research report 255 · computational fluid dynamics (cfd) to predict the transport of smoke in...

HSE Health & Safety

Executive

Evaluation of CFD to predict smoke movement in complex enclosed spaces

Application to three real scenarios: an underground station,an offshore accommodation module and

a building under construction

Prepared by Health and Safety Laboratory for the Health and Safety Executive 2004

RESEARCH REPORT 255

HSE Health & Safety

Executive

Evaluation of CFD to predict smoke movement in complex enclosed spaces

Application to three real scenarios: an underground station,an offshore accommodation module and

a building under construction

Dr N. Gobeau and Dr X.X. Zhou Fire and Explosion Group

Health and Safety Laboratory Harpur Hill

Buxton SK17 9JN

RI, TD and OD HSE divisions have jointly commissioned HSL to investigate the capabilities and limitations of Computational Fluid Dynamics (CFD) to predict the transport of smoke in complex enclosed spaces. Despite a lack of validation for this application, CFD is increasingly used in safety cases as a predictive tool to demonstrate the effectiveness of modern building designs and/or emergency ventilation to control the movement of smoke in the event of a fire. To meet the objective, HSL has undertaken a research project combining CFD modelling and experiments. The work consisted of three distinct phases:

Phase 1 comprised CFD calculations relating to three real “complex spaces”. These were an underground station, an accommodation module on an offshore platform and a high rise building under construction. Different CFD modelling approaches were used to investigate their effect on the prediction of smoke movement. The particular modelling approaches tested were representative of those being employed in fire safety engineering.

Phase 2 produced a “benchmark” dataset of experimental measurements of the movement of hot smoke in simple small scale structures. A range of basic geometries were constructed, instrumented and tested, each addressing a particular aspect of the physical behaviour of smoke layers. While the experiments were deliberately simplified to concentrate on the fundamental of smoke movement, the geometries were similar to those found in the three real cases - corridors/tunnels, both horizontal and sloping, larger open spaces and tall atria.

Phase 3 was a detailed examination of CFD performance in modelling the phase 2 benchmark experiments. The specific aspects of the modelling process were varied, such as the computational grid, and the discretisation scheme. The results of each calculation were compared with measurements, allowing the level of agreement to be quantified. The results of the different modelling approaches were also compared to quantify their relative effects.

The present report contains the description and conclusions of the work related to Phase 1, the modelling of three real scenarios by different approaches.

This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

HSE BOOKS

© Crown copyright 2004

First published 2004

ISBN 0 7176 2881 7

All rights reserved. No part of this publication may bereproduced, stored in a retrieval system, or transmitted inany form or by any means (electronic, mechanical,photocopying, recording or otherwise) without the priorwritten permission of the copyright owner.

Applications for reproduction should be made in writing to:Licensing Division, Her Majesty's Stationery Office, St Clements House, 2-16 Colegate, Norwich NR3 1BQ or by e-mail to [email protected]

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Summary

Objectives

RI, TD and OD HSE divisions have jointly commissioned HSL to investigate the capabilities and limitations of Computational Fluid Dynamics (CFD) to predict the transport of smoke in complex enclosed spaces. Despite a lack of validation for this application, CFD is increasingly used in safety cases as a predictive tool to demonstrate the effectiveness of modern building designs and/or emergency ventilation to control the movement of smoke in the event of a fire.

To meet the objective, HSL has undertaken a research project combining CFD modelling and experiments. The work consisted of three distinct phases:

Phase 1 comprised CFD calculations relating to three real “complex spaces”. These were an underground station, an accommodation module on an offshore platform and a high rise building under construction. Different CFD modelling approaches were used to investigate their effect on the prediction of smoke movement. The particular modelling approaches tested were representative of those being employed in fire safety engineering.

Phase 2 produced a “benchmark” dataset of experimental measurements of the movement of hot smoke in simple small scale structures. A range of basic geometries were constructed, instrumented and tested, each addressing a particular aspect of the physical behaviour of smoke layers. While the experiments were deliberately simplified to concentrate on the fundamental of smoke movement, the geometries were similar to those found in the three real cases - corridors/tunnels, both horizontal and sloping, larger open spaces and tall atria.

Phase 3 was a detailed examination of CFD performance in modelling the phase 2 benchmark experiments. The specific aspects of the modelling process were varied, such as the computational grid, and the discretisation scheme. The results of each calculation were compared with measurements, allowing the level of agreement to be quantified. The results of the different modelling approaches were also compared to quantify their relative effects.

The present report contains the description and conclusions of the work related to Phase 1, the modelling of three real scenarios by different approaches.

Main Findings

All the CFD modelling approaches employed provided results that looked realistic. In each case they could in principle be used as a basis of an engineered approach to fire safety. In particular, the effects of complex geometry and forced ventilation on smoke movement are readily addressed using CFD.

However, a comparison of quantitative data; such as the temperature of the hot layer, the depth of the smoke layer along the ceilings, and the rate of propagation of smoke, showed that these key parameters can vary significantly - depending on the modelling approach used. The

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particular conclusions we can draw from the modelling approaches applied in these three real scenarios are:

� Surprisingly, differing grid resolution did not lead to significant differences in smoke movement. This was because both grids employed here, including the coarser one, were appropriate for the scenario modelled. In general, however, the grid resolution needs to be fine enough to adequately capture the key flow phenomena.

� The use of a high order convection discretisation scheme resulted in the prediction of more flow detail and a more rapid rate of smoke spread. Ideally, second- or higher-order accurate schemes should be used. First-order scheme may be acceptable providing the grid is not too coarse and that the resulting error is shown to be conservative.

� A Boussinesq approximation to account for thermal effects on flow compressibility under-predicted the temperatures and the rate of smoke propagation when compared to a more valid approach of calculating the air density from an equation of state. A Boussinesq approximation should therefore not be used unless there is clear evidence that the error is conservative.

� A standard k-e turbulence model failed to predict the correct behaviour of the flow. An additional buoyancy-related production term was found necessary to reproduce successfully the features of the flow. It is highly recommended that it is implemented in any model of transport of smoke.

� A volumetric heat source model and an eddy-break-up combustion model both provided acceptable and ultimately similar results for smoke propagation.

� However, the realistic prescription of the fire source was found to be crucial for both a volumetric heat source model and an eddy-break-up combustion model. Since a volumetric heat source model requires more assumptions input to the source (heat and volume output) than an eddy-break-up model, the latter is likely to provide more realistic results for situations where the fire shape is a-priori not well defined and/or may vary with time.

� The boundary conditions for heat transfer at the walls were found to have an impact on the transport of smoke, but this was highly dependent on the scenario - they were more crucial for a confined fire and in the absence of forced ventilation.

Main Recommendations

A significant, although limited, number of CFD simulations have been undertaken for three real scenarios. These obviously do not cover all the situations that Fire Safety Engineers are likely to encounter. Hence the present findings and conclusions must be interpreted with caution, especially for scenarios in which the flow characteristics could be very different near the fire source.

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All of the existing CFD modelling approaches could not be reviewed in this work. It was decided to concentrate on those most commonly employed and therefore the most likely to be presented in safety cases submitted to HSE. Amongst the models not included in this study are those for simulation of radiation and advanced turbulence models.

Nevertheless, the simulations have examined some of the main modelling approaches being employed in fire safety engineering. They therefore do allow general conclusions to be drawn.

In particular, the scenario-dependent sensitivity of the results to the detailed modelling approach employed means that it is vital that the user of CFD for smoke movement applications is knowledgeable and well trained both in CFD and fire science.

Since, however, the sensitivities to the modelling approaches are not always evident a-priori, it is also strongly recommended that a set of CFD simulations be undertaken, rather than a one-off case; which could be misleading. This set of CFD simulations should focus on the potential key sensitivities.

CFD is under constant development. Any new models made available to the Fire Safety community should be carefully assessed before trust in their predictions can be gained.

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Contents

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. SCENARIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1. Description of premises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.1. Underground station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.2. Offshore accommodation module . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.3. Building under construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2. Possible fires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2.1. In the underground station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2.2. In the offshore accommodation module . . . . . . . . . . . . . . . . . . . . . 52.2.3. In the building under construction . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3. ILLUSTRATION OF CFD CAPABILITY: INITIAL MODELLING . . . . . . . . . . . . . 7

3.1. CFD code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.2. Computational domain and grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2.1. Underground station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2.2. Offshore accommodation module . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2.3. Building under construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.3. Physical sub-models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.4. Fire source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.4.1. In the underground station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.4.2. In the offshore accommodation module and the building

under construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.5. Boundary conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.6. Initial conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.7. Numerical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.8. Convergence criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.9. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.9.1. Underground station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.9.2. Offshore accommodation module . . . . . . . . . . . . . . . . . . . . . . . . . . 153.9.3. Building under construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.9.4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

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4. COMPARISON OF DIFFERENT MODELLING APPROACHES . . . . . . . . . . . . 18

4.1. Underground station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.1.1. Flow compressibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.1.2. Buoyancy effects in the k-e turbulence model . . . . . . . . . . . . . . 204.1.3. Heat transfer at the walls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2. Offshore accommodation module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2.1. Grid size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2.2. Discretisation scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.2.3. Heat transfer at the walls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.3. Building under construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3.1. Shape of the prescribed heat source . . . . . . . . . . . . . . . . . . . . . . . 284.3.2. Fire growth curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.3.3. Fire modelling approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5. CONCLUSIONS AND RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.1. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2. Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

7. ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

APPENDIX A - Summary of scenarios and initial CFD models

APPENDIX B - Figures related to the initial simulation of the underground station

Figure B.1 - Layout of the underground station

Figure B.2 - Computational domain and grid

Figure B.3 - Background ventilation inside the underground station

Figure B.4 - Airflow field and iso-surfaces of smoke concentration 3 minutes after ignition (forced ventilation off)

Figure B.5 - Airflow field and iso-surfaces of smoke concentration 5 minutes after ignition, just before forced ventilation is switched on.

