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Probabilistic Framework for Onboard Fire Safety

Fire Ignition Model Specification (D1.2)

Nikos Themelis (NTUA)Mermiris (SSRC), Wenkui Cai

- document author -

Kostas Spyrou

- document approved by -

15 June 2010

- submission date -

Probabilistic Framework for Onboard Fire Safety

Fire Ignition Model Specification (D1.2)

(NTUA), George , Wenkui Cai (SSRC) NTUA, SSRC

- organisation name -

4.0

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PU

- distribution level -

Probabilistic Framework for

Document history

Revision number Date Remarks

1.0 27/11/2009 Draft document for comments and input by partners

2.0 01/04/2010 Document for deliver

3.0 26/05/2010 Final draft for comments and input by partners

4.0 15/06/2010 Final version of the document

Executive summary

Despite continuous efforts in the maritime industry during the last twenty years, fire remains the most frequent type accident onboard passenger ships, albeit less catastrophic compared to collision and grounding. This report describes the development of a model for the prediction of ignition frequency in various spaces onboard a passenger ship. The development is consistent with the risk-based design methodology.

The input of the ignition model is provided on the basis of (i) historical data (regarding fire incidents) amassed in the database of Task 1.1, and (ii) statistical data for floor areas of various spaces under the assumption that the flammable contents of an onboard space have some degree of correlation with the size of the room expressed with this variable. All formulations are expressed in terms of the 14 SOLAS spaces.

Considering that the floor areas introduced in the calculation of the ignition frequency are extracted from the GA of the ship (i.e. in the form of direct design input), one of the foci of this report is the derivation of the historical frequency of ignition, which comprises database frequencies per ship-year, the number of spaces and typical area for each space. The Rayleigh distribution represents well the distribution of areas per SOLAS space. The typical area per space is represented by the 50th percentile of the corresponding distribution.

The derived frequencies are compared against respective frequencies from the building industry and, although the available data is not sufficient to draw concrete conclusions, the building ignition frequencies fall within the interval defined by the minimum and maximum frequencies for ships.

Except from the probability of ignition for each SOLAS space category based on the developed database incidents, characteristics related with the incipient stage, i.e. the period until established burning occurs, should be determined also. These characteristics relate with the intensity of fire hazards, such as the fire load and the products that can be released. A probabilistic model is thus developed for the estimation of the duration and type of the incipient stage, considering the type of fuel inside the space and the strength of ignition source. Data from the database regarding the ignition sources for each space category can be implemented in the model. In addition, a methodology for the calculation of fire load density has been developed, where the mass of the combustible materials present in the space as well as their type and percentage of contribution to the total combustible mass have been accounted for. Due to the uncertainty of the above parameters, probability density functions rather than single values have been produced.

The type of fuel available as well as the type of ignition affects the amount of fire products such as smoke, carbon monoxide and dioxide which have a significant impact on human safety onboard. A method that generates distributions of the above parameters has been developed.

The models described in this Task can be utilised later in the Fire Scenario Generation (Task 1.4).

Contents

1 Introduction ............................................................................................................................ 6

2 Risk-based design ................................................................................................................... 7

2.1 Introduction to risk-based design ............................................................................... 7

2.2 SOLAS 2009 Vs Risk-based design approach to damage stability ......................... 9

2.3 Fire risk analysis .......................................................................................................... 10

3 Ignition probability ............................................................................................................. 12

3.1 Space categories .......................................................................................................... 12

3.2 Mathematical model for ignition frequency ........................................................... 13

3.3 Historical frequency of ignition ................................................................................ 16

3.4 Benchmarking ............................................................................................................. 20

3.5 Case Study ................................................................................................................... 22

4 Ignition characteristics ....................................................................................................... 23

4.1 General framework of fire development in an enclosure ..................................... 23

4.2 Ignition or pre - growth phase ................................................................................. 25

4.2.1 General scope of the analysis ........................................................................... 25

4.2.2 Types of ignition and ignition sources ............................................................ 25

4.3 Probabilistic model for piloted ignition: flaming combustion ............................. 26

4.3.1 Demonstration of the model ............................................................................ 28

4.3.2 Verification of the model .................................................................................. 30

4.4 Smouldering combustion of upholstered furniture ............................................... 31

5 Fire load properties ............................................................................................................. 34

5.1 Description of the methodology .............................................................................. 34

5.2 Demonstration of the methodology ........................................................................ 35

5.3 Relevant data from the literature: Comparing the results ..................................... 37

6 Fire effluents and species production .............................................................................. 43

6.1 Introduction and scope ............................................................................................. 43

6.2 Example ....................................................................................................................... 44

7 Conclusions ......................................................................................................................... 47

References .................................................................................................................................... 48

Appendix I – Rayleigh distribution .......................................................................................... 51

Appendix II – GA of two MVZs ............................................................................................. 52

Appendix III – Data from the literature .................................................................................. 56

Fire Ignition Model Specification (D1.2) 6

1 Introduction Despite consistent efforts towards prevention and mitigation of fire occurrences onboard passenger ships, both at IMO and ship operator level, fire remain the most frequent accident; albeit, as evidenced from accident statistics, less catastrophic compared to collision and grounding, (Figure 1).

Figure 1 – Frequency of collision, grounding and fire accidents in passenger ships, (Nilsen, 2007).

FIREPROOF is set to develop a probabilistic framework for fire risk analysis that would rationally capture the causes and the effects of fire occurrences in existing and future designs and would provide cost-effective safeguards against fire risks without penalising novel features of modern passenger ships. Moreover, this framework will be compatible with the damage stability framework, thus ensuring a holistic approach to safety from the same perspective. This will be achieved by following the risk-based design methodology as it is presented in Section 2.

The first step in this direction, and the objective of Task 1.2, is the development of the ignition model, which describes the distribution of the ignition probability in various spaces of a passenger ship by taking into account the usage of the space and its size (expressed as floor area), under the assumption that root causes, i.e. ignition sources, are common in all onboard spaces. Input to this task originates from the work conducted in Task 1.1, (Ventikos et all 2010), and close collaboration with the Task 1.2 participants.

The model should also take into account characteristic fire parameters that affect the escalation process and the consequences, assuming that an ignition event proceeds to a developed fire. Thus, a successful model should consider the critical fire parameters that affect the potential growth and spread of a fire. As a matter of fact, the model should account for the characteristics of the incipient phase, such as its duration and the type of ignition. Furthermore, the energy that can be released due to the combustible mass inside a space will be estimated and expressed through the fire load density, while the type of fuels determines also the intensity of fire products that perils human life such as smoke, carbon monoxide and dioxide. These parameters can be represented by yields of the respective products and as fire load can be viewed as the inherent hazard of each space. Sections 4, 5 and 6 are treating these issues. In addition, the specification of these parameters is proved to be a key input for the generation of heat release rate (HRR) curves, which describes the fire development inside a space. HRR curves constitute a


major parameter for the scenario generation model that will follow (task 1.4), therefore some discussion is provided in Section 4.

In short, the output from this deliverable is expected to be the basis of a model that could assist in future amendments of SOLAS concerning fire safety.

2 Risk-based design

2.1 Introduction to risk-based design

The definition of design given in Wikipedia (http://en.wikipedia.org/wiki/Design) is:

“Design is the process of originating and developing a plan for a product”. In particular ship design is very much concerned with balancing a large number of objectives regarding feasibility, performance, cost and logistics, preferences and aesthetics, etc. The compelling need to tackle effectively a high-risk business like shipping necessitates ground-breaking techniques and approaches that can address the real risk issues at a very early design stage. Such an approach can be most effective if applied very early in the design process, i.e. where maximum changes can be performed at minimum cost. By making safety a major performance parameter the designer, and potentially the ship operator, has a unique opportunity to deal cost-effectively and efficiently with the root causes of major hazards.

Such a change of approach in the design development has one additional advantage: there is no need to mimic past designs to their full extent just because they have proven successful. The services the new ships have to offer only remotely match those 15 or 20 years ago. For example, modern cruise liners that can accommodate more than 5000 passengers (and about 3000 crew members) have been delivered for service recently. The concentration of this number of people on a single floating structure requires alternative techniques, imagination and creativity, and it cannot be regulated by past experience.

