MODELLING OF FIRES IN BUILDINGS WITH A CAD-BASED GRAPHICAL USER
INTERFACE
A.LANCIA, L.BORDIGNON and M.SINI TRI srl, Scanzorosciate (BG), Italy
G.GALLINA
CNR—ICITE, Sesto Ulteriano (MI), Italy
ABSTRACT: Fire risk assessment in buildings can be performed by running a representative set of simulations including modelling for fire dynamics, smoke transport and occupants movement. A software package is described allowing the user to define the modelling data while working in a friendly CAD environment. The analyst can choose to perform a certain simulation in a deterministic way or to indicate statistical distributions for input parameters and then generating and running a given number of simulations (stochostic mode). A graphical and numeric statistical post-processor is also added to help in the evaluation of stochastic modelling results. Fire and smoke transport modelling is accomplished by the CFAST (W: Jones et al.) multi-compartment zone model while evacuation is simulated by a rule-based proprietary code. A sample application of the package is given for a 13 compartments building hosting an applied research laboratory. Typical expected applications of the developed system such as cost/benefit analysis, performance oriented design and support for fire engineering courses are discussed.
1. Introduction to the TRISTAR project
The TRISTAR project was started in 1989 with the aim of bridging the gap between fire modelling research and its application to practical problems in fire safety engineering for large buildings where such an approach can be useful and potentially cost effective.
The project was executed by the R&D group of the company Tecsa SpA within the framework of “Progetto Finalizzato Edilizia” which is a large R&D actions (total budget about 50 million ECU) promoted by the Italian National Council of Research (CNR) through their ICITE Institute (the body in charge of building technology). The project was completed in 1994 by TRI srl which is a company formed in 1993 by detaching from Tecsa SpA the R&D and the technology departments. A close co-operation with the
researchers of ICITE was maintained during the work and a joint effort is still in progress with the aim of refining the software and applying it on a series of test cases.
The following basic needs were identified from the beginning of our work:
• allowing the software to be applied by fire safety engineers in a user friendly way
• taking into account a representative set of fire scenarios and the variability of their development
• incorporating a fire & smoke transport modelling software with a sufficient accuracy but within the execution time constraints related to the need to run the models thousand of times with a reasonable time and computing budget
• incorporating a suitable model for the prediction of the occupants movement
• producing results in a form useful to fire safety engineers
The problem of user friendliness is a general one for engineering packages. In the case of multi-compartment fire models the main discouraging aspect for the users is the long and boring procedure which is necessary to prepare the data sef which is describing the building
Fire Engineering and Emergency Planning. Edited by R.Barham.
Published in 1996 by E & FN Spon. ISBN 0 419 20180 7.
and the input variables needed for the computation. The decision was therefore taken in the project to use a CAD environment as the main MMI (man-machine interface) and developing a series of procedures to input the required data while interacting with the graphical building layout.
A major issue in the project was the need to take into account for each fire scenario the variabilily of its development. Regardless the complexity and the accuracy of the fire model being used, a single deterministic computation is just producing a set of output data that cannot be taken as the meaningful answer to the problem of forecasting the consequence of a certain fire initiation. The main reasons for this are the following:
• the description of the building and its contents is necessarily incomplete
• a significant uncertainty often exists in the actual input data
• the combustible items in the building are often subject to changes in their position within compartments
• any of the doors and windows are never always open or close at different times
• models are more or less precise but they are never reproducing exactly the actual phenomena
• the behaviour of peoples movement is not deterministic
• the variability of people behaviour affects the chances to escape safely but also the fire scenario dynamics (e.g. they open or close the doors)
• the response of fire detection and fire suppression systems cannot be modelled sufficiently well and such systems are characterised by a rather good but limited reliability
Therefore a meaningful evaluation of the outcome of an initial fire scenario requires to carry out a large number of calculations where the scenario development is different by reflecting the input data variabilily and the branching processes related to the decisions of
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occupants and to the consequences of such decisions on fire and smoke transport dynamics.