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Figure B.6 - Airflow field and iso-surfaces of smoke concentration 46 seconds after forced ventilation started.

Figure B.7- Airflow field and iso-surfaces of smoke concentration 65 seconds after forced ventilation started.

APPENDIX C - Figures related to the initial simulation of the offshore accommodation module

Figure C.1 - Layout of the offshore accommodation module : ground and first floors.

Figure C.2 -Layout of the offshore accommodation module : first and second floors.

Figure C.3 - Computational domain and grid.

Figure C.4 - Fire source in the laundry.

Figure C.5 - Iso-surfaces of smoke concentration 60 seconds after ignition.







APPENDIX D - Figures related to the initial simulation of the building under construction

Figure D.1 - Schematic diagram of the building under construction, side elevation.

Figure D.2 - Computational domain and grid.

Figure D.3 - Fire source and computational grid on third floor.

Figure D.4 - Smoke iso-surface 30 seconds after ignition.


Figure D.6 - Airflow field and smoke iso-surface 120 seconds after ignition.



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APPENDIX E - Comparison between different CFD modelling approaches for the underground station

Figure E.1 -Temperature distribution near the fire 115 seconds after ignition.

Figure E.2 - Smoke concentration at the walls 60 seconds after ignition


Figure E.4 -Smoke concentration at the walls 115 seconds after ignition

Figure E.5 - Time-dependent smoke fluxes across vertical planes at entrance, mid-length and exit of the bridge.

Figure E.6 - Time-dependent smoke fluxes across vertical planes at the entrance of the corridor leading to exit 2 and at exit 2.

APPENDIX F - Comparison between different CFD modelling approaches for the offshore accommodation module

Figure F.1 - Comparison of the two different grids employed.

Figure F.2 - Temperature distribution and velocities in the laundry, 120 seconds after ignition.

Figure F.3 - Profiles of excess temperature and vertical velocity in the fire source, 120 seconds after ignition.

Figure F.4 - Profiles of excess temperature, smoke mass fraction and lateral velocities near the fire, 120 seconds after ignition.

Figure F.5 - Temperature distribution and velocity vectors on the first floor location of the fire - 45 centimetres above the ground, 120 seconds after ignition.

Figure F.6 - Temperature distribution and velocity vectors on the first floor location of the fire - 2 metres above the ground, 120 seconds after ignition.

Figure F.7 - Profiles of excess temperature, smoke mass fraction and velocity in the doorway of the laundry opening to a corridor, 120 seconds after ignition.

Figure F.8 - Profiles of excess temperature, smoke mass fraction and velocity in the doorway of the laundry near a stairwell, 120 seconds after ignition.

Figure F.9 - Smoke iso-surfaces 120 seconds after ignition.

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APPENDIX G - Comparison between different CFD modelling approaches for the building under construction

Figure G.1 - Excess temperature and mesh near the fire 150 seconds after ignition

Figure G.2 - Vertical profiles of excess temperature and smoke concentration in the centreline of the fire 150 seconds after ignition

Figure G.3 - Vertical profiles of excess temperature and smoke concentration ten metres away from the fire, 150 seconds after ignition

Figure G.4 - Horizontal profiles of excess temperature and smoke concentration 4.55 metres above the fire, 0.45 metres from the ceiling 150 seconds after ignition.

Figure G.5 - Excess temperature distribution on the third floor - location of the fire,150 seconds after ignition.

Figure G.6 - Smoke concentration distribution on the third floor -location of the fire,150 seconds after ignition.

Figure G.7 - Smoke concentration distribution in the atrium, 150 seconds after ignition.

APPENDIX H - Details of the buoyancy modification of the k-e turbulence model in CFX5.

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1. INTRODUCTION

Computational Fluid Dynamics (CFD) is a powerful technique which provides an approximate solution to the coupled governing fluid flow equations for mass, momentum and energy transport. The flexibility of the technique makes it possible to solve these equations for fluid flow in very complex spaces.

Originally widely used by the aerospace and other manufacturing industries, CFD is now being increasingly employed in fire safety engineering to predict the movement of smoke from possible fires in complex enclosed spaces, such as atria, shopping malls, warehouses, etc... For instance, London Underground Ltd. applied CFD to examine the effectiveness of smoke control systems in tunnels and stations on the Jubilee Line Extension, particularly in relation to novel design features such as platform edge doors and smoke canopies. Eurotunnel commissioned CFD work during the construction of the Channel Tunnel and the incident investigation following the 1996 fire. CFD has also been employed to predict smoke movement in the Millenium Dome (Sinai et al., 2000).

In each of the above examples, reliance could not be placed on simpler modelling techniques to predict smoke movement. This is primarily because these simpler methods assume that the space of interest is geometrically simple, i.e. a box-shaped room. Also, they often require empirical input. When the space is geometrically complex and experimental data is absent, CFD approaches are becoming more commonly used. The growth in the use of CFD for the prediction of smoke movement is also a result of the shift from prescriptive codes to predictive methods - as allowed in fire safety engineered solutions to the problems of fire.

As the use of CFD in fire safety engineering is growing, it is likely that HSE will be increasingly faced with assessing fire safety cases which are either entirely or partly based on CFD simulations. Although CFD is a powerful technique, it does however have its limitations. It is very important to be aware of these when assessing the conclusions drawn from CFD predictions.

Unfortunately, CFD modelling has not been carefully evaluated against reliable data for complex smoke movement applications. RI, OSD and TD divisions of HSE are therefore funding a project to address this issue. The main aim of the project is to quantify the advantages and limitations of CFD for predicting smoke movement in complex enclosed spaces. The work comprises:

1. Initial application of CFD to real scenarios of interest to HSE funding divisions to illustrate the capabilities of CFD modelling;

2. Sensitivity of the CFD results to a range of modelling approaches widely employed by the Fire Engineering Community;

3. Experimental measurements at small-scale, focusing on areas identified by the initial CFD simulations as potentially challenging for CFD;

4. CFD modelling of the small-scale experiments and quantification of the accuracy of the CFD solution by comparing against experimental data; the influence of CFD sub-models and parameters on the accuracy of the solution will be investigated;

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This report presents the CFD modelling of real scenarios in tasks 1 and 2 above. The benchmark experiments and their CFD modelling in tasks 3 and 4 are described in the report HSL CM/01/18 (Ledin et al., 2004). The findings of the whole project are the basis of a guidance note aimed at HSE Inspectors to help them assess CFD results used to support safety cases (Gobeau et al., 2003). The project is summarised in the report HSL CM/03/15 (Gobeau et al., 2004).

A total of three real scenarios were simulated: an underground station, an offshore accommodation module and a building under construction. They were selected for their complex geometry and for their relevance to each HSE division funding the project. Possible fires, and their induced smoke movement, were modelled. It should be stressed that the specific cases were chosen as being representative of the type of application for which CFD could be employed. They were not selected because of any outstanding concern over fire safety in these premises, merely as illustrative examples. They are described in Section 2.

Section 3 presents an initial CFD modelling of these scenarios. Qualitatively, smoke propagation was predicted quite realistically in all three scenarios. The direction and rate of smoke propagation, together with effects of any ventilation systems, are predicted clearly highlighting the potential of CFD. However, in the absence of measurements, the accuracy of these results cannot be readily assessed. This is due to potential errors from a number of sources; the adequacy of the modelled governing equations, simplifications in the modelled geometry, grid and discretisation errors, assumed boundary conditions, etc... It is therefore important to highlight that these initial simulations informed the design of the small-scale experiments; ultimately providing a quantitative assessment of CFD for the modelling of the smoke movement.

Whilst it is not readily possible to draw absolute conclusions on the accuracy with which smoke movement is predicted for these real scenarios, it is however possible to illustrate the sensitivity of the results to a range of modelling approaches. When setting up a model, CFD practitioners have to make numerous numerical and physical assumptions. These will depend on a number of factors, such as the scenario modelled, the resources available - in computer, time and fundings, as well as on the user expertise. For example, the physical processes of the fire itself can be described by a variety of different sub-models of varying complexity. The simplest approach is a prescribed heat source model; in which the fire is represented by imposing a heat release rate in a pre-determined volume. Although this is a simple approach, it is very widely used (Hadjisophocleous et al., 1999; Sinclair, 2001; Tonkelaar, 2001). In more advanced approaches a combustion model is used - which means that the volume in which heat is released - the flaming region, is predicted, rather than prescribed. The modelling of combustion is a whole branch of science in itself. When a combustion model is used in Fire Safety engineering, often a crude approach is taken - as exemplified by ‘Eddy Break-Up’ models (Sinai, 2001; Yau et al., 2001; Drake and Meeks, 2001). Whilst this approach can often lead to a more realistic representation of a fire than a prescribed heat source, it should be noted that it is still a gross approximation. Indeed, there are on-going discussions amongst CFD Fire Safety practitioners on the actual benefits of this approach compared to the simpler heat source representation (Xue et al., 2001; Kumar and Cox, 2001).

Section 4 therefore compares different CFD modelling approaches, representative of those commonly employed by the Fire Safety Engineering community and covering those likely to

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be encountered in fire safety cases submitted to HSE and other regulations. It is important to stress that the CFD modelling in this report is very representative of approaches actually employed. It is not claimed to be ‘state-of-the-art’ from an academic perspective. Indeed, that is not its aim. Readers who would like to gain a comprehensive knowledge of the modelling approaches available to predict smoke movement are invited to refer to Cox (1995) or Grant and Lea (2001).