Figure 2 – High-level Risk-Based Design, (Vassalos et al., 2006)


Research at the Ship Stability Research Centre (SSRC) has focused on the development of the Design for Safety philosophy and the establishment of Risk-based Ship Design

methodology (Figure 2) as it is presented and advocated in (Vassalos, 2000), (Vassalos et al., 2000a), (Vassalos et al., 2000b), (Konovessis, 2001), (Oestvik, 2001), (Vassalos et al., 2003), (Vassalos et al., 2005) and (Vassalos et al., 2006).

Figure 3 – Typical sequence of events, (Konovessis and Vassalos, 2003)

Risk-based Ship Design integrates systematically risk assessment in the design process with prevention and/or reduction of risk to life, property and the environment. Safety is expressed as another explicit design objective, alongside “conventional” design objectives (such as speed, capacity, etc). In this context, it is important to make the following considerations, (Vassalos et al. 2006):

• The notion of risk is usually associated with events that may result in catastrophic outcomes. Consequently, there is a clear distinction from reliability approaches, which are heavily related to maintenance policies in finite time intervals during the life-cycle of the ship. Unfortunate events (occurring in sequence or in parallel Figure 3) in ship operations have led to well-documented accidents of collision, grounding, fire, sinking, foundering, etc., in many cases with dire results.

• Addressing safety explicitly indicates the need to measure it: in this respect, risk is considered as the currency of safety, which is necessary to evaluate in the design phase (especially in the early stages), where most of the fundamental characteristics of the ship are generated and easily altered.

• In risk-based design, the target is to increase the influence of good engineering practice and judgement, state-of-the-art tools and knowledge, all of which are the ingredients for innovation.

Within this context, the essential contribution of risk-based design methodology in the conventional ship design practice is the explicit, rational and cost-effective treatment of safety. To achieve this, the following have to be considered:

(i) A consistent measure of safety can only be a product of proper risk analysis. That is, considering the complexity of what constitutes safety, clear focus on key safety drivers is necessary (i.e. major accident categories like fire) and the manner they are manifested (e.g. fire scenarios and their permutations).

(ii) Risk analysis must be integrated into the design process to allow for trade-offs between safety and other design objectives by utilising overlaps among performance, life-cycle cost considerations and functionality. Consequently,

System Hazard

Loss of Structural Integrity

Collision / Grounding

Fire / Explosion

Sinking / CapsizeFlooding

Evacuation


the available amount of information for design decision making and design optimisation can be significantly enriched.

(iii) The level of necessary computations for addressing all major safety concerns and the effect of safety-related design changes on functionality and other performance can be quite substantial and requires highly sophisticated software tools, (Vassalos et al., 2004). An alternative approach is to use analytical models that allow trade-offs and overlaps among design objectives and access to fast and accurate first-principles tools.

This short description of risk-based ship design prepares the ground for the presentation of the thinking behind the analytical models for risk assessment during early stages of design.

2.2 SOLAS 2009 Vs Risk-based design approach to damage stability

In the probabilistic damage stability framework (SOLAS Ch. II-1), the performance of a ship when it loses its watertight integrity is assessed as the product of probability of flooding a compartment, or group of adjacent compartments, (p-factor) and the probability of surviving a flooding of such extent (s-factor):

A� �pi�sii

(1)

A is the Attained Subdivision Index, obtained by summation of the products of p- and s-factor for every damage case i (formulations for these calculations are also provided in

the rule). A design will comply with the regulation when A ≥ R, where R is the Required Subdivision Index, which reflects an acceptable level of safety in the industry.

Figure 4 – Probabilistic damage stability calculation for a ROPAX ship with high index A, (Vassalos, 2004)


This approach is based on three premises: (i) a collision accident has occurred, (ii) the side shell of the struck ship (i.e. the ship under consideration) is breached, and (iii) the affected compartments are instantly flooded. However, a large number of collision incidents do not lead to flooding and loss of stability of the ship, i.e. although a collision may occur this does not mean that the watertightness is lost. As a result, the designers are forced to treat the loss of watertightness in a vulnerability analysis context. That is, in order to comply with the rule, they define a large number of compartments, which burden the construction and the operation of the ship. This approach impedes the creativity associated with the design itself when it is mostly needed, i.e. when innovative arrangements are required (e.g. large entertainment spaces).

On top of that, even for ships with fairly high subdivision index (Figure 4), the number of damages that the ship cannot survive is a significant proportion of the total damages examined. This result demonstrates that in addition to the above assumptions in the current regulatory framework, there even more fundamental weaknesses in its formulation and philosophy.

These weaknesses of the regulation are addressed in a risk-based design context by considering the simplest mathematical expression for risk, i.e. the product of probability

and consequence: R = P × C. In the particular case of collision risk the probability part is expressed in terms of conditional probabilities as follows, (Vassalos, 2004):

Rflooding= Pc×Pw/c×Pcap/w/c×C (2)

Where:

• Pc: probability of collision,

• Pw/c: probability of water ingress due to collision and

• Pcap/w/c: probability of capsizing due to water ingress, due to collision.

“C” stands for the consequences further to the initiation of a collision event and, in general, it reflects the loss of human life and property, and the damage to the environment.

2.3 Fire risk analysis

Fire safety is currently addressed through (i) statutory means for established design solutions, and (ii) performance-based approaches (in a risk assessment context) for alternative designs and arrangements not catered for in the prescriptive regulations. The latter, takes place at qualitative level initially with quantitative analysis (fire engineering) mainly focusing on fire consequences for a limited number of representative scenarios. Hence, the vulnerability character of the regulation emerges once again.

As a result, the fire risk contribution to the total risk of a ship design is not being quantified and hence consistent risk summation is not possible for design, operation and regulatory purposes. Moreover, in light of today’s trend for bigger, more complex and safer ships, it becomes obvious that in addition to the current regulatory regime, a more systematic and rational design framework is needed to assist the design team to undertake pro-active fire risk screening as part of the early design iterations, in the same


way as damage stability is addressed, so that better-informed decisions can be made when design decision-making is still cost-effective, (Vassalos, 2009).

Along these lines, the corresponding risk-based design approach to fire risk analysis is:

Rfire= Pignition×Pgrowth×Pescalation×C (3)

Where:

• Pignition: probability of ignition

• Pgrowth: probability of fire growth due to ignition

• Pescalation: probability of fire escalation

• C: ensuing societal losses.

The focus of this report is to address the first element of the entailed fire risk, i.e. the derivation of a probabilistic model of ignition in various spaces onboard a passenger ship.


3 Ignition probability

3.1 Space categories

The analysis that will follow in this chapter is based on the SOLAS-defined space categories. That is, the 14 spaces defined in SOLAS Ch. II-2, Regulation 9, as listed in Table 1. Table 2 is a mapping between the SOLAS space categories and data extracted from the database (Task 1.1), signifying the usage of each space.

Table 1 – 14 spaces defined in SOLAS Ch. II-2, Regulation 9, (IMO, 2004)

Code Space defined 1 Control station 2 Stairway 3 Corridors 4 Evacuation stations and external escape routes 5 Open deck spaces 6 Accommodation spaces for minor fire risk 7 Accommodation spaces for moderate fire risk 8 Accommodation spaces for greater fire risk 9 Sanitary and similar spaces 10 Tanks, voids and auxiliary machinery spaces having little or no fire risk 11 Auxiliary machinery spaces, cargo spaces, cargo and other oil tanks and other similar

spaces of moderate fire risk 12 Machinery spaces and main galleys 13 Store-rooms, workshops, pantries, etc 14 Other spaces in which flammable liquids are stowed

Table 2 – Usage of spaces on board passenger ships and corresponding SOLAS categories

Cabin (crew / officer) 6 Gift shop 7 Sauna / spa / jacuzzi 9

Cabin (passenger) 6 Guest Disco 8 Solarium 9

Cabin Balcony 5 Guest gym 8 Stage / backstage 8

Café 8 Ice rink 7 Stairs (interior) 2

Casino 8 Incinerator room 12 Swimming pool (area) 9

Centrum 8 Laundry room 13 Tender 4

Children / teen areas 7 Library 7 Theatre 8

Corridor 3 Lounge / bar (public) 8 Cabin Bath 6

Crew areas (other) 8 Luggage area 13 Conference Centre 8

Crew bar 7 Mess (crew / officer) 8 Gangway 4

Crew gym 6 Muster Station / life boats

4 Golf Course 5

Deck area (exterior) 5 Other Medical Facility 14

Dining room 8 Pantry 13 Office Areas 6

Electrical room 10 Provision area 13 Specialty Restaurant 8

Elevator 2 Public Area (others) 8 Sports Deck 5

Engine / machinery space 12 Restroom (public) 9

Galley 12 Promenade Deck 5

Generator room 12 Salon 8


3.2 Mathematical model for ignition frequency

According to BSI, (2003), the general model for the frequency of ignition for probabilistic risk analysis and assessment is given by:

Fi=αAbc (4)

Where

• α is the number of fires in a period divided by the number of buildings at risk over the same period,

• Ab is the total floor area of the building, and

• c is a coefficient smaller than 1.0 reflecting the fact that in buildings with larger floor areas there are less chances of interaction between ignition sources and combustible material in a space.