A basic choice had to be made initially on the type of model to be used for fire dynamics and smoke transport. The use of CFD models (field models) was excluded for the impossibility to run such codes automatically and for a large number of times within reasonable computing resources. The only major limitation in choosing zone models was that they are applicable only to sets of small or medium size compartments and therefore they cannot deal with large atria, underground stations, industrial spaces with single large volumes, etc. This is not however a very important problem since most of the buildings we are interested in are made of a number of small compartments (offices, hospitals, schools, etc.).
Concerning evacuation modelling we chose to exclude any deterministic model based on the optimisation of evacuation or on the straightforward application of queue theory on predetermined pathways. The reason for this is that such models are useful only for preparing emergency evacuation plans or in those cases where the building is accessible only to certain persons and all of them have been trained to follow predetermined escape ways (e.g. the case of military ships, certain research centres, etc.). For our purpose we wanted an evacuation model that could simulate the actual behaviour of the occupants taking into account the results of the recent studies on the behaviour of people in fires (e.g. J.Sime et al.).
Running multiple fire simulations is necessary for our purpose but the huge set of output data is impossible to be evaluated directly regardless the forms in which such results are produced. Therefore we decided to design a module to merge the results of the individual simulations and to produce a series of tables and graphs in order to show the overall results and to evaluate the statistical distributions, the correlation and the sensitivity of the outcome to certain input parameters.
Fig. 1 shows a simplified diagram of the first conception of the TRISTAR system. A module for deterministic modelling was included to offer the possibility to use the data input by CAD for running without large efforts some modelling programs such as EVACNET and CFAST.
The first year of the CNR TRISTAR project was devoted to the reviewing of available modelling programs and to collect additional information for designing the software. The system design was carried out in the second year and the software was developed in its first version during the third year.
This section contains a summary of the functional specification of the TRISTAR software package version as it was implemented in 1994.
Fig. 1 shows the structure of the system indicating the main software modules and the principal sets of data files.
2. General information on the system TRISTAR provides the user with the following main functions:
• entering the description of the building model by defining a “virtual building description” (VBD) within an application using a standard architectural CAD
• executing the fire & smoke dynamics models
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• performing the evacuation simulation for a defined VBD
• displaying in graphical form the results of modelling
• generating series of varied VBD and executing the related modelling in order to perform sensitivity analysis and randomised studies
• analysing the results of the execution of multiple VBD modelling
The drawing of the building can be prepared or imported in the CAD system and can be used as a graphical background for defining the VBD.
Fire and smoke dynamics modelling is performed by the program CFAST by W.Jones at al. (US-NIST). The VBD structure is defined by nodes and arcs. VBD nodes and arcs directly correspond to the nodes (compartments and ventilation nodes) and arcs (links between nodes) used by CFAST. A description of CFAST can be found in the documents produced by NIST and available through the US NTIS.
Evacuation modelling is performed by a proprietary TRI software hereby indicated as EMS (evacuation modelling system). A subset of VBD nodes and arcs corresponds to the occupants movement network used by EMS. The EMS model is based on the assumption that occupants at a node make a decision on what they should do and then they execute the selected move until they reach another node where they will make next decision.
Decision making for the occupants is based on a set of rules that are used to set statistical likelihood for the possible alternative decisions (i.e. moving to a certain node, waiting, etc.). The actual move is determined by a random choice weighted on the calculated likelihood. The rules can be chosen by the user and they reflect the current knowledge on occupants behaviour. The data used for computing the rules are the relevant perception of the occupant such as the presence of an escape way sign, the view of some other occupants on one end of a corridor, the presence of smoke, an audible alarm, etc. The actual movement of the occupants from one node to the other is based on the usual algorithms used in deterministic modelling of people movement in buildings. This model is still being refined at TRI.