The sensitivities which have been explored are as follows:

� Different computational grids;

� First and second order convection discretisation schemes;

� Heat transfer boundary conditions at the walls;

� Representation of the fire source: prescribed heat source and simple eddy break-up combustion model; different prescribed fire growth curves.

This has made it possible to quantify the effects of these differing approaches on the prediction of temperature distribution, air flow field and smoke propagation between the different models as applied to real complex scenarios. This is important: it illustrates where key sensitivities may lie in the practical application of CFD to the modelling of smoke movement information which is significant for both the CFD practitioner and regulator.

It must be noted that for practical reasons it has not been possible to employ each modelling approach for each scenario. However, lessons can be learnt and extrapolated from one case to another. Therefore the reader is strongly encouraged to look at all the scenarios, even though if interested in only one application, for instance railway or construction safety.

Finally, conclusions are drawn on CFD’s capabilities and limitations for the prediction of smoke movement in complex enclosed spaces in Section 5.

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2. SCENARIOS

To illustrate CFD modelling of smoke movement in complex enclosed spaces of interest to HSE, each sponsoring division provided a representative real scenario. Hence, an underground station was investigated for RI; an offshore accommodation module for OSD and a building under construction for TD. For each of these spaces, a possible fire - its power and location - was defined.

2.1. Description of premises

2.1.1. Underground station

An underground station on the Jubilee Line Extension was chosen as representative of a complex space for which RI may have to assess a fire safety case. The station was visited by HSL staff, accompanied by a representative of London Underground Ltd.

The station is on four levels, which are from top to bottom: street level with three exits; a ticket hall; platforms for the East London line; platforms for the Jubilee line. The ticket hall is a large space, the floor dimensions of which are roughly 30 m x 30 m. Its height is 3.75m except where it is roofed by a glass dome housing the main exit. Escalators and stairs lead from the paid area to the Southbound platform of the East London line on one side and to the Southbound East London line on the other side. From the ticket hall, three exits lead outside: one passes directly to surface level via a short flight of stairs; the other two are accessed via a bridge overlooking the stairs and escalators of the Southbound platform of the East London line. This bridge is enclosed by glass windows. A layout of the station is presented in Figure B.1 (in Appendix B).

The station is equipped with smoke detectors and for the purpose of fire safety is divided into a number of zones. The emergency response upon detection of fire depends upon the zone in which it is located, but is typically forced ventilation from lower levels exhausting to atmosphere via the three exits.

2.1.2. Offshore accommodation module

OSD have a regulatory duty to assess offshore safety cases. These can include the consequences of fire in living quarters. An accommodation module on an existing platform was thus considered suitable for the present study. Diagrams of the layout of the module were supplied and are presented in Figures C.1 and C.2 (in Appendix C) showing the module has four main floors. From ground floor to the second floor are the utility and public areas: operating rooms, laundries, kitchen, dining room, recreational rooms, etc...The third floor houses bedrooms. Two staircases at the opposite ends of the module link all the floors. These staircases do not have a central ‘well’.

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2.1.3. Building under construction

FOD, advised by TD, is responsible for regulating safety on construction sites. This includes fire safety during the construction and fitting-out stage of a building. Although the building will eventually be equipped with a detection system, and mitigation via sprinklers and possibly ventilation, these systems are unlikely to be fully operational until construction is finished. During the building and fitting-out phase, there are also fire hazards present which will not be seen during normal occupation, i.e. electrical power tools, waste packing material, etc...

An eighteen-storey office building under construction in London was chosen as representative of a typical large development and visited by HSL staff. A 76m high atrium links all the floors. With the exception of the first floor, two open bridges cross the atrium at each storey. In addition, there are two staircases which give access to all floors and offer a possible additional route for smoke transport. The main part of each floor is an open-plan area. Figure D.1 presents a schematic diagram of the building.

2.1.4. Summary

Altogether, the three scenarios provide the opportunity to test CFD in a wide range of real complex enclosed spaces. The geometrical features of these spaces include: large floor areas with restricted elevation, i.e. the ticket hall in the underground station and open-plan offices in the building under construction; large vertical spaces, i.e. building atrium; a complex network of interconnecting rooms and corridors, i.e. offshore accommodation module; levels interconnected by staircases, escalators or atria.

2.2. Possible fires

Fires were chosen in each case to be as realistic as possible, rather than those leading to worstcase conditions.

2.2.1. In the underground station

In the underground station, none of the fittings or equipment is highly flammable. It was therefore assumed that the main fire source in the public areas could be the suitcase of a passenger, containing clothes of different fabrics. The fire was assumed to occur in the ticket hall, in front of the shops on the unpaid side of the ticket barrier. This location was suggested by fire services. This location also allows the consequences of fire in a shop to be inferred, although it should be noted that no shops were fitted out when the station was visited.

Further details of the fire source are given in Section 3.4.

2.2.2. In the offshore accommodation module

It is reported that 20% of fires in offshore accommodation modules occur in kitchens or laundries (Connolly, 2000). In this work, a mix of 50% cotton-50% polyester linen was

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assumed to burn in the laundry on the first floor. The main interest is in the transport of smoke out of the laundry into corridors and upper levels via the stairwells.

Further details of the fire source are given in Section 3.4.

2.2.3. In the building under construction

Most fires on construction sites happen close to the end of completion, when part of the furniture has already been brought in (Buckland, 2000). An armchair, made of PU foam 23 was therefore assumed to catch fire on the first floor. The main interest here is the transport of smoke to remote upper storeys of the building, via the stairwell and atrium.

Further details of the fire source can be found in Section 3.4.

6

3. ILLUSTRATION OF CFD CAPABILITY: INITIAL MODELLING

This section explains the initial selection of physical and numerical models, and boundary conditions, used to set up the CFD models, a summary of which can be found in Table 1 in Appendix A.

3.1. CFD code

A commercial CFD code, rather than an in-house or academic code, is most likely to be used by a fire safety consultant or industry sector to carry out a fire safety assessment. This is because it is more cost-effective than developing and maintaining an in-house code and academic codes tend not to offer the full range of geometric flexibility required.

The codes known as CFX (formerly FLOW3D), developed and marketed by AEA Technology, were used in all cases. This was for several reasons. AEA technology is possibly the market leader for fire safety applications, having been contracted by HSL to model the Kings Cross Fire as part of the subsequent investigation (Fennel, 1988) and having had wellpublicised success and technical development opportunities as a consequence. The codes embody most, if not all, of the physical and numerical sub-models included in other commercial codes. In addition, AEA Technology is the main code supplier for HSL: we have thus established a strong relationship over several years with AEA Technology and we have gained a significant experience of using their codes for our CFD calculations.

Two codes were used: CFX4 and CFX5. Their main difference is the structure of the mesh, i.e. how the geometry is subdivided into smaller volumes called grid cells. In CFX4, a mesh must be structured: being based on distorted ‘brick-like’ cells grouped in ‘blocks’. With CFX5 an unstructured approach is used, based on tetrahedra. The latter allows the modelling of very complex geometries much more easily and more efficiently.

CFX5 is still under major development. As a consequence, the current version of CFX5 -CFX5.4 - includes fewer physical and numerical models than CFX4, for instance until very recently no combustion model has been available in CFX5. However this does not preclude its use for fire safety applications. Indeed, CFX5 was used to investigate the consequences of a fire in the Millenium Dome (Hiorns and Sinai, 1999). AEA Technology’s long-term objective is to stop developing CFX4. Therefore in the future, state-of-the art models will be implemented in CFX5 solely.

The use of these two codes ensures that many of the different meshing, physical sub-models and numerical techniques embodied in the CFD codes presently available on the market can be represented in this study.

7

3.2. Computational domain and grid

The geometry of the problem, i.e. computational domain, needs to be defined for the CFD code and then subdivided into grid cells. One transport equation for each flow variable is solved at each cell. Hence, even though modern workstations are powerful, CFD calculations still require large computer resources. The computational domain is therefore often a compromise between the complete interior space and a more limited region in which smoke movement is of concern. Where necessary, boundary conditions must be applied to take into account the effect of the flow external to the reduced geometry.

The computational domains for the three real scenarios, and their grids, are described below.


The underground station is on four levels. In this study, however, only two levels were considered: the ticket hall where the fire occurs and the dome at the upper level. This was for reasons of computational economy. The two platform levels below the ticket hall were excluded from the computational domain. Half of the escalators and stairs leading to the platforms were represented though, in order to be able to reproduce reasonably well the natural or forced-ventilation flow that enters the ticket hall from the platforms below.

The model was created in CFX5.4 and thus an unstructured mesh approach was used. The mesh was refined at strategic locations, such as in the vicinity of the fire, where key features may need to be captured. However, to allow small geometrical features to be resolved by the mesh, refinement was also necessary in other areas. As a result, the grid consisted of 93,052 nodes. Figure B.2 in Appendix B illustrates the computational domain and grid for the underground station.


The fire was assumed to occur in the first floor laundry, making it most unlikely that the smoke would be transported to the ground floor. Therefore only floors one to three were represented in the CFD model. Rooms, whose doors were likely to be closed, were also ignored in the computational domain. However, doors leading from the laundry to the corridor on the first floor and those opening onto the stairwells were assumed to be open.