This formulation is based on statistical analyses of fire incident data in buildings. A similar model to Eq. (4), also proposed by Guarin at al., (2007), is adopted in the current context as follows:

fi=γiAi (5)

Where

• γi is the historical frequency of ignition per ship-year (s-y) and m2 (see Section 3.3 for

explanation on this choice) of the SOLAS category i that the space under consideration is classified under,

• Ai is the floor area of the space under consideration, and

• c = 1.0 and therefore ignored in the current formulation. This is attributed to the fact that irrespective of the size of ship under consideration the variation of areas is substantial as presented in Figure 5 and At this stage it should be noted that in order to proceed with the necessary analyses of this document, three sample passenger ships have been used from past research projects. The only data extracted from their general arrangement designs is the floor areas for each space onboard and its classification according the 14 SOLAS categories. In the rest of this document, reference to each ship will be performed in the form of Ship 1, Ship 2 and Ship 3, when it is deemed necessary for the analysis.

• Table 3.


Figure 5 – The average area per SOLAS space category has large standard deviation and it does not reflect the size of the ship under consideration

At this stage it should be noted that in order to proceed with the necessary analyses of this document, three sample passenger ships have been used from past research projects. The only data extracted from their general arrangement designs is the floor areas for each space onboard and its classification according the 14 SOLAS categories. In the rest of this document, reference to each ship will be performed in the form of Ship 1, Ship 2 and Ship 3, when it is deemed necessary for the analysis.

Table 3 – Floor area ranges (contribution from project participants)

Fire Location SOLAS Space

Floor Area Minimum [m2]

Floor Area Maximum [m2]

Floor Area Average [m2]

Cabin (crew / officer) 6 8.0 87.3 47.7

Cabin (passenger) 6 13.7 88.0 50.9

Cabin Balcony 5 3.2 28.0 15.6

Cafe 8 520.0 1595.0 1057.5

Casino 8 675.0 760.0 717.5

Centrum 8 505.0 1020.0 762.5

Children / teen areas 7 44.0 546.0 295.0

Corridor 3 10 60 35

Crew areas (other) 8 165.0 590.0 377.5

Crew bar 7 60.0 229.0 144.5

Crew gym 6 22.0 205.0 113.5

Deck area (exterior) 5 10000 10000 10000

Dining room 8 840.0 2034.0 1437.0

Electrical room 10 17.0 278.0 147.5

Elevator 2 2.0 4.0 3.0

Engine / machinery space 12 40.0 4228.0 2134.0

Galley 12 47.0 1972.0 1009.5

Generator room 12 156.0 230.0 193.0

Gift shop 7 60.0 190.0 125.0

Guest Disco 8 118.0 1000.0 559.0

Guest gym 8 118.0 404.0 261.0

Ice rink 7 1000 1400 1200

Incinerator room 12 120.0 255.0 187.5

Laundry room 13 8.0 453.0 230.5

Library 7 45.0 146.0 95.5

Lounge / bar (public) 8 90.0 1430.0 760.0

Luggage area 13 100 500 300

Mess (crew / officer) 8 50.0 420.0 235.0

Muster Station / life boats 4 500 1500 1000

Pantry 13 8.0 12.0 10.0


Provision area 13 17.0 56.0 36.5

Public Area (others) 8 42.0 944.0 493.0

Restroom (public) 9 20 60 40

Promenade Deck 5 310.0 1382.0 846.0

Salon 8 97.0 135.0 116.0

Sauna / spa / jacuzzi 9 130.0 850.0 490.0

Solarium 9 90.0 130.0 110.0

Stage / backstage 8 225.0 305.0 265.0

Stairs (interior) 2 16.0 16.0 16.0

Swimming pool (area) 9 187.0 2562.0 1374.5

Tender 4 10 30 20

Theater 8 420.0 1120.0 770.0

Cabin Bathroom 6 1.9 12.8 7.3

Conference Center 8 525.0 540.0 532.5

Gangway 4 10 30 20

Golf Course 5 70.0 260.0 165.0

Medical Facility 14 114.0 346.0 230.0

Office Areas 6 12.0 60.0 36.0

Specialty Restaurant 8 352.0 420.0 386.0

Sports Deck 5 523.0 706.0 614.5

The model presented in Eq. (5) requires some further justification with respect to the variables included concerning:

(i) The representation of the combustible materials in a space, and (ii) The independency of the historical frequency and the floor area of the space.

The first point has been considered in past fire incident data and studies, e.g. (Tillander, 2004), and it is indicated that there is a degree of correlation between the floor area of a space and its combustible contents (various pieces of furniture, floor material, wall and ceiling coverings, fittings, etc.). In this respect, the floor area of a space can be used in the formulation as a representative measure of the fuel contained in this space.

On the other hand, this is not the case for the ignition frequency per unit area and the floor area of a space. The graph presented in Figure 6 demonstrates that the floor areas per SOLAS space category for three passenger ships under consideration, has very poor correlation (as it is demonstrated by the scatter of the data points and the values of the correlation coefficient R for an exemplified linear model fit in each case) with the frequencies extracted from the database.

Figure 6 – Statistical analysis on the correlation between ignition frequency and total floor area


It is therefore reasonable to assume that the two variables included in Eq. (5) are independent. This result is also confirmed with fire accidents statistics in buildings, (Tillander 2007, Hasofer et al. 2007).

Finally, it should be stressed that in the context of FIREPROOF project, where the probabilistic framework is the ultimate objective, the main target is the formulation of the historical frequency of ignition per SOLAS space category, i.e. γ, and it will be pursued in the next section. The actual floor area for each space (Ai) is a design input as it will be demonstrated in the case study of Section 0.

3.3 Historical frequency of ignition

The derivation of historical frequency of ignition is based on the incident database of Task 1.1. In light of this, a comparison is performed for the top 10 spaces, where ignition occurs more frequently, between the existing database and the data provided by Guarin at al., (2007). The results are presented in Figure 7 and Figure 8 respectively. With the exception of one case (i.e. “Corridor” and “Public space”) similar conclusions can be drawn for the spaces with the highest relative frequency of occurrence of fire incidents. This is particularly true for spaces, such as galley, incinerator room, cabin, machinery space, laundry room, etc., despite the small variation which can be justified by inherent data attributes and size of the sample under consideration.

Figure 7 – Top 10 spaces with the highest frequency of fire occurrence in the FIREPROOF database

Figure 8 – Relative frequency of occurrence, reproduced from (Guarin et al., 2007)

The fire incident data (1521 records) in the database correspond to the 463.13 s-y. The weighted average fire ignition frequency is 3.28/s-y (considering the total number of ships per


operator for which fire incidents are collected). The expected frequency per SOLAS space category is presented in Table 4. For categories 1 and 11 there are no records available.

Table 4 – Fire ignition frequency /s-y

SOLAS space category Number of occurrences Frequency of ignition / s-y

1 0 0.000

2 23 0.050

3 52 0.112

4 11 0.024

5 72 0.155

6 315 0.68

7 19 0.041

8 192 0.415

9 55 0.119

10 10 0.022

11 0 0.000

12 642 1.386

13 126 0.272

14 4 0.009

The last ingredient of the historical frequency is the typical area per SOLAS space. This parameter reflects the exposure of the space to ignition and it will be included in the model as follows:

γi=

frequency of ignition

s-y × �ni×Atypicali� (6)

Where ni is the number of spaces of the SOLAS category i.

The typical floor area per space will be introduced in the formulation of the ignition model according to the statistical properties of the sample areas collected for Ship 1 – Ship 3. Although it would be trivial to assign a Normal distribution to this data, in practice it proved that this choice would result in truncated distributions as it is demonstrated in Figure 9. This practically means that when sampling will be performed for the generation of scenarios in Tasks 1.4 and 1.5, negative areas will be obtained.


Figure 9 – The Normal distribution fit is truncated for most of the spaces of Ship 2

Contrary to this, the Rayleigh distribution (Appendix I) is proved a much better fit for most of the spaces as it is presented in Figure 10 for all space categories where data is available.


