Flooding Risk Assessment Background

IMO (SLF 47/48) Passenger Ship Safety

2.2 RBD Case Story: Large Passenger Vessel

2.2.1 Building Blocks of Risk-Based Design

2.2.1.2 Flooding Risk Assessment Background

Collision and grounding/stranding are the largest contributors to the risk of sink- age/capsize for passenger-carrying vessels. The probability of survival and even- tually the time to sink/capsize are crucial factors in determining the actual level of safety of a ship design. Whilst the former can be reasonably estimated using

empirical methods, the latter (time to sink/capsize) strongly depends on the geom- etry, topology and status of the internal compartmentation and openings (including doors, ducts, valves, etc) in addition to the random sea environment.

The dynamic response of a damaged vessel, and the progression of floodwater through it in a random seaway form a highly non-linear dynamic system, the be- haviour of which can only be assessed through time domain simulation. Building on this view, the University of Strathclyde began to develop the first-ever numerical model of this kind over 25 years ago. Since then, this model has been amply vali- dated and calibrated through its application in both research and consulting work, before arriving at its current version, PROTEUS3. This software suite is capable of simulating the vessel’s behaviour (6-dof motions at zero or forward speed of in- tact or damaged ships, the latter of single or multiple compartment configuration) as well as the evolution of transient and progressive flooding through any damage compartment configuration and any shape and position of openings through which flooding takes place. In addition, a number of non-linear effects can be incorporated, such as wave generated drift, wind loading, dynamic effects of cargo shifting, im- pulsive excitation and mooring forces, among others. In PROTEUS3, the complex behaviour of floodwater is modelled with a simplified method, developed as an alter- native to RANSE CFD techniques. This technique derives from an approximation of floodwater transfer by pendulum-like movement driven by ship-motion-related/

gravity acceleration field and constrained by internal compartment geometry on one hand and undisturbed floodwater free surface on the other. Thus, fully non-linear interactions between the ship and floodwater, treated as two interdependent, albeit separate, dynamical systems is represented meaningfully and with sufficient engi- neering accuracy. The output from PROTEUS3, including time histories of the ves- sel motions and accelerations, as well as floodwater mass, elevation and attitude in every modelled compartment of the ship, is incorporated into the evacuation simula- tion environment (Evi) as explicit semantic information with the following effects:

• Deck inclination: Asymmetric flooding will heel the ship making it more difficult to walk and reducing the speed of agents;

• Ship Motions: Dynamic motions will affect peoples’ orientation and movement capabilities, consequently, agents will move more slowly, make wrong decisions or may fall over;

• Inaccessibility: Flooding will make some areas of the ship inaccessible. This means that for people on lower decks certain evacuation routes may become unavailable.

The simulation imports motion and flooding data, which is processed to give deck inclination to the horizontal (level) position. Using inclination as input, a correction factor is applied to the maximum walking speed of an evacuee based on results of research undertaken in the EU FP4 MEPDesign project – this has been described in detail in (Guarin et al. 2004). Thus, flooding data is used to control the aware- ness and walking speed of agents, reducing it as they become more affected by the floodwater, as illustrated earlier in Fig. 2.40.

All the above-listed effects, would affect the time needed for orderly assembly and eventually, the time needed for safe evacuation of all people on board. There- fore, for any predetermined flooding scenario, the integrated simulation environment

offers the means to evaluate set safety objectives. If these were not met, the effec- tiveness of potential solutions could be evaluated until an acceptable solution was found. This iterative process, allowing for a direct link between a given scenario and risk, in terms of pertinent parameters affecting frequency and consequences, hence risk prevention/reduction, affords an effective way of “de-risking” a passenger ship from collision/grounding risks.