In contrast with the underground station, CFX4.3, based on a structured mesh, was used for this scenario. This was possible because of the reduced complexity of the interior space. The grid density was refined in the fire region. Elsewhere the grid density was near-uniform. A grid of 28,214 cells distributed in 54 blocks was created.


In this scenario the main interest is the transport of smoke from a lower level to a remote upper level via either the atrium or stairwells. The fire was assumed to break out on the third floor. The next three floors are represented in the computational domain, as well as the uppermost floor on the 18th storey and of course the atrium and stairwells. Doors giving access to open-plan offices on one side of the atrium from floors seven to seventeen and on the ground

8

and first floors were assumed to be closed. Hence these areas were not represented in the CFD model.

Figure D.1 in Appendix D shows the whole geometry of the building and the highlighted areas are the parts included in the computational domain. Figures D.2 and D.3, show the computational domain and its subdivision into grid cells. The geometry was simplified somewhat and so the two staircases are assumed to be vertical empty spaces; the stairs are not represented.

Again this is a less complex geometry than the underground station and so the structured mesh approach of CFX4.3 was used. The grid was also refined at the fire location. A total of 155,734 cells and 109 blocks were employed.

3.3. Physical sub-models

Transport of smoke was, in all cases, initially simulated by modelling the fire as a prescribed source of heat and smoke, with an additional passive scalar transport equation solved for movement of the smoke. Compared to using a combustion model - which could be expected to better reproduce the characteristics of the fire source, the use of a prescribed heat source is known to lead to poorer results near the fire but may nevertheless produce reasonable results in the far-field (Ivings, 1999). The use of a prescribed volumetric heat source was the preferred approach here, since smoke movement is being predicted in large spaces and the far-field behaviour is of most interest. In addition, this is the approach most commonly used by fire consultants. Furthermore, it involves solving fewer transport equations than acquired by a combustion model, thus easing computational run-times.

The sensitivity of the results to the modelled fire source for smoke movement in the building under construction are explored in Section 4.3. Initially, the model used to represent the changes in air density was a ‘weakly’ compressible (i.e. Low Mach number) approach, in which air density is assumed independent of pressure fluctuations and air kinetic energy is negligible compared to its internal energy. An equation of state is used to couple temperature, pressure and determine density. However, internal numerical problems in CFX5 alone (see Section 4.1) required the use of a simpler model based on the Boussinesq approximation; the density here being assumed constant except in the gravity term in the momentum equation. This approximation is strictly only valid for temperature variations over a range of a few tens of centigrade. This is in fact likely to be the case in most parts of the computational domain away from the fire. That said, this approach could result in errors in calculated air velocities induced by the fire, which could be propagated elsewhere. However, the adoption of the Boussinesq approximation is again one which is sometimes used by others in this field. This is often as a means of increasing the rate of convergence of a problem and therefore reducing the run-time. The effect of this approximation is explored in Section 4.1.1.

A standard k-e turbulence model, modified to account for buoyancy effects, was employed. The k-e model is certainly the most widely used turbulence model for fire safety applications, although it does have its limitations. Its main advantages are that it is computationally unexpensive and is relatively stable numerically. Its main limitations in the context of smoke movement is that it assumes an isotropic eddy viscosity, which does not account for the

9

non-isotropic effects of buoyancy on turbulent mixing. The buoyancy modification, referred to above, aims, for example, to account for the reduced mixing due to stable stratification, i.e. as exhibited by a hot smoke layer, but it does not by any means represent all of the important effects of buoyancy on turbulent mixing. This issue, and its consequences, is discussed in more details in Section 4.1.2.

The overall approach used here is a compromise of a relatively simple set of physical sub-models, which will allow solutions to be obtained in practical timescales. The approach is in fact widely used by fire safety consultants and industry for predicting smoke movement in complex enclosed spaces.

3.4. Fire source

3.4.1. In the underground station

In the underground station, a fire with a peak heat output of 0.2 MW was estimated from previous HSL work involving tests on a suitcase containing a range of clothes; the peak heat output was determined from the mass loss rate measured by Thyer (1999). The plan area of the fire was set the same as the dimensions of the container (1 m x 1m) in which the suitcase was tested. The height of the flaming region was fixed at 2.5 m, from observations during this experiment. The heat output was increased linearly over one minute in a volume of grid cells corresponding to the above dimensions, i.e. 1 m x 1 m x 2.5 m. After one minute, a constant heat release rate of 0.2 MW was imposed. The production of smoke was deduced from values of smoke yield assuming the products burning consisted of 50% cotton - 50% polyester (The SFPE Handbook of Fire Protection Engineering, 1995). Note however, that the volume over which heat was reduced in the subsequent simulations, since predicted temperatures with the above source prescription were found to be unrealistically low. Section 4.1 provides more details.

3.4.2. In the offshore accommodation module and the building under construction

For both the offshore accommodation module and building under construction, a fire of 1 MW was considered to be a realistic fire size, following discussion with fire safety specialists (Atkinson, 2000). Radiation can be modelled but was not included here since, other than very simple approaches can lead to greatly increased run-times. A fixed percentage of the fire power, based on the combustible materials, was hence assumed to be lost by radiation. This is a common practice, although it is strictly only valid for materials producing small quantities of smoke. Therefore, the fire sources in the models were set at 0.7 MW in the offshore accommodation module and at 0.55 MW in the building under construction; these quantities representing the convective heat output - primarily responsible for the transport of smoke (Table 3-4.11 of the SFPE Handbook of Fire Protection Engineering, 1995).

For the offshore accommodation module, linen made of 50% cotton and 50% polyester were assumed to burn. For the building under construction the combustible was assumed to be an armchair made of PU foam 23. A realistic plan area for each fire was decided. The height was deduced from the fire power and plan area using an empirical relation (Hekestad, 1983). The fixed volume in which heat was distributed was assumed to be a simple parallelepiped. The

10

fire was assumed to grow to its steady heat output following a time-squared growth rate (Rho and Ryou, 1999). As previously, smoke production was deduced from empirical data on the burning materials (Table 3-4.11 of the SFPE Handbook of Fire Protection Engineering, 1995).

3.5. Boundary conditions

The offshore accommodation module and the building under construction were considered completely sealed and therefore no inlet nor outlet boundary conditions were defined.

For the underground station, the ventilation strategy in the event of a fire in the unpaid side of ticket hall is to generate a ventilation flow aiming to clear smoke from the ticket hall and exhaust it via the passenger exits. This ventilation is created by large fans in the Jubilee line tunnels. To model this situation, imposed flow boundary conditions were applied on the surfaces of the computational domain which correspond to connections between the ticket hall and East London line level. These surfaces appear in red in Figure B.2 - Appendix B. Normal velocities were imposed, however their values and area distribution were not easy to determine: the operational conditions of the ventilation fans are known but as the platform levels are not modelled, the distribution of air velocities is unknown. This velocity distribution will depend on the complex geometrical shape of the unmodelled platform levels and on intervening obstacles. The values eventually imposed at these boundaries were deduced from measurements of mass fluxes carried out by LUL. The values were gradually increased from a low velocity of 0.1 m/s representative of normal operating conditions, to a forced ventilation of 0.21 m/s from the Southbound platform and of 0.36 m/s from the Northbound platform. The forced-ventilation was assumed to begin five minutes after ignition - which includes a delay for detection. The full power was assumed to occur after a further minute. At the three exits, fully-developed flow was assumed by imposing pressure boundaries.

At the walls, a no-slip condition was applied. Standard turbulent logarithmic wall functions are used. Adiabatic walls were imposed at the underground station, i.e. no heat flux was allowed, whilst constant ambient temperatures were set for all walls in the offshore accommodation and building under construction. The effect of these assumed conditions is explored in Section 4.1.3.

3.6. Initial conditions

As the offshore accommodation module and the building under construction are assumed to be well sealed, quiescent conditions at ambient temperature were imposed at the start of the calculations.

For the underground station, a small background ventilation flux of 119 kg/s was imposed and used as the initial conditions for the transient fire simulation. The initial temperature was ambient. This small background ventilation flow is assumed to correspond to air movement induced by train movements. Note that setting an initial low ventilation flow also helps the code to converge.

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3.7. Numerical methods

Default first-order numerical schemes are used for the discretisation of convection, apart from the simulation of the offshore accommodation module; where second-order schemes were used for all equations except the pressure equation. Results obtained by first-order schemes will suffer to a greater or lesser degree from the spurious effects of numerical diffusion - a tendency for over-rapid mixing. First-order schemes are, however, often more stable and less costly in computational time than more accurate higher order schemes, hence this approach is commonly encountered in fire safety applications of CFD. It is explored in Section 4.2.2.

The Algebraic Multigrid solver in CFX4 was required for solution of the pressure equation for both the offshore accommodation module and the building under construction. This was made necessary by the complexity of the geometry. Unfortunately it ruled out the possibility of running these simulations in parallel over multiple processors, which would have reduced run-times.

The time steps for the simulations of the offshore accommodation module and building under construction were gradually increased to a maximum of 0.5 second - see details in Appendix A. Smaller time steps of 0.2 second were used for the underground station. Larger time stepsof 1 second were employed after two minutes, once the fire was fully developed, to simulate the movement of smoke up to the start of the forced ventilation. The time steps were then reduced to 0.2 second to ensure convergence of the computed time-dependent forced velocity field. A sensitivity test of the predictions to the value of the time step was undertaken for the offshore accommodation module and the value of 0.5 second was found acceptable. Although ideally a sensitivity test should have been undertaken for the two other scenarios, the values chosen were expected to be adequate on the following grounds: the heat output of the fire in the building under construction was identical to that in the offshore accommodation module and so a similar time step should successfully reproduce the flow induced by the fire; the fire in the underground station was less powerful but influenced by a small background ventilation flow, hence a smaller time step was fixed to capture the interaction between the flow induced by the fire and the ventilation flow. The time step was later increased for reducing the computing time, once the flow was fully developed.