Figure 10 – Rayleigh distribution fit for floor area per SOLAS space category (Ship 2)


A property of the Rayleigh distribution is that its 50-th percentile does not correspond to the average value of the sample due to its asymmetric form. This value will be used as the typical area per SOLAS space category.

The calculation process is presented in Table 5. Column (1) represents the SOLAS spaces whereas columns (2) – (4) and (5) – (7) represent the 50-th percentile of the floor area per space and the number of spaces recorded for each of the three ships under consideration. The ignition frequency / s-y and space category in column (8) are reproduced from Table 4. Columns (9) – (11) are obtained according to Eq. (6). Finally, column (12) is the maximum value of historical frequency obtained per ship.

Table 5 – Calculation of the historical frequency based on the floor area data selected from Ship 1, 2 and 3

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

50-th percentile of floor area Number of spaces

freq./s-y γ1 γ2 γ3 max γ Ship 1 Ship 2 Ship 3 Ship 1 Ship 2 Ship 3

1 47.2 43.2 91.1 5 9 3 0.000 0.00E+00 0.00E+00 0.00E+00 0.00E+00

2 15.5 8.7 7.9 306 564 140 0.05 1.06E-05 1.02E-05 4.55E-05 4.55E-05

3 64.6 42.4 22.4 189 200 129 0.112 9.17E-06 1.32E-05 3.88E-05 3.88E-05

4 0.0 0.0 0.0 1 1 1 0.024 0.00E+00 0.00E+00 0.00E+00 0.00E+00

5 23.0 87.9 303.2 99 795 14 0.155 6.80E-05 2.22E-06 3.65E-05 6.80E-05

6 7.4 5.6 5.5 3315 4512 1564 0.68 2.77E-05 2.70E-05 7.91E-05 7.91E-05

7 215.1 89.5 133.2 9 15 5 0.041 2.12E-05 3.05E-05 6.16E-05 6.16E-05

8 194.2 201.7 103.0 58 83 40 0.415 3.68E-05 2.48E-05 1.01E-04 1.01E-04

9 36.1 6.6 11.3 56 56 51 0.119 5.89E-05 3.22E-04 2.06E-04 3.22E-04

10 57.5 44.3 51.2 128 133 33 0.022 2.99E-06 3.74E-06 1.30E-05 1.30E-05

11 35.3 42.3 288.8 14 28 25 0.000 0.00E+00 0.00E+00 0.00E+00 0.00E+00

12 260.0 144.0 144.2 21 21 12 1.386 2.54E-04 4.58E-04 8.01E-04 8.01E-04

13 24.9 20.2 13.2 189 252 102 0.272 5.78E-05 5.34E-05 2.02E-04 2.02E-04

14 18.2 23.0 8.6 19 12 4 0.009 2.60E-05 3.26E-05 2.62E-04 2.62E-04

3.4 Benchmarking

The historical frequencies derived above are benchmarked against building data for spaces with similar usage under the assumption that similar ignition sources are present. The data originate from (BSI, 2003), (Tillander, 2004) and the minimum and average historical frequencies, which are derived in a similar manner to the maximum ones as presented in Table 5. All the information is summarised in Table 6 and depicted in Figure 11. It should be stressed that the data derived in Table 5 are expressed as number of occurrences per s-y and m2, whereas the data in (BSI, 2003) and (Tillander, 2004) are expressed as number of occurrences per year and m2. For this comparison, the maximum, minimum and average historical frequencies are divided by 3.7, i.e. 463.13 s-y / 125 exposed ships, the fire incidents of which are included in the FIREPROOF database.


Table 6 – Data summary for the benchmarking of historical frequencies

SOLAS category

(BSI, 2003) max γ min γ average γ (Tillander,

2004)

Offices 6 1.20E-05 2.13E-05 7.29E-06 1.20E-05 3.90E-06

Storage 13 3.30E-05 5.46E-05 1.44E-05 2.82E-05 9.00E-06

Public assembly

8 9.70E-05 2.72E-05 6.69E-06 1.46E-05 7.65E-06

Figure 11 – Comparison of historical frequency of ignition

Although the data of Table 6 is not sufficient to draw any solid conclusions, the following points can be made:

• For the space category “Offices” and “Storage”, the BSI values fall between the minimum and maximum value of the derived frequency for the respective space. This is not the case for the “Public assembly” space, where clearly the assumption of similar usage is not valid. That is, the public area referred to in the building statistics and the public areas included in the FIREPROOF database have different usage of the respective space.

• The frequency data originating from Tillander’s work is consistently closer to the

minimum values of γ, verifying the general perception that the maritime regulations for fire are stricter than those of the built environment industry.

• The data mentioned in BSI and Tillander have little agreement between them, a fact that demonstrates the importance and the quality of sample data for the purposes described in this section and the report in general.

Considering the above, the historical frequency of ignition for the 14 SOLAS spaces will be taken as the one presented in column 12 of Table 5. It should be stressed that in light of the identified variation and uncertainty in the available data, the formulation reported here should be revisited once more data is available in the database, as it is envisaged to


take place in the duration of FIREPROOF. In this case, effort should be spent in identifying tendencies for convergence which will concretise further the current results.

Case Study

The frequency of ignition calculation is demonstrated for the two MVZ of the ship presented in Appendix II. The floor areas of different SOLAS spaces are summarised in Table 7 and Table 8 for MVZ 5 and 6 respectively.

Table 7 – Floor areas for Zone 5

Zone 5 SOLAS Space

Floor areas 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Tank top 0 0 0 0 0 0 0 0 0 0 0 1279.44 0 0

Tween deck 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Deck 0 0 29.76 73.2 0 0 317.424 0 0 0 272.175 0 451.2 0 0

Deck 1 0 152.407 101.866 0 0 290.932 0 496.259 32.6 0 0 0 7.216 191.391

Deck 2 0 131.991 199.327 0 0 816.115 0 0 0 0 0 0 0 0

Deck 3 0 131.991 39.15 0 0 161.876 0 890.507 57.1 14.5 0 0 9.3 0

Deck 4 0 123.622 0 0 386 0 0 705 54.7 0 0 0 53.7 0

Deck 5 0 123.622 0 0 446.6 0 0 750 54.7 0 0 0 8.7 0

Deck 6 0 152.062 105.585 0 133.24 690.16 0 56 0 21.2 0 0 0 0

Deck 7 0 152.062 105.585 0 133.24 584.76 105.4 56 0 21.2 0 0 0 0

Deck 8 0 152.062 105.585 0 132.28 584.76 105.4 56 0 21.2 0 0 0 0

Deck 9 0 152.062 105.585 0 132 659.211 0 56 0 91 0 0 0 0

Sum 0 1301.641 835.883 0 1363.36 4105.24 210.8 3065.766 199.1 441.275 0 1730.64 78.916 191.391

Table 8 – Floor areas for Zone 6

Zone 6 SOLAS Space

Raw information 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Tank top 0 0 0 0 0 0 0 0 0 0 0 1259.2 0 0

Tween deck 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Deck 0 0 0 0 0 0 0 0 0 0 284.57 0 1010.57 0 0

Deck 1 74.649 85.996 149.171 0 0 207.441 0 47.005 17.19 69.285 0 365.51 245.72 0

Deck 2 26.738 56.387 226.044 0 0 776.013 0 0 0 111.476 0 0 0 0

Deck 3 0 29.76 0 0 0 0 0 1269.726 0 0 0 0 0 0

Deck 4 0 86.32 0 0 200 0 0 907.24 0 0 0 0 0 0

Deck 5 0 86.32 0 0 112.4 0 0 907.24 0 0 0 0 0 0

Deck 6 0 29.76 190.398 0 126 892.924 0 0 0 77.3 0 0 0 0

Deck 7 0 29.76 179.257 0 126 906.073 0 0 0 43.8 0 0 0 0

Deck 8 0 29.76 179.257 0 126 906.073 0 0 0 43.8 0 0 0 0

Deck 9 0 29.76 130.174 0 122.4 750.223 0 0 0 282.5 0 0 0 0

Sum 101.39 463.82 1054.30 0 812.8 4438.75 0 3131.21 17.19 912.73 0 2635.28 245.72 0

The overall ignition frequency calculation is presented in Table 9.