The user defines the VBD by creating, deleting and editing nodes and arcs which are corresponding to certain graphic patterns within CAD and to which a list of editable attributes is associated. The nodes and arcs attributes comprise all the information related to modelling by CFAST and EMS plus certain data indicating if and how the individual information must be varied to generate the stochastic data sets (when the user is asked for data which can be randomised to generate a set of input data files, he is given the possibility to define—in addition to a base value—a statistical distribution for the input data).
The user navigates in the system without accessing the operating system command level by interacting with a supervisor system software.
The system has been developed on an IBM RS6000 workstation using as a main developing shell the Microstation CAD system by Intergraph. The modules not developed within Microstation are written in C language.
In the first program version the models were running on a PC connected by a TCP-IP ethernet link while the current version is fully implemented on the workstation following the kind decision of NIST to make the CFAST source files available.
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3. Applications of the TRISTAR system
TRISTAR can be applied to different problems in fire safety engineering. We mention here the principal ones with a few short comments.
3.1 cost vs. benefit analysis
A common problem in fire safety engineering is deciding which is the most cost effective way to reduce risk in a certain building. This can be accomplished by selecting a set of reference fire scenarios (they might be the “worst cases”) and then running a stochastic simulation for each of the possible changes introduced to improve the safety level (e.g.
one more escape way, fire detectors, smoke venting, stairwell pressurisation, etc.).
A engineering decision can be made by comparing the expected improvements and taking into account the cost of the alternate solutions. In some case the cost will just be limited by a maximum budget and the system will help to select the best solution or combination of solutions within such economic constraint.
3.2 evaluation of the fire safety impact of changes in building structure or in its use
In this case the reference fire scenarios are identified and the stochastic simulations are run with and without taking into account a planned modification to the building or its contents and use. The risk histograms produced from the simulation will help in deciding if authorising the change or conditioning the permission to the implementation of better prevention and/or protection means.
3.3 performance oriented design
The idea of performance oriented design consists in setting a safety goal and then leaving the building designers and the fire safety engineers free to adopt the solutions they wish, providing that the safety goal is reached. In the most common opinion this will not however cause the relaxation of a series of basic rules such as the maximum distance between fire exits, the availability of fire extinguishers etc. Performance oriented design looks to be the only way to allow architects and engineers to create new buildings without rule based constraints only but within the respect of safety. Cost benefit analysis concepts are frequently used within this kind of application.
3.4 risk analysis
A system like TRISTAR can be used for the evaluation of risk (to human lives or to property). In this case the basic approach consists in:
• selecting or generating a representative set of fire scenarios
• running stochastic simulation for each scenario
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• adding the risk from each simulation after multiplying it for the expected frequency of the relevant scenario
An absolute risk evaluation can be useful in a number of cases such as the negotiation of insurance costs and conditions.
3.5 training
TRISTAR could be used in training courses for fire safety engineers, fire brigades officers, civil engineers, etc. The software can be a tool to be used by a teacher in the classroom with the help of a computer screen projector and/or accessed by the trainees for carrying out exercises aiming to explore through modelling a series of notions such as how the building design features affect fire dynamics and smoke movement.
3.6 research in fire safety engineering
The TRISTAR system is designed in such a way to allow the replacement of the fire model with a limited effort. Thus the researchers can use the system as a test bench for their models allowing the user friendly input of building data, carrying out sensitivity analysis and comparing the predictions by different models.
4. An example application of the TRISTAR system
In order to give an example of the use of TRISTAR we report in this section a relatively simple exercise demonstrating the kind of application mentioned in 3.2.
This example is explained in great detail in Volume II of the final TRISTAR report to CNR and here we will just give a minimum of information to illustrate the application example.
The building used for the exercise is a two storey rectangular premise used by a department of a research institute for the development of special electro-optics instrumentation.
The building layout is represented in the annexed printouts of computer screens documenting the data input process. Two rows of offices and laboratories are stacked along one of the longer sides of the building while the other part is occupied by a large hall conceived to carry our experiments and miscellaneous work without constraints, with a flexible use of space and with the possibility to move large items in and out through the large doors at the two extremities of the hall. Structural beams are dividing the building and particularly the ceiling of the test hall into three sections. The hall was therefore simulated by three large rooms with open intercommunicating tall doors.