Flooding Design Scenario

Containment of collision∩flooding risk has recently been advanced to a stage at least comparable to that of fire, particularly so with the development of harmonised regulations for damage stability calculations (SOLAS 2009). The relevant design it- eration is shown in Fig. 2.44. In the process depicted in (Vassalos 2004), it is implied that if a collision damage case is “critical” (i.e. likely and probably non-survivable), there should be made an effort to find solutions (RCOs) to prevent the vessel from capsizing/sinking. These solutions may be of local and/or global character, enabling the vessel to survive in a “habitable” manner whilst waiting for help or to reach a port of safe refuge (SLF 47/48). When finding cost-effective solutions to survive collision damage passively becomes difficult (i.e. when the “damage threshold” has been exceeded) then the focus might shift to mitigating the ensuing risk to human life. This entails ensuring that all people onboard can be evacuated safely. In this sense, the expected time to capsize should be evaluated in conjunction with the

Collision

Grounding Fire Etc.

Accident Categories:

Identification of Design Scenarios

Design

Survivability Analysis

Critical Case?

Improve Survival Feasible?

Make sure all people can be evacuated safely within the

time available!

Survivability analysis tools

Evacuation analysis tools

yes yes

Risk Analysis:

Find solutions (RCO) to

improve evacuability! no Demonstrable?

yes Acceptable Design

Solution Elements of A index calculations HAZID

no

Fig. 2.44 Design iteration for collision damage (flooding scenarios)

expected time needed for evacuation. If this assertion can be demonstrated (through simulation using first-principles tools), then the risk implicit in the resulting design concept could be rendered as low as best knowledge available permits. This ap- proach demonstrates that ship safety, ultimately risk to human life, can be evaluated explicitly and more rationally than just by following the rules.

Figure 2.45 presents schematically typical probability curves for time to cap- size/sink and for surviving a given sea state (represented by significant wave height, Hs) corresponding to a damaged vessel subjected to progressive flooding as a func- tion of loading condition (represented by metacentric height –GM– and freeboard).

Figure 2.46 illustrates the evaluation of potential loss of life through passen- ger evacuation advanced simulation tools, taking as input the available time to sink/capsize deriving from flooding survivability analysis. The figure shows a typi- cal passenger objective completion curve and the quantification of the ensuing risk in terms of potential loss of human life (shaded area).

Deriving from the probabilistic rules for damage stability (SOLAS 2009) and building on the elements comprising Index-A, affords a way of identifying the relative risk contribution of each collision∩flooding scenario at an early design stage and hence devise an effective means of risk reduction by focusing primarily on the high risk-contributing loss scenarios. This concept is illustrated in Fig. 2.48 for a RoPax vessel (this is an actual study case). In this figure, a point on the hor- izontal axis corresponds to the mid coordinate of the flooded compartments. The

“relative risk” of non-survival, pi·(1−si), is plotted on the vertical axis. For a spe- cific damage location, there may be several damage case scenarios depending on the extent of flooding (longitudinally, vertically and transversely). The non-survival probability (relative risk) can be used to identify high-risk areas of the watertight subdivision; design changes in those areas will be the most effective in reducing risk, and of course in improving the subdivision index. This goal can be approached

Hs [m]

GM intact [m]

freeboard, compartmentation

Probability of sinkage/capsize

Time to sink/capsize

UNSAFE REGION

→

100% sink/capsize

SAFE REGION

→

0% sink/capsize Potential for

Design and/or Regulations

Fig. 2.45 Consequence analysis of a flooding loss scenario (time to capsize)

Time to capsize

P otential loss of life

Fig. 2.46 Consequence analysis of flooding loss scenario (risk quantification)

either at scenario or at ship level, the latter by setting up an optimisation problem as explained in the following section.

The flooding design scenario presented here is a night time scenario focusing on an existing large passenger Ro-Ro vessel with 2502 passengers and 190 crew, which at her current configuration achieves an Attained Subdivision Index of A= 0.751, whilst the Required Index has a value of R=0.821. This means that an improvement ofΔA=0.07 is needed for the vessel to comply with the probabilistic rules. Figure 2.47 illustrates the cumulative curve of the quantity p.v.(1 – s) (where

“v” is a factor limiting vertical extent above the waterline) for all damage cases calculated for this vessel. It shows that in order to achieve the improvementΔA= 0.07 to ensure compliance, i.e., A≥R, all damage cases with p.v.(1–s)>0.0048 need to be addressed.