Table 9 – Ignition frequency for the two zones under consideration

SOLAS space category

Zone 5 Zone 6

Areas, m2 γ Frequency

/s-y Areas, m2 γ

Frequency /s-y

1 0.00 0.000E+00 0.000E+00 101.39 0.000E+00 0.000E+00

2 1301.64 4.547E-05 5.919E-02 463.82 4.547E-05 2.109E-02

3 835.88 3.880E-05 3.243E-02 1054.30 3.880E-05 4.090E-02

4 0.00 0.000E+00 0.000E+00 0.00 0.000E+00 0.000E+00

5 1363.36 6.804E-05 9.277E-02 812.80 6.804E-05 5.530E-02

6 4105.24 7.905E-05 3.245E-01 4438.75 7.905E-05 3.509E-01

7 210.80 6.156E-05 1.298E-02 0.00 6.156E-05 0.000E+00

8 3065.77 1.007E-04 3.088E-01 3131.21 1.007E-04 3.154E-01

9 199.10 3.216E-04 6.404E-02 17.19 3.216E-04 5.529E-03

10 441.28 1.302E-05 5.746E-03 912.73 1.302E-05 1.189E-02

11 0.00 0.000E+00 0.000E+00 0.00 0.000E+00 0.000E+00

12 1730.64 8.012E-04 1.387E+00 2635.28 8.012E-04 2.111E+00

13 78.92 2.022E-04 1.596E-02 245.72 2.022E-04 4.968E-02

14 191.39 2.622E-04 5.018E-02 0.00 2.622E-04 0.000E+00

2.35 / s-y 2.96 / s-y


4 Ignition characteristics This part of the report will consider characteristics of the ignition phase. In fact, the specification of these parameters will serve as partial input to the specification of HRR curves, which describes the fire development within the enclosure. HRR curves represent a characteristic element of a fire scenario (see for example IMO 2001, ISO 1999, SPFE 2002).

Conceptually, a fire scenario accounts of course for many more parameters; for example, for the effect of suppression/detections means that can be accounted in a structured manner through event trees. In the current task the modelling effort will be focused on those variables that determine the type of the fire; its intensity; its potential duration and growth characteristics). An innovative aspect of the current approach is that, these HRR curves will be calculated probabilistically. Due to the vast range and uncertainty of the input parameters, these constitute essentially random variables and a probabilistic treatment seems to be most appropriate. Then, a set of HRR curves will be generated for each space category, which will be utilized in the Scenario Generation Task. Ultimately, some of these curves will be found linked to strong fire growth potential and thus will be labelled as critical. The HRR is also often required as input to numerical codes (CFD or zone models) for prediction of the gas temperature and smoke propagation.

4.1 General framework of fire development in an enclosure

At this stage it is useful to present briefly the stages of fire development within an enclosure, from the viewpoint of energy release. A more detailed analysis is part of the Task 1.4: Randomized Scenario Generation Specification, where the generation of HRR curves will drive the selected fire scenarios for further investigation. However, at this point the aim is to show how the values of particular parameters, such as the ignition characteristics and the fire load, are linked with the shape and definition of the HRR curve. In general, the chronological sequence of the main stages that characterise fire development in an enclosure are the ignition, the growth, the stage of full development and the decay (Karlsson and Quintiere 2000). The main assumption is that, the fire initiates from a fuel package and then its growth can result in full enclosure involvement (Karlsson and Quintiere 2000, Quintiere 2000). Nevertheless, a more complex arrangement could also be assumed, for the case where successive ignition of fuel packages has occurred, which is the case of the fire development in a large enclosure. Such a scenario will be discussed in the forthcoming task related to the fire scenarios generation.


Figure 12: Development of fire in an enclosure in terms of HRR (extracted from ISO 1999).

A short description of each stage of fire development follows:

Ignition: Before fire growth is established, an incipient phase normally occurs, whose duration and type depends on the ignition source. For example, a piloted ignition is usually followed by flaming combustion and quicker flame spread resulting in fire growth. On the other hand, spontaneous ignition, which results in accumulation of heat in the surface or a low energy ignition source (cigarette), will lead to smouldering combustion and not to so quick fire growth. However, toxic gases and smoke may be produced at this phase, while due to the smoke generation the incident may be detected before fire growth. Such time lag before the actual fire growth should be considered in the model. Fitzgerald (2004) noticed that a heat release rate of 20 kW, corresponding to flame of 25 cm height, can be assumed as a reference for the established burning phase.

Fire growth: After ignition and supposing that sufficient oxygen exists, the fire grows approximately following a power law with respect to time (Karlsson and Quintiere 2000). The growth rate depends mainly on the type and amount of the available fuel and type of combustion (usually flaming combustion is faster). This phase is usually governed by the amount of fuel and, in the presence of sufficient oxygen, it is called “fuel surface controlled”.

Flashover: This phenomenon occurs when there is a transition to a total surface involvement in a fire of combustible material within a compartment (ISO 1999). This transition leads to the fully developed stage, while the most common criterion demands the temperature to reach to 500 – 6000 C, or the radiation to the floor of the compartment to be 15 – 20 KW/m2 (Karlsson and Quintiere, 2000). Flashover is a key event from the perspective of fire safety. Therefore it is common to distinguish between pre-flashover fires which are related with human safety (within the space origin of flashover occurrence) and post-flashover fires which can affect structural safety. The occurrence of flashover depends on the fire load, the geometry of the enclosure, the ventilation openings and the thermal properties of the boundaries.

Fully developed fires and decay: At this stage the released heat reaches an almost steady value, which is usually limited by the amount of oxygen in the compartment. In this case, the fire is characterised as “ventilation controlled”. The rate of air entering the space through openings controls the mass loss rate. If the space is well-ventilated, the maximum heat release rate will


be determined by the amount of fuel. As soon as the full amount of the available fire load has been consumed the decay stage has started. This can be, in turn, also fuel surface controlled.

4.2 Ignition or pre - growth phase

4.2.1 General scope of the analysis

The analysis of the ignition or else pre-growth phase (Figure 12) targets the estimation of the time duration of the incipient phase. This time window can be defined as the time where the ignition source is coming in contact with the fuel source until the time where established burning and actual growth begins. In established burning sustained flaming occurs. As a matter of fact, considerable amount of energy is released and spreading of flames can follow. During the incipient phase the energy released is very low; however toxic gases could be generated, thus its calculation should be considered. Furthermore it is both unrealistic, as results for the estimation of this time duration from relevant experiments shows (see the column mentioned as virtual time in Table 21 in Appendix III), and rather adverse to assume that sustained burning and thus rapid flame spread occurs simultaneously with the inception of ignition.

Apparently the strength and type of the ignition source as well as the respective characteristics of the fuel available determine the type and duration of the incipient phase. Next follows a presentation of the ignition and fire types that will be taken into account.

4.2.2 Types of ignition and ignition sources

The ignition process is regarded as the initiation of a flaming fire. In more specific words, ignition is as a process that produces an exothermic reaction characterized by an increase in temperature greatly above the ambient (Karlsson and Quintiere 2000). During this process, vapours generated by heating the surface of a material mixed with air form a combustible mixture, which ignites and fire occurs (Drysdale 1999, Quintiere 2006).

Concentrating on solids, in general two types of ignition are identified: the piloted ignition, in which flaming is initiated in a flammable vapour mixture through a pilot (thus an independent flame); and spontaneous ignition through accumulation of heat in the fuel. The accompanying combustion process (for both types) can be either flaming or smouldering combustion, which is a much slower process.

Piloted ignition can be a flaming match, a spark or other pilot source such as a heated surface. Thus, the energy content can vary. It is obvious that the larger the energy of the pilot source the faster the oncoming fire growth, while the source of energy can be chemical, electrical or mechanical. Piloted ignition occurs if the concentration of pyrolysis gases is above the lower flammable limit. A pilot flame will usually result in flaming combustion directly. However smouldering combustion can occur by the presence of a glowing cigarette or a spark. In this case the combustion can continue for a long time before flaming occurs. Even when the produced heat is low, a substantial amount of toxic gases may be released.


For a solid to be ignited, it should be heated sufficiently in order to release flammable vapours. Then, flammable vapours can be given off either by pyrolysis or by melting and subsequent vaporization. Pyrolysis occurs when a material is heated and decomposes, releasing vapours known as pyrolyzates (Drysdale, 1999).

On the other hand, spontaneous ignition concerns the autoignition arising from exothermiscity of the material itself (Quintiere, 2006), which in other words can be described by the self-heating of the fuel in the absence of an ignition source.