3.8. Convergence criteria

For the underground station, simulated with CFX5, the normalised residuals were decreasing and their values at the end of a time-step were below the criteria advised by the code supplier by a factor of ten. It was difficult to establish if the results were fully converged but this was the best convergence that could be achieved.

CFX4, used for the simulations of the building under construction and the offshore accommodation module, provides non-dimensionalised residuals (actually the sum of the absolute values for all cells). The residuals of the enthalpy equation were normalised by the timedependent heat output. These were found to be typically below 0.002 for the simulations of the building under construction and about 0.1 for the simulations of the offshore accommodation module. The residuals for the mass conservation were compared with the total mass in

12

the computational domains. Once the fire was fully developed the residuals of the mass equation in the building were around 1 kg/s, i.e. 0.5 kg per time step - as the time step was 0.5 s. This was very small compared to the overall mass of air of 110,000 kg. In the offshoreaccommodation module, the mass residuals corresponded to 4.3 kg for a total mass of air of 2,000 kg. The low values of the ratio of the mass residuals to the total mass of air - respectively 10-5 and 2.10-3 for the building under construction and the offshore accommodation module -suggest that mass is being conserved to a good degree of accuracy. Ideally, however, the mass residuals should be compared with a reference flux indicative of the air flow inside the domain - which will be far lower than the total mass of air. Unfortunately, for both scenarios, such a reference flux is not easy to determine a priori.

The residuals of the smoke transport equation were compared with the smoke source term. The ratios of the residuals to the source terms were of the order of 0.005 for both the building under construction and the offshore accommodation module.

In addition, the values of all solved variables were monitored at a point near the fire. The values were found to reach a steady-state level at each time step.

Ideally, the mass and heat balances should be checked globally and in a region including the fire. However, this is in practice difficult for a complex geometry divided into a large number of blocks.

In order to gain more confidence that the results were converged, a sensitivity test to the number of iterations per time step was performed for the building under construction. No significant difference was found in the results by doubling the number of iterations per time step over the first minute.

Although the above checks indicate that the solutions were adequately converged, there might still be some uncertainty: a) there was only limited information on the residuals and overall balances in the version CFX5.3 of the CFD code employed to carry out the simulations of the underground station; b) it was difficult to determine the values characteristic of the flow to compare the residuals against for the scenarios where the fire occurred in a closed building and was driving the flow - scenarios of the building under construction and offshore accommodation module. These situations are, however, typical of the challenges faced by CFD practitioners. The sensitivity tests to numerical parameters such as the number of iterations per time step have been undertaken to increase the confidence that the convergence achieved was good enough not to affect the overall conclusions of this work.

3.9. Results and discussion


The work reported here must certainly be viewed as initial results only, due to a number of shortcomings in the modelling approach - outlined below. Nevertheless, despite these limitations, the approach taken in these simulations is not untypical of the wider CFD fire community. It is therefore instructive to examine these results, both to highlight the capabilities of

13

CFD as a tool for modelling smoke movement and to speculate on the possible consequences of these shortcomings.

Figures B.3 to B.7, Appendix B, illustrate the predicted flow and movement of smoke in the station at various times - before ignition, prior and subsequent to forced emergency ventilation being initiated.

In the five minute period before forced ventilation is initiated, Figures B.4 and B.5 show that smoke is transported throughout most parts of the ticket hall and appears to extend to the main exit routes. In particular, the ‘bridge’ to two of the exits is smoke-logged. It is worth noting that other emergency routes do exist on the platforms that enable the passengers and staff to escape without going through the ticket hall.

Following the start-up of forced ventilation, smoke is cleared from large parts of the paid side of the ticket hall, by being convected towards the exits and into the dome. After one minute, Figure B.7 shows that the bridge is still not quite clear of smoke. However, by one and half minutes after start-up of forced ventilation, the bridge is clear of smoke.

The benefits of CFD simulations of smoke movement around such a complex space are well illustrated by this application: for example, the consequences of delays in starting emergency ventilation, and its effectiveness once operational, can readily be assessed. In principle this can even be done at the design stage, allowing alternative strategies to be examined.

These CFD results are in broad agreement with cold smoke tests conducted at the station. It is encouraging that this is the case. Indeed, the CFD results show the behaviour that would intuitively be expected . However the shortcomings referred to above need to be examined. Firstly, the initial fire source described in Section 3.4 was abandoned because predicted temperatures were unrealistically low. A much reduced fire volume, made of a plan area of 0.5m x 0.5m, and a height of 0.6 m, was instead specified. Predicted temperatures were now far higher, with a peak of just 450oC. Note that unconfined flame temperatures are more typically 600oC700oC. This under-prediction in temperature may in part be a problem due to lack of grid resolution in the fire source region. However the use of a prescribed volumetric heat source to represent a fire is likely to be the prime reason, since to produce a realistic fire source, it involves ad-hoc specification and adjustment of the heat release rate and volume over which heat is liberated. The sensitivity of results to the fire source are addressed in Section 4.

Secondly, the use of the Boussinesq approximation - necessitated due to restrictions within the code, is strictly only valid when temperature differences are small, i.e. of the order of a few tens of Centigrade. That is clearly not the case here. It is difficult to state with certainty the effects of this approximation, but it will affect the calculation of buoyancy-induced flow. The impact of errors is thus most likely to be felt when forced ventilation flows are small, i.e. before start-up of emergency ventilation. Again sensitivity tests are required and these are provided in section 4.

In summary, simulation of this real scenario shows that whilst plausible results can be obtained for certain flow parameters - such as smoke transport, there is still considerable uncertainty in the predicted flows. This uncertainty can however be reduced by sensitivity studies.

14


All the figures related to the accommodation module can be found in Appendix C.

A prescribed volumetric heat source is again used to represent the fire. This is illustrated in Figure C.4. The predicted peak temperatures inside the laundry are high, at just over 1000oC immediately above the fire. In this case, this could conceivably be a broadly realistic temperature, since the laundry is relatively small, at 10 m x 10 m, and ventilated through only two doors.

Analysis of the results shows that initially smoke is confined to the laundry. At approximately 60 seconds after ignition, it makes its way into the adjoining corridor (Figure C.6). By 90 seconds, it has spread to the bottom of the nearest staircase (Figure C.7). It rises nearly half way up this staircase some 30 seconds after entering the stairwell, whilst at the same time it just reaches the other staircase, at the opposite end of the first floor corridor (Figure C.8). It reaches the third floor 180 seconds after ignition (Figure C.9) and it then propagates along it (Figure C.10). Although the third floor has become fully smoke-logged, little smoke has travelled onto the middle second floor dining room (Figure C.11).

Whilst fire doors should prevent such rapid transport of smoke around an accommodation module, they can be left, or wedged, open. CFD clearly illustrates the risks which are then posed by rapid smoke movement.


An isometric view of the floor on which the fire is located is shown in Figure D.3, Appendix D.

The movement of smoke is illustrated by a series of figures, D.4 to D.8, which show the flowfield on a plane through the fire and an iso-surface of smoke concentration, at differing times.

Initially the smoke spreads as a ceiling layer within the third floor open plan office. Figure D.4 shows its progress 30 seconds after ignition. Shortly after one minute, smoke has enteredthe atrium; Figure D.5 shows the position at 80 seconds. By 40 seconds later, smoke has risen a further five storeys, Figure D.6. By 150 seconds after ignition, smoke reaches the upper storeys of the atrium, Figure D.7, however it has only just begun to rise in the open stairwell. At just four minutes after ignition, smoke has found its way from the atrium into the uppermost floor, Figure D.8. By this time, it is also rising in the stairwell.

This simulation illustrates the potential rapid progress of smoke around a building in which fire and smoke protection measures are not yet operational. This could obviously be of significant benefit when assessing and controlling risks during construction. Clearly CFD would be a useful tool in this context.

However, although the simulated movement of smoke appears physically plausible, there are uncertainties in its rate of progress. These arise because the maximum predicted temperature in the fire plume is around 300oC. For a 1MW fire, application of the Mc Caffrey’s plume

15

relationship (McCaffrey, 1979) indicate that a peak temperature of about 900oC. could be expected. Since the simulated flow is entirely driven by the effects of buoyancy, errors in temperature at the source are likely to be reflected in the rate at which smoke is transported. In this case, since temperatures are under-predicted, rates of transport may also be underpredicted. This could mean that smoke would propagate even faster in practice i.e. the simulations may well be non- conservative.

Again the use of a prescribed volumetric heat source is likely to be the prime reason for the unrealistically low temperatures. It is probable that the volume in which heat is introduced is too large; this volume is currently set as a parallelepiped, whilst in reality it more closely approximates a cone.

The need for sensitivity tests, here to the representation of the fire source, is well illustrated by this scenario.

It should be noted that the simulation could not be continued after 260 seconds, since the solution diverged. This is believed to be due to the somewhat unrealistic assumption that the building is completely sealed. A simulation using a fully compressible flow model, rather than a weakly compressible model in which the pressure is assumed constant in the solution of the equation of state, should allow the calculations to be continued, with no significant differences in the predictions. This is a useful point to make: practical difficulties in applying CFD codes may well result in truncated simulations.