The longitudinal location of all damage cases with s<1 – damage scenarios with p.v.(1 – s) above 0.0048 – represent critical scenarios with high likelihood in terms of piand low survivability in terms of(1−si). This analysis shows that there are 12 cases with the highest potential to improve A (if “s” is made equal to 1 for these cases, then A will be exactly equal to R). The longitudinal location of these damage cases is illustrated in Fig. 2.48. It should be noted that all critical damage cases are either two- or three-compartment damages (7 two-compartment and 5 three-compartment), hence cases that known design measures can be applied for improving the vessel survivability. It goes without saying that it might be more cost-effective to deal with, say, all two compartment damages even if their relative

Fig. 2.47 Cumulative dA(risk) for selection of critical damage cases

0.000 0.002 0.004 0.006 0.008 0.010 0.012

0 20 40 60 80 100 120 140 160 180 200

XM (m)

dARisk = P.V.(1-S)

1-zone 2-zone 3-zone 4-zone 5-zone crit

0.0048 Engine Room

(Comp. 3-4)

←

←

Fig. 2.48 Identification of critical design scenarios (basis ship)

risk contribution is less than the aforementioned value but it makes good sense to focus on high risk areas from top down.

The simplest way to proceed from here is o consider individually critical design scenarios (if only a limited number should be examined) with a view to assessing and negating potential loss of human life trough local design measures (RCOs – such as partial bulkheads, subdivision of side casings, void tanks, double hill and so on).

To this end, survivability and time evolution of non-survivable scenarios need to be addressed, followed by assessment of Evacuability for the latter to allow the designer

Collision Collision

Lower compartments flooded Lower compartments flooded Heel 20

degrees Heel 20 degrees

Heel 75 degrees Heel 75 degrees

Vessel Sinks Vessel

Sinks 0’ 10’ 30’ 60’ 70’

Time (minutes)

0’ 10’ 30’ 60’ 70’

Time (minutes)

Agents cannot move

Reduced reaction time Alarm system

& Crew effect Reduced reaction time Alarm system

& Crew effect

Reduced walking speed Increasing heeling angle

Reduced walking speed Increasing heeling angle

Severely reduced

“walking” speed

Excessive heeling angle

Severely reduced

“walking” speed

Excessive heeling angle

Embarkation Limit !!

Embarkation

Fatalities ! Fatalities !

Fig. 2.49 Time evolution of the collision damage scenario

to identify and rank RCOs based on cost-effectiveness. From Fig. 2.48 it would appear that the most “risky” scenario is engine room damage – compartments 3–4). Using IMO Demographics (Vassalos et al. 2003), results from Evi show that after 31 min only 2,281 passengers will have abandoned ship, thus leaving 411 passengers in assembly stations (potential casualties). This is, of course, unacceptable and hence cost-effective preventive and mitigating measures need to be identified to contain risk, as explained in Fig. 2.44. The time evolution of this scenario and the effect that it would have on passenger evacuation, hence the ensuing risk, are shown in Fig. 2.49 below.

The optimum local solution identified to address this critical flooding scenario comprises a local repositioning of transverse bulkheads together with 1.2 m double hull in the engine room, up to the subdivision deck. It is interesting to note that using this solution alone results in Index-A of 0.823(R=0.821), hence compliance with the rules. A number of critical scenarios, however, as shown in Fig. 2.50, still remain critical. In addition, a close examination of Fig. 2.50 helps to demonstrate that even local small changes on a ship may have a wider and profound effect; hence considering isolated scenarios and equivalent safety might lead to overlooking the wide-ranging influence that invariably these changes have on the ship as a whole.

0.000 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.010

0 20 40 60 80 100 120 140 160 180 200

XM (m)

dARisk = P.V.(1-S)

1-zone 2-zone 3-zone 4-zone 5-zone

←0.0048

Fig. 2.50 Identification of critical design scenarios (design solution 1)

2.2.1.3 Fire Risk Assessment