In this analysis, piloted ignition will be targeted which can result either in flaming or in smouldering combustion. For the first case a model will be developed taking into account the strength of the ignition source and the type of the fuel in order to calculate the time of the incipient phase.

For smouldering combustion, due to the difficulty of generating a similar and yet practical for engineering purposes model for predicting the transition from smouldering to flaming combustion, experimental results will be utilised as will be presented later. Cases with smouldering combustion are essential as these involve ignition of upholstered furniture or bedding components due to cigarette, which are common causes of ignition in a passenger cabin.

4.3 Probabilistic model for piloted ignition: flaming combustion

This part of the analysis targets the piloted ignition of solids. It is assumed that the heat flux of the pilot, or else the ignition source, is continuous. Furthermore, for established burning to occur, the flame at the surface of the material should be sustainable and established. For this, a fire power of 20-30 kW should be attained (Fitzgerald 2000). These values could be assumed as satisfactory for practical use. Therefore, the objective will be to estimate the duration of the incipient phase and then assume that, at the end of this time, the HRR will have obtained a value around 20-30 kW.

In general, the methods for predicting ignition of solid materials exposed to thermal radiation depends on whether the material is assumed thick or thin. In a thermally thick material that is heated on one side, the temperature rise will not be substantial on the unexposed side. We can assume that the majority of objects that can be present in an enclosure are thermally thick materials.

A practical method for predicting the ignition of thermally thick materials is that of Tewarson

(2002). In this method, the time of the incipient phase igt depends on the next three variables

and is given by Eq. (7):

• The thermal response parameter TRP

• The critical heat flux: CHF

• The external heat flux eq′′&

2

ig

e

TRPt

q CHF

=

′′ − & (7)


With the thermal response parameter TRP one characterises the resistance to ignition and fire propagation, as it accounts for the rate with which heat is transferred to the interior of the material while its surface is exposed to a continuous heat flux. Therefore, it depends on the

ignition and ambient temperature ( )ig aT ,Τ respectively, the material thermal conductivityk ,

the material specific heat pc and the material densityp . According to Tewarson, (2002), TRP

is calculated by:

ig pTRP T k c p4

π= ∆ ⋅ ⋅ ⋅ (8)

Where ig ig aT T∆Τ = − , and its units is in 1/ 2 2kW s m⋅ . The critical heat flux CHF

2kW m is related with the minimum heat flux required to heat a material for fire initiation

to occur. Both TRP and CHF are determined for various materials from ignition data derived from several fire propagation apparatus like the ASTM E2058 and the cone calorimeter. For example, Table 10 provides some reference values for these parameters. Considering wood (red oak), Figure 13 shows the time of incipient phase for a range of external heat fluxes

2

eq kW m′′ & . It should be mentioned also that the respective time prediction of that

Mikkola and Vichman (Eq. 9) presented in the Engineering Guide: Piloted Ignition of Solid Materials

Under Radiant Exposure (SPFE, 2002) is the same with that of Tewarson, where crq CHF′′ =&

2

ign

ig p

e cr

Tt k p c

4 q q

π ∆ = ⋅ ⋅

′′ ′′− & & (9)

The probabilistic fire ignition model will then be established according to the next framework:

• Each source of ignition that is present in an examined space will be associated with a

value of the corresponding external heat flux eq′′& .

• The combustible materials present in the space will be categorised according to generic groups, as will for the analysis of fire load. The parameters CHF and TRP will be treated as random variables, estimated by the percentage of the contribution of each group to the total combustible mass in the space.

• The time of the incipient phase should also be treated as a random variable.

• The heat release rate estQ& that is required for the flame to become sustainable and

allows established burning to occur will be considered as random variable within the range 20-30 kW.

Therefore, several cases for the variation of HRR at the incipient phase can be generated, according to the next equation:

esb ig

ig

tQ Q , 0 t t

t= ≤ ≤& & (10)


4.3.1 Demonstration of the model

The combustible materials’ generic grouping is assumed also for this example. From SPFE, the critical heat flux and thermal response parameter for these groups are presented in

Table 11. Values assigned to groups are the average values of the materials comprising the respective groups. The contribution of each group to the total mass follows the same hypothesis (as in the estimation of the fire load distribution presented in the next chapter). 1000 cases have been considered under the following assumptions: textile percentage is uniformly distributed in (30 – 38), wood-based in the range (39-48), plastics and miscellaneous consist the rest with the plastics to be 1.5 times more than miscellaneous.

Table 10: Typical values of CHF and TRP (extracted from Tewarson 2002)

30 40 50 60external heat flux �kW�m2�

100

200

300

400

500

600

700

time of incipinent phase �s�


Figure 13: Time of incipient phase for wood (red oak) for a range of heat fluxes

Table 11: Characteristic values of CHF and TFR for the generic group materials

group CHF

(kW/m2) THR(kWs0.5/m2)

textiles 14.00 241.67

wood based 11.00 154.67

plastics 18.00 350.00

miscalaneous 15.00 161.00

Furthermore, the external heat flux has been assumed as a random variable, uniformly distributed at the range (20-40) kW/m2. It should be mentioned that the range and the type of the distribution should be derived from the characteristics of the ignition sources that are present in the examined space. Figure 14 and Figure 15 show the results for the probabilistic calculation of the incipient phase. Assuming also a uniform distribution for the required HRR for established burning (20-30) kW, the HRR in the ignition phase for various scenarios is presented in Figure 16.

Figure 14: Probability density of duration of incipient phase.

0

100

200

300

400

500

600

700

800

900


Figure 15: Cumulative ascending duration of incipient phase

Figure 16: Incipient stage in terms of HRR time history.

4.3.2 Verification of the model

In order to check the rationality of the model developed, a comparison with experimental results for a passenger cabin will be carried out. Data from a report concerning large –scale experiments for a passenger cabin conducted at SP Technical Research Institute of Sweden (Arvidson et al 2008) will be used. As described in the report, the ignition source was

standardized wood crib No. 7 according to BS 5852: Part 2 (1999). The heat flux eq′′& for this

ignition source according to Babrauskas, (1985), is 25 kW/m2. The wood crib was positioned on a bed. It was considered in direct contact with the foam mattress and in front of the pillow.

0

100

200

300

400

500

600

700

800

900

200 400 600 800 1000time�s�

5

10

15

20

25

30

HRR �kW�


The characteristic values for CHF and TRP for the materials of the above items are given in Table 12 (derived from Tewarson 2002).

Table 12: Ignition characteristics of the mattress and bedding material

item materials CHF (kW/m2) THR(kWs0.5/m2)

mattress latex foam 16 113-172

pillow Polyester foam 10-15 111-317

Following the methodology described above and taking into account the variations presented in Table 12, the time period of the incipient phase ranges as follows:

• for the mattress from 128 to 295 s

• for the pillow the respective times are 54.5 to 1000 s.

The results from the five experiments (Table 13) show that the average times until flames appear in these items are 68.2 and 131 s for the pillow and mattress respectively. Therefore, good agreement is noticed.

Table 13: Ignition times in seconds for the pillow and mattress to ignite

Test Pillow mattress

1 95 150

2 60 110

3 66 160

4a 55 80

4b 65 155

average 68.2 131

4.4 Smouldering combustion of upholstered furniture

The most common smouldering combustion fire can be resulted by a cigarette ignition of upholstered furniture and mattresses. The major materials of upholstered furniture are cellulosic, PU (Polyurethane) foam for padding and fabric cover. The consequences from smouldering fires may be significant, even though the heat output is small, due to suffocation in the room of origin or in adjacent rooms. A smouldering fire may convert to a flaming one after some period. The transition mechanisms are difficult to explain theoretically (some


review is provided in Drysdale 1999) and empirical and experimental deductions are usually utilized. For example the transition may happen due to oxygen availability and air currents, such as the opening of a door can result. The reverse phenomenon, the switch from a flaming fire to smouldering can also occur due to oxygen depletion. Furthermore, relevant studies (Babrauskas, 1985) reveal that since the transition to flaming has occurred, the behaviour of variables like the heat release rate and smoke production is identical with a fire that would have been started at that instance, thus means that the history of the fire presents no memory. Experimental results for upholstered furniture (sofas, chairs) and mattresses targeting the estimation of the transition time have been gathered in Babrauskas, (1985). The results are described through mean values and standard deviations, assuming Gaussian distributions. Such a distribution regarding tests for sofas and chairs has been generated and presented next, while a similar distribution for mattresses can be also derived. Following the same lines for the generation of HRR time histories during the incipient phase as for flaming ignition, Figure 18 shows the respective incipient phase. At the end the transition to a flaming fire will be initiated.