3.9.4. Summary

Three fire scenarios, in an underground station, an offshore accommodation module and a building under construction, each of interest to sponsoring HSE divisions, have been modelled using a state-of-the-art CFD code. Each has a complex geometry. All three scenarios provide the opportunity to demonstrate CFD’s capabilities and limitations for the modelling of smoke movement

Initial simulations have here been undertaken to primarily give an indication of CFD’s capabilities. The gross features of the flow certainly appear to be plausibly predicted: a lesswell ventilated area on the bridge between the ticket hall and the exits in the underground station is identified; smoke transport can be tracked along corridors and staircases in an accommodation module; the rapid rise of smoke in the atrium of a building under construction is illustrated. These simulations also appear to provide key information on the effectiveness of forced ventilation on smoke clearance, as well as the generally rapid rate of transport of smoke in complex enclosed spaces.

However, whilst these calculations appear plausible, they are nevertheless subject to considerable uncertainty due to shortcomings in the representation of the fire source, use of simplified physical sub-models, assumed boundary conditions and unquantified numerical errors. The approach taken in modelling these scenarios is, however, typical of that employed by others active in this field, and the predictions are similar to those that would be likely to be presented should CFD have been used in support of a safety assessment for these situations.

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In the absence of experimental data, it is thus imperative that sensitivity tests be undertaken to attempt to reduce some of the uncertainty in these, and similar, CFD simulations.

Ultimately, however, comparison with experimental data is essential to quantify the capabilities of CFD. The physics involved in the development of a fire and production of smoke is complex. Key areas of concern, i.e. those of importance for smoke transport and for which CFD’s capabilities are still uncertain are:

Readers are invited to consult the HSL report on benchmark experiments and CFD modelling (Ledin et al., 2002) which aims to elucidate the capability of CFD to reproduce the key physics of the flow; the complexity of which is increased, in part, by the geometrical features of the spaces in which a fire occurs. The benchmark experiments were designed to retain those aspects of the geometry that led to complex physics. They comprised:

� influence of a ventilation flow;

� transport along corridors and in areas with large floor plan ;

� transport in vertical spaces, such as escalator shafts, stairwells or atria;

� transport between interconnected large open spaces, or large open spaces and corridors: either large horizontal spaces, as per the ticket hall of an underground station, or large vertical spaces such as building atria.

The next section investigates the effect that different CFD modelling approaches have on the prediction of smoke movement for the three scenarios.

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4. COMPARISON OF DIFFERENT MODELLING APPROACHES

When using a CFD package to create a CFD model, the user has to select physical and numerical sub-models, ways of defining the source, mesh sizes, locations of the boundaries, etc... In some cases, the user is forced to make compromises: for instance because of the contradictory requirements of the different sub-models implemented or because of practical limitations on resources available. The latter situation is very likely to be the case for simulations of smoke transport in large and complex spaces, which are demanding of computer resources. Simple mathematical models, or relatively coarse grids, for example, might have to be employed in order to achieve reasonable run-times. The objective of this part of the work is to report the extent to which the CFD results are influenced by the parameters chosen by the CFD practitioners i.e. the sensitivity of simulations.

A range of CFD modelling approaches, representative of those commonly used by many consultants, have therefore been employed to simulate the three real scenarios. These include different physical models, numerical schemes and fire source representations, as summarised in Table 1. More advanced models, which embody more details of the physics of the flow, do exist. However, they are more demanding in computer resources and therefore are less likely to be applied to smoke movement scenarios in large and complex spaces, certainly as presented to HSE in safety cases.

Table 1 - The different modelling approaches employed Scenarios

CFD Underground station Offshore module Building under construction

parameters Initial grid

Grid dependence

Vs Grid cells doubled on

floor where fire is located

First-order accurate

Spatial discretisation schemes

Num

eric

al p

aram

eter

s

(upwind) Vs

Higher order accurate (CCCT)

Flow compressibility

Boussinesq approximation Vs

Compressible k- e model:

Turbulence

Phy

sica

l mod

ellin

g

C3=0 modelling Vs

C3=1 Volume heat source model

(no combustion) Fire modelling Vs

Eddy-break-up combustion model

Wall heat transfer

Bou

ndar

y co

ndit

ions

Adiabatic wall Vs

Wall at ambient temperature

Adiabatic wall Vs

Wall at ambient temperature

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The temperatures, velocities and transported smoke concentrations predicted by the different modelling approaches. This provides an insight into the sensitivity of the results and consequently indicated where there is uncertainty.

4.1. Underground station

Four simulations have been carried out using CFX5.4.1 for the scenario of the underground station. One modelling parameter has been changed from one simulation to another, as outlined in Table 2 below.

Table 2 - The different simulations for the underground station scenario

Initial Run 2 Run 3 Run 4 Compressibility Boussinesq Compressible Compressible Compressible

approximation model model model Buoyancy effect in the k-e model

C3=0 C3=0 C3=1 C3=1

Heat transfer Adiabatic wall Adiabatic wall Adiabatic wall Wall at ambient wall boundary temperature

conditions

This allowed us to investigate the influence of the following parameters:

� Boussinesq approximation compared to a compressible flow model (by comparing simulations ‘Initial’ and ‘Run 2’);

� buoyancy modification term in the k-e turbulence model (by comparing simulations ‘Run 2’ and ‘Run 3’)

� heat transfer at the walls (by comparing simulations ‘Run 3’ and ‘Run 4’).

4.1.1. Flow compressibility

The Boussinesq approximation assumes a constant density and takes into account the air movement due to thermal effects by an additional term in the momentum equations. Details of its implementation in CFX5 are given in Appendix H. This approach is valid for small temperature gradients and is typically recommended in CFX-5 when temperature variations do not exceed 30oC. Despite this limitation, we are aware that this model is sometimes used to predict the transport of smoke from a fire in large volume spaces. The argument presented to us is that the model assumption will be valid in most parts of the domain with the exception

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of the vicinity of the fire and that the difference in the end result will therefore be small. The benefit is the relatively modest need in computing resources.

The fire plume temperatures predicted by the compressible flow model are 95 K higher than those calculated using the Boussinesq approximation model. They are still only about half of the theoretical value of at least 1000 K. - based on empirical correlations for unconfined flames. Even so, air density goes down to 0.4 kg/m3 with the compressible model. However, the region where the density is below 1.0 kg/m3, and for which the Boussinesq approximation is not recommended is limited to the vicinity of the fire. One may therefore initially expect the two simulations to be very similar, and the argument in the preceding paragraph validated. However this is not the case.

The buoyancy term added in the momentum equations by the Boussinesq approximation model is not sufficient to correctly account for the convection of such hot, and light, air. As a result, the vertical velocity induced by the fire plume is underestimated. It is believed that the relatively low momentum and somewhat less buoyant, plume is then more strongly influenced by the background ventilation: the plume is transported further away from the fire before it impinges on the ceiling and the resulting lateral velocities after impingement are also more influenced by the local background ventilation. The geometry of the station may also emphasise the differences in the predicted smoke transport as it generates a flow with rapid variations in space. As a consequence, a small difference in the prediction of the fire plume can lead to a significant difference in the transport of smoke.

Figures E.2 to E.4 show the smoke concentration close to the walls for the two models 60, 90 and 115 seconds after ignition. Up until 60 seconds, i.e. During the period of fire growth of the fire, the contours appear to look similar. However, at later times the smoke is propagating more rapidly across the bridge in the case of the Boussinesq approximation. This is confirmed by the smoke fluxes evaluated across vertical planes at mid-span and at the end of the bridge (see Figure E.5). Comparison with fluxes evaluated across planes along the corridor leading to exit 2 shows that a larger amount of smoke will be transported through exit 2 than over the bridge (see Figure E.6) .

Interestingly, and in contrast, there is no significant difference between these two modelling approaches in the transport of smoke along the corridor towards the exit 2. This is almost certainly due to the specific geometry of the station.

4.1.2. Buoyancy effects in the k-e turbulence model

The weakly compressible model was adopted. A sensitivity test to the turbulence modelling was undertaken, in particular to modifications to the k and e turbulence equations to account for the gross effects of buoyancy on turbulent mixing. Thus two different values of the constant C3 - a multiplying factor of the buoyancy production term in the e equation - were tested: 0 and 1 (respectively Run 2 and Run3).

The specific form of the k-e turbulence model in CFX5 can be found in Appendix H.

To summarise, the characteristics of the models as implemented in CFX codes are as follows:

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� Both C3=0 and C3=1 include a buoyancy production term in the k equation;

� C3=0 corresponds to no buoyancy production term in the e equation;

� C3=1 corresponds to adding a buoyancy source term in the e equation in the case of unstable density gradients i.e. as found in a fire plume. Its aim is to counteract the increase of turbulent kinetic energy caused by the buoyancy source term in the k equation. The term is neglected in CFX in the case of stable density gradients (attributed to Viollet et al., 1983) i.e. horizontal smoke flow under a ceiling.

In previous studies, this buoyancy-modified version of the k-e model was found to give good results for stable stratified flows and for vertical buoyant jet flows in calm surroundings. However, even with C3=1, for unstable density gradients, it still overestimates the turbulent mixing due to buoyancy effects (Viollet et al., 1983).

In fact, several ways of accounting for buoyancy effects in a k-e turbulence model have been proposed all based essentially on Rodi (1979). They all include a multiplying factor C3 in the e transport equation but it is important to note that this factor has different interpretations. Therefore the same value of C3 does not necessarily mean the same equations are being solved. However, for the special case of C3=1, it so happens that the implementation in CFX attributed to Viollet (1983), then becomes the same as that of Markatos et al. (1982), whose work is based on Rodi’s 1979 proposal.

