken meaningfully.
(b) The term “Evacuation” tends to be used interchangeably with that of “Muster- ing” or “Assembly” and in so doing the crucial element of vessel abandonment tends to be overlooked. Emphasis on quantification of time to abandon cannot be stressed enough. As shown in Fig. 2.37, this would involve, in addition to the assembly process (including counting of passengers), embarkation (into lifeboats and MES), launching of lifeboats and clearing the vessel.
All these would involve separate measurements using physical and numerical models (see Fig. 2.38).
(c) “Evacuability” post-accident, in addition to ensuring availability of emergency systems, the influence of floodwater/fire must be ascertained by using coupled flooding/fire evacuation models as described in (Vassalos 2006) and briefly out- lined next:
Evacuation Analysis in Flooding Scenarios
The output from PROTEUS3, including time histories of the vessel motions and accelerations, as well as floodwater mass, elevation and attitude in every modelled compartment of the ship, is incorporated into the evacuation model environment (Evi) as explicit semantic information concerning the effects of: deck inclination, ship motions and inaccessibility due to floodwater (Fig. 2.39).
The simulation imports motion and flooding data, which is processed to give deck inclination to the horizontal (level) position. Using inclination, a correction factor is applied to the maximum walking speed of an evacuee (agent), based on the results
Fig. 2.39 Evacuation analysis – flooding scenarios
0.0 0.2 0.4 0.6 0.8 1.0
0 0.5 1 1.5 2 2.5
immersion (m)
Speed reduction factor
Fatality
Fig. 2.40 Speed reduction factor due to effect of floodwater
of research undertaken in the MEPDesign project – this has been described in detail in (Vassalos et al. 2002). Thus, flooding data is used to control the awareness and walking speed of agents, reducing it as they become more affected by (immersed into) the floodwater, as illustrated in Fig. 2.40, (Guarin et al. 2004).
Evacuation Analysis in Fire Scenarios
In fire scenarios, the number of injuries/fatalities associated with a specific fire sce- nario depends on the number of people exposed to the resulting fire hazards.
In this respect, it is well known that when evaluating the consequences of fire effluent to human life, the basic performance criterion states that the time required for escape, normally referred to as RSET (Required Safe Egress Time) should be shorter than the time available for the fire and smoke hazards to reach untenable conditions, normally referred to as ASET (Available Safe Egress Time), (Cooper 2002).
RSET<ASET
The ASET is the interval between the time of ignition and the time at which conditions become untenable such that occupants can no longer take effective ac- tion to accomplish their own escape. Untenable conditions during fires may re- sult from inhalation of asphyxiant gases, exposure to radiant and convective heat and visual obscuration due to smoke. A quantitative approach to evaluate the above criterion was implemented by (Guarin et al. 2004, 2007a) and shown here in Fig. 2.41.
Figure 2.42 illustrates an example of this assessment for a large public space.
On the left hand side, a snap shot is shown of the time history of temperature cal- culated with a state-of-the-art field model (Guarin et al. 2007a). On the right hand side, egress simulations include a model for estimating the Fractional Effective Dose (FED) of heat of each occupant, based on the distribution of temperature in time and space. When the FED of an occupant exceeds the tenability criterion(FED>1), the occupant can be assumed to be incapacitated. The same can be followed for toxicity and visibility evaluation.
By adopting this approach, the actual human life loss is scenario-specific and is influenced by use of space, occupancy profile, and location of space within the main fire zone (MFZ) as well as the escape and evacuation arrangements. The criterion is evaluated in the space of fire origin and in spaces likely to be affected by smoke propagation within the same MFZ.
0.0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1 1.2
FED/FEC
Speed reduction factor
agent lost FEC
(smoke obscuration) FED (toxicity and heat fluxes)
Fatality
Fig. 2.41 Speed reduction factor due to fire hazards, based on conservative engineering judgement
Fig. 2.42 Left: temperature at 1.5 m height from floor level for a large public space after 4 min from ignition (Horvat et al. 2007) Right: Human injury analyses for the same scenario (Guarin et al. 2007a)