Table 14: Results for smouldering combustion for sofas/chairs and mattresses (Babrauskas, 1985)

mean (s) st. dev (s)

sofas/chairs 4172 1877

mattresses 4951 1582

Figure 17: Probability density of transition times from smouldering to flaming for sofas/chairs based on data in Babrauskas, (1985).


Figure 18: HRR of incipient phase for smouldering combustion.

20 40 60 80 100 120 140time�min�

5

10

15

20

25

30

35

HRR �kW�


5 Fire load properties

5.1 Description of the methodology

The total fire load [ ]Q MJ that corresponds to a compartment is a measure of the energy that

can be released by combustion of all the combustible material in the enclosure (Karlsson and Quintiere, 2000). Thus, knowledge of the fire load of a space provides an indication about the duration of the fire. In order to have a better sense of the fire load located in an enclosure, the

fire load density 2Q MJ / m′′ is commonly used. In the majority of the respective

estimations, the floor area is considered instead of the total enclose surface area. However, for large spaces onboard characterised by large height, the enclosure surface area can be used for the respective calculations. The FLD1 for a space is calculated as follows:

i i2

f

m hQ MJ / m

A′′ =

∑ (11)

where [ ] [ ] 2

i i fm kgr ,h MJ / kgr ,A m are the mass, the calorific value of the i object and

the floor area of the space.

Various studies have been carried out for the estimation of fire load densities; see for example Thomas (1986), Kumar and Kameswara Rao (1995), Bwalya et al (2005), Zalok et al (2009). Actually, these studies provide a statistical analysis of data collected for various types of premises and intensity of occupancy. Some characteristic values obtained from these studies are presented later (in Section 4.3). A survey of the objects that are present in the space, both fixed (construction and lining materials) and movable in terms of their mass and the type of combustible material, should be carried out. It should be mentioned that for ships, relevant data is very sparse.

For the current task, the basic idea for estimating FLD is to assume that each space encloses a group of combustible materials with some contribution to the total combustible mass of the space. The total mass of combustible materials in the enclosure, which is usually called fuel load (FL), can be represented as a distribution of the mass per unit floor area. It should be noticed that, as the framework of our analysis is probabilistic, the amount and type of combustibles inside a space are considered as random variables. However, they can be specified also in a deterministic way, an option that can be regarded as a single case of the probabilistic viewpoint. FLD distributions can be derived as follows:

• By specifying the fuel load (FL) distribution (kgr/m2) for the examined space. IMO MSC/Circ. 1003 provides some reference maximum values for accommodation and service spaces (e.g. cabins: 15 – 35 kgr/m2).

1 FLD is the abbreviation of Fire Load Density


• Selecting some generic groups which characterise the type of materials in the space. For example textiles, wood – based etc. Each group will be represented by a representative calorific value.

• Estimating the contribution of each group to the total combustible mass. Due to same reasons mentioned above, a range for each group can be taken into account. In other words, a number of different designs, in terms of material content, will be considered.

From the above, FLD will be determined as follows:

2 ii

total

mQ MJ m FL h

m′′ = ∑ (12)

5.2 Demonstration of the methodology

For demonstration, the FLD distribution corresponding to a passenger cabin will be derived. Two FL distributions will be considered, which differ mainly in the assumed maximum value of the allowed fuel load. In the first one it is 35 kgr/m2 and in the second 15 kgr/m2. Both distributions, for the needs of demonstration, follow the gamma2 distribution (Figure 19). The selection of the distribution of fuel load density implies the knowledge that the most common statistical distribution of the fire load density are the lognormal, the Gumbel, gamma and Weibul distributions (e.g. Zalok et al 2009, Hakkarainen et al 2009). Thus considering eq.8, the fuel load can be selected by one of the above mentioned models. If detailed data exists for cabins for a number of ships, one can generate more precisely the appropriate form of distribution. Next, the group of combustibles with their average calorific value needs to be specified. For example, four groups will be specified regarding the type of combustibles. The first includes the textiles, the second the wood – based items, the third is plastics and the last everything else (miscellaneous).

Table 15 presents in more detail the type of materials that constitute each generic group, their heat of combustion and finally the derived average value estimated for each generic group. For plastics an average value based on the various types of plastics has been calculated. A more detailed analysis could include many more groups and objects per group.

In order to consider the uncertainty of the contribution of each group to the total combustible mass, a number of types of cabins will be considered, varying the percentage of each material group to the total mass. 1000 cases have been considered with the next assumptions: textile percentage is uniformly distributed in (30 – 38), wood-based in the range (39-48), plastics and miscellaneous consist the rest with the plastics to be 1.5 times more than miscellaneous.

2 As the range of gamma distribution is from zero to infinity, some cut-off limit will be set.


Figure 19: Two F.L distributions with maximum 35 and 15 kgr/m2 respectively

Table 15: Generic groups of materials

group ∆hc (MJ/kgr)

1 textiles 22.50

wool 23.50

clothes 19

cotton 18

silk 19

acrlylic 33.00


2 wood based 17.33

wood 18.50

paper 17.00

cellulose 16.50

3 Plastics 24.81

4 miscalaneous 31.70

foam rubber 37.00

leather 18.6

rubber 39.5

From Figure 21 it can be deduced that the dominant parameter affecting the fire load is the fuel load.

5.3 Relevant data from the literature: Comparing the results

In order to verify the methodology for the calculation of the fire load, some reference values of fire load densities from the literature will be provided as mentioned before. It should be mentioned that these values have been calculated mainly from statistical surveys in residential buildings. In Figure 23 the total enclosure area has been considered and values correspond to the 80% percentile. Furthermore it is common to present similar results in terms of calorific equivalent mass of wood, as those of Figure 25, assuming that the net calorific value for wood is 16.7 MJ/kg.

5.0% 90.0% 5.0%

116 529

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0%

12.5%

25.0%

37.5%

50.0%

62.5%

75.0%

87.5%

100.0%

Fire load Density (MJ/m2)

fire load total

Minimum 60.1842

Maximum 704.5393

Mean 284.3404

Std Dev 124.9167

Values 990 / 1000

Filtered 10


Figure 20: Fire load densities (with cumulative ascending overlaid) corresponding to the two assumed fuel load distributions.

Figure 21: Comparison of the derived cumulative ascending distributions. The dashed one is that with the lower maximum value of fuel load.

Figure 22: Fire load densities derived from Thomas, (1986)

5.0% 90.0% 5.0%

58.9 213.5

0

50

100

150

200

250

300

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.010

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Fire Load Density (MJ/m2)

fire load total

Minimum 44.0542

Maximum 291.0622

Mean 119.0223

Std Dev 48.0794

Values 990 / 1000

Filtered 10

5.0% 90.0% 5.0%

0.0% 33.0% 67.0%

59 213

0

100

200

300

400

500

600

700

800

0.0

0.2

0.4

0.6

0.8

1.0

Fire load (MJ/m2)

fire load total

Minimum 44.0542

Maximum 291.0622

Mean 119.0223

Std Dev 48.0794

Values 990 / 1000

Filtered 10

fire load total

Minimum 60.1842

Maximum 704.5393

Mean 284.3404

Std Dev 124.9167

Values 990 / 1000

Filtered 10


Figure 23: Fire load densities extracted from Pettersson et al., (1976)

Figure 24: Fire load densities extracted from Yii, (2000)

Figure 25: Fire load densities in terms of calorific equivalent mass of wood Harmathy and Mehaffey, (1983)

An example from Zalok et al, (2009), statistical survey on commercial premises is presented in the next figure. Very useful is the analysis of the contribution of generic materials to the combustible mass for the various premises (Figure 27).

Figure 26: Characteristics of fire load for various premises (extracted from Zalok et al, 2009)


Figure 27: Contribution of generic group materials to the combustible mass (extracted from Zalok et al, 2009)

Regarding fire load from ship industry, data from a report concerning large –scale experiments for passenger cabin conducted in SP Technical Research Institute of Sweden (Arvidson et al, 2008) will be utilized. The cabins constructed from realistic materials used in passenger cabins, so the comparison of the results for the fuel and fire load densities will be helpful. Figure 28 summarizes the list of materials/objects placed in the cabin, their mass and energy characteristics. The floor are of the cabin was 12.9 m2. The total combustible mass is 159.4 kg and thus the fuel load density is 12.32 kg/m2. On the other hand for each item an effective heat of combustion value has been assigned either from published data like that found in SPFE (2000) or from confidential experiments (Arvidson et al, 2008). The total fire load was estimated to 3.07 GJ, so the fire load density is 238 MJ/m2.