For the present scenario, the temperature distributions near the fire (see Figure E.1) are very different for the two values of C3. When C3=1, the temperature reaches a more realistic value of 860 K, compared to a maximum of just 560 K when no buoyancy term is added in the e

equation. An unstable density gradient zone develops in the rising plume and it is believed that the turbulent mixing in this region is overestimated, this to a far greater extent when C3=0. The background ventilation, by curving the plume, might lead to stronger density gradients and hence further increase the over-estimate of the turbulent mixing. As a result, the hot air in the plume is mixed more effectively with the cooler ambient air and this reduces the plume temperature. This under-prediction in temperature is particularly pronounced when C3=0. It might not be solely due to the shortcomings of the models to take into account buoyancy effects on the turbulent mixing. Hence, the coarse nature of the grid might also play a role in the under-prediction in temperature.

There is then, unsurprisingly, a significant difference in the transport of smoke predicted by the two values of C3. This can be seen 60 seconds after ignition (Figure E.2), where smoke has already been convected along the bridge with C3=1, whilst it just enters the bridge with C3=0. The smoke fluxes also show that a value of 1 for C3 will favour the bridge as a route for smoke, at the expense of exit 2. Importantly the smoke propagates twice as fast over the bridge with C3=1 than with C3=0. What appears to be a minor change to just one of the turbulence model parameters thus has a profound effect. With C3=1, the buoyancy-induced horizontal flow is more important, thus propagating the smoke more rapidly inside the whole domain. If the smoke is more dispersed laterally, it is however less dispersed vertically. This

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is due to the more pronounced stratification of the flow, believed to be mainly the result of the higher temperatures predicted by C3=1.

In summary, inclusion of a buoyancy term in the k equation alone is known to adequately reproduce the gross effects of buoyancy on turbulent mixing for stable density gradients (Launder, 1975; Rodi, 1979; Markatos et al., 1982). Previous work based on Rodi’s 1979 proposal has also shown that a further term must be embedded in the e equation if unstable density gradients - as found in a fire plume, are to be adequately modelled (Markatos et al., 1982; Viollet et al., 1983). In the present study, such a model has been applied to a scenario where the flow was stably-stratified in most parts of the domain, except of course in the region of the fire. It was found that introducing this additional term in the e equation, achieved by setting C3=1 in the CFX codes, had a significant effect on the temperature predicted in the plume: as a consequence, transport of smoke in the whole domain was predicted to be much more rapid.

4.1.3. Heat transfer at the walls

The two extreme situations of no heat transfer at the walls (adiabatic walls) and complete loss of heat (walls fixed at ambient temperature) have been investigated. The aim is to examine the maximum sensitivity of the predictions to the wall heat transfer as modelled by CFD. The heat transfer close to walls is in fact not fully resolved by CFD since this would require too many computing resources. Instead, prescribed wall functions are applied. These implicitly correspond to considering only the effects of forced convection and neglecting the effects of buoyancy and natural convection to heated surfaces. Although these wall functions do not fully describe the physics of heat transfer, they are widely used. In this section, their sensitivity to the heat transfer value is examined.

The transport of smoke is hardly affected by the different wall heat transfer conditions, as can be seen both from the smoke concentration at the walls (Figures E.2 to E.4) and from the smoke fluxes along the bridge and through exit 2 (Figures E.5 and E.6).

In Figure E.1, which shows the temperature distributions close to the fire source, the temperature at the ceiling is effectively at ambient temperature and the temperature of the hot layer is slightly cooled down by the cold wall but it does not seem to affect the layer depth.

This finding is not in agreement with some other work in this field, for instance Ivings (1999) who simulated a 0.3 MW fire in a 2.4 m x 3.6 m x 2.57 m room, and found a marked sensitivity to the boundary condition for wall heat transfer. The reasons for the lack of sensitivity in the present scenario are likely to be that the fire heat output is lower, thus producing a cooler hot layer that will exchange less heat at the walls; the fire is located in a much larger space thus encountering a smaller wall surface area than an enclosed fire, again limiting the loss of heat at the walls; a background ventilation flow will exchange heat, mass and momentum with the hot layer; this could also be due to a poor representation of the boundary layer close to the ceiling by the turbulent wall functions because of the requirement on the size of the grid cells could not be met.

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To gain further insight into the sensitivity of smoke transport to wall heat transfer, it was decided to investigate the effect of heat transfer at the walls for another scenario, the offshore accommodation module where a larger fire occurs in a room.

4.2. Offshore accommodation module

The sensitivity to two different numerical modelling refinements were tested for this offshore accommodation module scenario:

� the size of the computational grid;

� the discretisation scheme employed for the convection terms in the governing transport equations;

In addition the effects of the heat transfer boundary conditions at the walls was also investigated, as per the underground station scenario.

Table 3 summarises the different modelling approaches investigated for the offshore accommodation module.

Table 3 - The different simulations for the offshore accommodation module.

Initial Run 2 Run 3 Run 4 Grid Coarse Coarse Fine Coarse Discretisation CCCT Hybrid CCCT CCCT scheme (second order) (first order) (second order) (second order) Wall heat transfer

Fixed temperature

Fixed temperature

Fixed temperature

Adiabatic

4.2.1. Grid size

A fine mesh, in which each cell edge was divided by two on the first floor and in the stairwells, was employed in Run 3 (see Figure F.1). The average dimensions of the grid in the laundry - in fire source is located, are given in Table 4. The overall number of grid cells increases from a relatively coarse 28,214 for the initial simulation, to 149,934. For this finer grid case.

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Table 4 - Comparison of grid cell dimensions in the laundry

Direction Room dimensions

Number of grid cells Average grid cell dimension

Coarse Fine Coarse |Fine X 10.2 m 25 50 41 cm 20.5 cm Y 5.4 m 14 28 38 cm 19 cm Z (height) 3.2 m 11 22 29 cm 14.5 cm

Overall, examining Run 3, the same gross trends are seen in the transport of smoke as with the initial coarser grid. In the laundry, a hot gas layer develops and is directed downwards to the ground after impinging on the side wall (see Figure F.2 - Initial Vs Run 3). The hot gas layer behaviour - in particular its depth, overshoot against the side walls, and temperature- is similar to that obtained with the coarse grid (see Figure F.3).

There is, however, more flow detail predicted by the fine grid: for instance, eddies are clearly seen on both sides of the door near the stairwell at 0.45 metres above the ground (Figure F.5). See also Figure F.6. They are not apparent with the coarse grid predictions although a similar air entrainment into the room is predicted (see Figure F.8). As expected, the coarser grid tends to smooth the flow details and gradients in flow variables, in effect averaging flow variables over a larger volume.

Overall, there is no significant difference in the prediction of smoke propagation - see Figures F.7 to F.9. The fact that grossly similar flow behaviour is predicted by both grids increases confidence that they are both of a resolution adequate for predicting the main features of the flow. Consistent predictions of smoke movement - distribution and propagation speed- are obtained, although in lesser detail with a coarser grid.

The CFD user needs to have a good understanding of the flow behaviour when constructing the grid, in order to adapt its size to the flow phenomena to be resolved. Commercial CFD codes offer the possibility to refine the grid at specific locations within the computational domain. This flexibility enables the user to limit the number of grid cells, thus the run-time, without significantly compromising the accuracy of the results. In practice, it can prove difficult, prior to any simulation, to determine the appropriate size of the grid and to identify the regions that need refinement. Ideally, the CFD practitioner should undertake a series of simulations refining the grid until there are no significant differences in flow predictions. Unless such checks are undertaken it is not possible to be sure of the extent to which the CFD grid is determining the end result. This, however, is time-consuming process and often not practical. In this work, for instance, a grid refinement test was undertaken only for the present scenario. Ideally, a similar check should have been carried out for the underground station and for the building under construction. In such circumstances, a limited grid refinement check may be all that can be managed but with resulting uncertainty in the results.

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4.2.2. Discretisation scheme

Commercial CFD codes generally allow the user to choose a discretisation scheme for the convection term of each transport equation solved. The scheme provides discrete approximation for the convection term based on the values of the surrounding flow variables. For example, perhaps the simplest scheme consists of an extrapolation from upwind values. A wide range of schemes, of varying orders, do exist. The order of a scheme corresponds to the rate at which the error tends to zero as the grid is refined. For example, a simple upwind scheme is a first order scheme. The higher the order of the scheme, the more accurate the representation of flow convection should be, providing that an adequate grid resolution is employed. First order schemes are prone to what is called numerical diffusion. This corresponds to an error purely caused by the numerical discretisation and the effect of which is equivalent to spuriously increase rates of diffusion. Higher order schemes are less sensitive to this effect but they tend to be less stable, therefore more difficult to apply successfully.

Here, two schemes are compared: a first-order hybrid scheme - which can be expected to default to upwind at most locations- (Run2) and a second order CCCT - Curvature Compensated Convective Transport - scheme (Initial run). Mathematical details of these schemes can be found for example in Versteeg and Malalasekera (1995).

Comparing the initial run to Run 2, temperature distributions across the laundry in Figures F.2, F.5 and F.6 show a much less distinct hot layer in the case of the first order scheme. In essence, the hot air from the fire has been subject to increased, spurious mixing with the ambient air as a result of numerical diffusion. As a consequence, the vertical profile of excess temperature near the fire source is almost uniform except very close to the ceiling. The excess temperature profiles at the doors are however similar, certainly due to the supply of fresh air at low level from the corridors.

This results in a more efficient mixing of smoke with air with a first order scheme, leading to a more uniform distribution of smoke. The lateral propagation of smoke is less rapid, since contrary to the second order scheme the smoke is not concentrated in the hot layer where the velocities are higher.