A total of 13 persons are normally present in the building (their work, functions and habits are described in the CNR report)
The example calculation we give here is related to the problem of deciding if authorising or not the regular use of flammable substances in the test hall. This problem arises because former simulations demonstrated that a fire in the test hall can make the evacuation of the upper rooms impossible in a few second unless costly changes are made
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such as the installation of smoke vents on the ceiling or the closure of the balcony and the stairwell e.g. with a series of air tight glass panels.
The fire scenario used for this example arises at an apparatus for generating atmospheric samples containing organic pollutants. A two litres glass bottle containing a mixture of alkylnitryles dissolved in n-hexane falls and breaks. The vapours are ignited and a spill fire rapidly develops. The fire area is however limited by confinement to about 1 square meter. Hexane combustion is completed within 40 sec but fire continues due to the combustion of plastic parts of the equipment including polyurethans, polyethylene and polypropylene. The generation of CO and HCN was considered and their dispersion modelled by CFAST. In this fire the heat release is quite high for a short time followed by a variable but mild combustion. Simulations were carried out for a fire time of 1000 sec.
The most critical element in the stochastic simulation are the status of doors and the distribution of occupants. The following values and statistical distribution were used in the exercise.
input variable type of distribution parameters of the distribution door status 5–9 binary P(0.00)=0.5; P(1.00)=0.5
door status 6–9 binary P(0.00)=0.9; P(1.00)=0.1 door status 10–7 binary P(0.00)=0.9; P(1.00)=0.1 door status 11–8 binary P(0.00)=0.9; P(1.00)=0.1 door status 12–8 binary P(0.00)=0.7; P(1.00)=0.3 door status 13–9 binary P(0.00)=0.9; P(1.00)=0.1
n. occupants cmpt. 1 4 values int. P(0)=0.1; P(1)=0.8; P(2)=0.1; P(3)=0.0 n. occupants cmpt. 5 4 values int. P(0)=0.2; P(1)= 0.3 P(2)=0.4; P(3)=0.1 n. occupants cmpt. 6 4 values int. P(0)=0.1; P(1 )=0.8; P(2)=0.1; P(3)=0.0 n. occupants cmpt. 10 4 values int. P(0)=0.1; P(1)=0.3; P(2)=0.3; P(3)=0.3 n. occupants cmpt. 1 1 4 values int. P(0)=0.1; P(1)=0.3: P(2)=0.6; P(3)=0.0 n. occupants cmpt. 12 4 values int. P(0)=0.1; P(1)= 0.3; P(2)=0.6; P(3)=0.0 n. occupants cmpt. 13 4 values int. P(0)=0.1; P(1)=0.1; P(2)=0.2; P(3)=0.6
The exercise is addressing the evaluation of the usefulness of an automatic fire detection system that could be installed in order to allow the use of flammable substances in the hall. Therefore the stochastic simulation is repeated for the following three data sets.
data set alarm tripping time
A 15 sec i.e. the detection system is correctly functioning
B 120 sec due to the undetected failure of smoke detector in cmpt. 8 C 2000 sec—the system doesn’t work or it is not installed
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The exercise was carried out before the models were ported to the RS6000 workstation and therefore the number of modelling run was limited to 30 due to the time required to run the modelling on a 66 MHz 80486 based PC (each CFAST calculation took between 12 and 150 minutes). Even though smooth converged distributions are not reached, the limited number of runs is sufficient to demonstrate the technique.
The three tables in Fig. 9, 10 and 11 collect the door status and the number of occupants of 7 compartments for each of the data input files in each set of simulations.
The results are reported in by specifying the number of successfully evacuated occupants for each run and by giving the statistical frequency distribution of total evacuees from
The results are reported in by specifying the number of successfully evacuated occupants for each run and by giving the statistical frequency distribution of total evacuees from