In order to perform an analysis of the fire load present according to methodology described before, four generic groups of materials have been assumed: textiles, wood-based, plastics and foams. From the description of the items listed in Figure 28, we have tried to assign each item to the generic list. Of course the combustible mass of an item can belong to different categories; however the specific relevant percentages were not available. From the descriptions of the materials of items,

Table 16 has been derived where the heat of combustion of each group has been calculated as the average of the respective values of the materials present. The total fire load estimated by this method is 3.52 GJ.

In order to compare this result with the respective calculated before, we will assume the same fuel load density as in Figure 19, second case (maximum value 15 kg/m2), while for the contribution of the groups to the total combustible mass two cases presented in Table 17 has been assumed. The derived fire load densities are presented in Figure 29. It can be deduced that the parameter that affects mainly the fire load density is the fuel load assumed and not so much the composition of the generic group materials.

Table 16: Assumed grouping of materials in the cabin and estimation of fire load.

type Weight (kg)

Weight (%)

∆hc (MJ/kg)

Fire load (MJ)

A: textiles 41.8 26.22% 18.27 763.5

B: wood 19.6 12.30% 17.00 333.2

C: plastics 70 43.91% 24.81 1737


D: foams 28 17.57% 24.5 686

sum 159.4 100% 3519.75

Table 17: Two scenarios for fire load densities distribution

case 1 case 2

case 1

∆Hc (MJ/kg)

case 2

∆Hc (MJ/kg)

textiles 35.00% 26.22% 22.50 18.27

wood based 45.00% 12.30% 17.00 17.00

Plastics 13.33% 43.91% 24.81 24.81

others 6.67% 17.57% 31.70 24.50

Figure 28: Passenger cabin data extracted from Arvidson et al, (2008).


Figure 29: Cumulative ascending distribution of the fire load density for the two cases. The dashed curve corresponds to the case 2.


6 Fire effluents and species production

6.1 Introduction and scope

The products of combustion like carbon monoxide (CO), carbon dioxide (CO2) and soot can be toxic and can affect the health of passengers onboard, thus their prediction during the fire is of great importance. Generally the generation of combustion products depend on the type of fuel, the model of combustion (e.g. flaming or smouldering) and the availability of air (well-ventilated or ventilated controlled fire).

One of the variables that quantify the combustion product is the yield of species parameter (eq.13) , which expresses the species mass emanating from the fire process in terms of mass loss of the fuel (Karlsson and Quintiere, 2000).

ii

f

my

m= (13)

Where im is the mass of i species produces and fm is the mass the mass lost in gasification.

Examples of the yields of various fuels can be found in Tewarson (2002), see Table 23 in Appendix 2. These values have been estimated for well ventilated conditions, this sufficient air exists in the enclosure. However for under-ventilated or else ventilation controlled fires the yields of CO and soot mainly increased significantly as the combustion is inefficient and so the species yields depends significantly on the available oxygen. So incomplete combustion can result to an increase in the production of CO and soot up to 100 times and thus not flaming and smouldering combustion can be proved extreme dangerous. However, the yields of products for ventilation-controlled fires are not steady, as in the case of the respective values for well-ventilated combustion, but depends on the equivalence ratio Φ which defined from (Eq. 14):

2f om m

rΦ = (14)

Where 2

mΟ is the available oxygen and r is the ideal reaction stoichiometric mass fuel to

oxygen ratio for complete combustion.

Table 18 and Figure 30 show the dependence of the yields on the equivalence ratio.

However at this stage we focused on the specification of the yields of CO, CO2 and smoke that emanate from the materials present in the enclosure. The scope is to derive distributions similar to that of fire load densities which express the availability of the enclosure to release fire effluents. Using the distribution of the materials inside the enclosure, expressed by the generic groups, and the related values of yields available in Tewarson, (2002) – see Appendix III for the materials that constitute in the categories, distributions of the yields of CO, CO2 and smoke can be derived. These will be valuable data also as input for fire modelling codes (either zone or CFD models). It should be mentioned that the respective yields at this stage will correspond to well-ventilated conditions, as this is the required input for the fire modelling codes.


Table 18: Yields of CO and smoke for various equivalence ratios (extracted from Tewarson, 2002)

Figure 30: Dependence of yields on equivalence ratio (adapted from Karlsson and Quintiere 2000).

6.2 Example

For demonstration the two cases as in Table 17 regarding the generic materials contribution to the total combustible mass has been regarded. Calculating the average yields for CO, CO2 and smoke for each generic group from data found in Tewarson (2002) and presented in Table 19 the distributions of CO, CO2 and smoke yields have been calculated and shown in the next figures.

Table 19: Average yields for the generic groups.

yields CO yields CO2 yields smoke textiles 0.051 1.420 0.065

wood based 0.004 1.280 0.015 Plastics 0.046 1.832 0.081

others (foams) 0.036 1.829 0.013


Figure 31: Cumulative ascending distributions of yields of CO for the two cases

Figure 32: Cumulative ascending distributions of yields of CO2 for the two cases

0.0

260

0.0

292

0.0

324

0.0

356

0.0

388

0.0

420


Figure 33: Cumulative ascending distributions of yields of smoke for the two cases.

0.0% 100.0%

0.0% 100.0%

0.03930 0.05990

0.0

0.2

0.4

0.6

0.8

1.0

yields smoke (g/g)

yields smoke: case 1

Minimum 0.03962

Maximum 0.04394

Mean 0.04175

Std Dev 0.00113

Values 1000

yields smoke:case 2

Minimum 0.05408

Maximum 0.05914

Mean 0.05672

Std Dev 0.00116

Values 1000


7 Conclusions

The fire risk onboard passenger ships will be addressed in a risk-based design context by rationalising mathematically the probabilistic elements that will lead to fire occurrence in various spaces. The focus of this report is the probability of ignition, estimation of the characteristics of the incipient phase and properties of fire parameters such as fire load and fire effluents for spaces onboard. The following points can be made:

• The probability of ignition model is based on a formulation proposed in the built environment industry, which combines the historical frequency of ignition (γ) and the floor area of an onboard space, cabin, restaurant, storage, etc. The floor area is introduced in the formulation from design information.

• The former part of the model is based on statistical data for floor areas for a number of passenger ships in combination to the ignition occurrences per SOLAS space obtained from the database compiled in the course of Task 1.1.

• The collected floor areas are described by the Rayleigh distribution. The 50th percentile of the distribution is used as the typical area per space for the derivation of γ.

• Benchmarking of the γ values with statistical data from buildings indicates that in most cases the frequency of ignition in a building falls in the minimum and maximum historical frequency values envelope derived in this report.

• The historical ignition frequency is the maximum frequency obtained for each of the passenger ships under consideration.

• As more fire incidents will be collected in the course of FIREPROOF, the derived model should be revisited and investigation into possible convergence to values should be identified.

• An ignition model for flaming combustion has been developed concerning the calculation of the incipient phase given the types of combustibles inside an enclosure and the strength of the ignition sources. Further improvement regarding the connection of the type of the ignition source with their strength will be considered in Task 1.4 where results for each SOLAS category will be available. Smouldering ignition can be regarded only from experimental data.

• A methodology to calculate fire load properties for an enclosure has been produced. More detailed estimations can be achieved given detailed data about the items inside the space.

• Finally, the specification in a probabilistic way of the yields of fire products has been presented taking into account the type and contribution of the combustible materials located in an onboard space.


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Appendix I – Rayleigh distribution

The probability density function (pdf) of the Rayleigh distribution is:

�� exp��2�� , � � 0, � � 0 (I-1)

The corresponding cumulative distribution function (cdf) is:

�� 1 � exp�� 2�� (I-2)

The mean and variance of the Rayleigh distribution are obtained as follows:

� � � !2 "� � ��4 � !�2 (I-3)

Figure I-1 – Rayleigh distribution for various values of δ

Appendix II – GA of two MVZs

Appendix III – Data from the literature Table 20: Heat of combustion of various materials, extracted from SPFE 2002

Table 21: Summary of NBS calorimeter tests, extracted from SPFE 2002.

Table 22: Critical heat flux and thermal response parameter of materials (extracted from SPFE 2002)

Table 23: Yields of fire products (extracted from Tewarson 2002)

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