4.2.3. Heat transfer at the walls

As for the underground station, two different boundary conditions for heat transfer were applied at the walls: adiabatic (i.e. no heat transfer: the walls will be at the temperature raised by the fire) and a fixed temperature imposed at the walls (i.e. maximum heat transfer: all the heat produced by the fire would ultimately be lost at the walls). In reality, walls will have a thermal behaviour between these two extreme situations that will depend on the materials they are made of and the coatings they are covered with. It is technically possible to define boundary conditions that will match experimental data on the transfer of heat at the walls (see for example Ivings, 1999), when such data is available. Here, since all the possible heat transfer conditions could not be investigated, it was decided to investigate these two extremes. The wall boundary conditions applied in both cases remain, however, a crude representation of the heat transfer since only the convective transfer is taken into account.

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Comparing Run 4 to the initial run, higher temperatures are unsurprisingly reached in the hot gas layer with adiabatic walls (see Figure F.2). Indeed, since no heat is lost at the walls, all the heat released by the fire is transferred to the surrounding air. There is a difference of about 50oC above the fire after two minutes (see Figure F.3) as well as in the hot air leaving through the door closest to the fire (see Figure F.7). The difference is even more significant, reaching nearly 100oC, at the door to the stairwells (see Figure F.8).

The mixing between the hot plume and the ambient air is less important, evidenced the horizontal temperature contours across the room in Figure F.2 and the well stratified excess temperature profiles in Figures F.3, F.4, F.7, F.8. The mixing principally occurs where the hot layer impinges on the side walls and is directed downwards along these, towards the cool air. In the case of walls at ambient temperature, the presence of cool air above the hot layer creates an instability that increases the mixing. As a result the flow is less stratified in the room.

Due to the absence of mixing between hot and cool air with adiabatic walls, the smoke generated by the fire raises with the hot air and remains concentrated in the hot layer close to the ceiling. The velocities induced by the fire are equivalent for both wall conditions: velocities of the plume as well as the horizontal convective velocities of the hot layers along the ceiling and at the doors (see Figures F.4, F.7 and F.8). However, since with adiabatic walls, smoke is principally in the hot layer where the velocities are higher, it is transported more rapidly inside the module as can be seen in Figure F.9. It is as well clear from the smoke iso-surfaces of Figure F.9 that the smoke remains closer to the ceiling, potentially at a lower risk for the public.

It is interesting to note in Figure F.2 the continuity of the temperature contours across the laundry and corridor in the case of adiabatic walls. These are due to the unphysical representation of the walls: in the CFD model, they have no thickness. That means that both sides of the wall have the same temperature. Therefore, the air in the corridor is heated first by diffusion from the laundry wall and not by convection of hot air through the door as in the case of walls fixed at ambient temperature.

For the underground station, a similar test was undertaken but no difference in the CFD predictions was found. This lack of sensitivity of the results to the heat transfer boundary condition at the walls could be because the fire heat output was lower for the underground station scenario, thus producing a cooler hot layer that exchanged less heat at the walls; the fire was located in a more open area - thus encountering a smaller wall surface area than an enclosed fire, limiting the loss of heat at the walls; a background ventilation exchanged heat, mass and momentum with the hot layer.

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4.3. Building under construction

The simulations carried out for the building under construction are presented in Table 5.

This allowed us to investigate the effects of :

� The shape of the prescribed volume in the volumetric heat source model by comparing a parallelepedic volume (‘Initial’) and an approximately conic volume (‘Run2’)

� The prescribed fire growth curve by comparing a time-squared (‘Run 2’) and a constant (‘Run 3’) heat release rate;

� The fire modelling approach: By comparing a prescribed volumetric heat source model (‘Initial’, ‘Run 2’ and ‘3’) and an eddy combustion model (‘Run 4’).

Table 5 - The different simulations for the building scenario

Initial Run 2 Run 3 Run 4 Model Volumetric heat Volumetric heat Volumetric Eddy Break-Up

source source heat source Combustion Fire source volume

Parallelepiped Base=1x1=1 m2

Cone Diameter=1.2m.

Cone Identical to

Parallelepiped Base=1x1=1 m2

Height=2.2 m Volume=2.2 m3

Height=2.1 m. Volume=0.74 m3

‘Run 2’ Height= 0.2 m. (one grid cell high) Area = 1 m2

Number of grid cells in the source

128 (=4x4x8)

52 52 16

(=4x4)

Convective heat output H(t)=a t2 , t<109s H(t)=a t2 , t<109s H=0.55 MW H(t)=a t2 , t<109s

H=0.55 MW, t>109s H=0.55 MW, t>109s H=0.55 MW, t>109s

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4.3.1. Shape of the prescribed heat source

Two different shapes, as shown in Figure 1, were tested.

Parallelepiped

Volume = 2.2 m3

Width= 1 m. Height=2.2 m.

Number of cells =128

Cone

Radius= 0.58 m.Height=2.1 m.Theoretical volume = 0.7394 m3

Actual volume = 0.7378 m3

Number of cells= 52

Figure 1 - The different shapes of the fire source in the volumetric heat source models (black lines: parallelepiped; red volume: cone)

Figure G.1 presents the temperature distribution in a plane across the fire: temperatures are higher in the cone (Run 2) than in the parallelepiped (Initial Run) but appear to be rapidly similar away from the fire. Values of predicted temperatures along the centreline of the fire are plotted in Figure G.2 and peak temperatures are given in Table 6. The difference in temperature is due to the larger volume of the parallelepiped in which the heat is released: the parallelepiped is nearly three times as big as the cone. The temperatures from the conic source are more realistic, though still somewhat underestimated.

Table 6 - Peak temperatures for the different shapes of the volumetric heat source with 0.55 MW power output.

McCaffrey Volume = Volume = cone

relation parallelepiped 900 ºC 300 ºC 750 ºC

Further away from the source, a hot air layer develops along the ceiling and, for both volume sources, its temperature and depth are similar (see Figures G.3 and G.4). The depth is slightly greater on the side of the fire opposite to the atrium: this is believed to be due to the presence of walls further away which constrain and deepen the hot layer.

The smoke accumulates in this hot layer and the levels reached are similar for both models. Figure G.5 shows that this behaviour can be observed at a given height close to the ceiling

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along the length of the floor. A near identical lateral dispersion of the smoke on the third floor is also predicted by both models, as can be seen in Figure G.6.

In the atrium, a similar distribution of smoke is also observed (Figure G.7). Table 7, which compares the arrival time of smoke predicted at several heights in the atrium, shows that the propagation of smoke in the atrium is similar for both models.

Table 7 - Estimated arrival time of smoke at different heights in the atrium (to within 10 seconds)

Level reached by the smoke (number of floors above the fire)

Initial Run 2 Run 3 Run 4

two floors 90 s. 90 s. 40 s. 90 s. five floors 120 s. 120 s. 50 s. 120 s.

sixteen floors (top of the atrium)

170 s. 170 s. 100 s. 170 s.

In conclusion, the two models present a marked difference very close to the fire source. There is, however, no significant difference in the predicted movement of smoke at locations more remote from the source.

The smaller volume of the cone compared to the parallelepiped explains the higher temperatures predicted near the fire. Intuitively the smoke would subsequently be expected to be transported more rapidly by higher velocities but this is not the case: these high velocities near the source might lead to increased mixing which ultimately lowers the temperatures and velocities further away from the source.

It is also possible that the first order discretisation scheme (that adds a spurious numerical diffusion) and that the heat transfer boundary condition at the walls (that will cool down the hot layer at the ceiling to an imposed temperature identical in both models) contribute to increasing the similarities between the two source prescriptions.

4.3.2. Fire growth curve

Two different heat release rates were tested: a constant and a t-squared fire over a period of 109 seconds. Both heat release rates are represented in Figure 2. The smoke production rate is proportional to the heat release rate and so will have the same profile.

Figures G.1 and G.2 show that the temperatures reached in the fire plume 150 seconds after ignition are identical. The vertical profiles of excess temperature and smoke concentration 10 metres away from the fire (Figure G.3) are near identical on the side nearest the atrium, but on the other side the hot layer is deeper and has a higher temperature when the heat output is constant. The distribution of smoke on the third floor (Figure G.6) and in the atrium (Figure G.7) show a more rapid propagation in the case of a constant heat output source: smoke has already filled the atrium at just 150 seconds after ignition whilst with a t-squared fire it has only reached a 2/3 of the height of the atrium.

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0.0

0.1

0.2

0.3

0.4

0.5

0.6

Hea

t out

put (

MW

)

0 50 100 150 200

Time (seconds)

Figure 2 - Heat release rates for the building scenario

These differences are due to the fire growth phase, during which temperatures, and thus velocities, are higher with a constant heat output. Therefore the convection of both temperature and smoke will be more rapid; hence the different distributions on the third floor and in the atrium. This also explains the different vertical profiles on the third floor between the two models: the constant heat output source will predict a deeper hot layer since the hot air will have reached the walls before the t-squared fire and will have accumulated over a longer period.

Table 6 shows that smoke starts rising in the atrium just 40 seconds with a constant heat output whilst it takes 90 seconds with a t-squared fire. This means that there will be a time lag of about one minute between the two models only one and a half minutes after ignition.

The fire growth rate that needs to be prescribed in any CFD model can have a profound effect on rates of smoke transport. A constant heat output was found to lead to conservative predictions.

4.3.3. Fire modelling approaches

The previous approach for introducing the fire source was compared with an eddy-break-up combustion model, whereby the region in which the heat is released does not need to be prescribed, but is instead predicted by the model. There is still however the need to specify the fire growth curve: the t-squared curve in Figure 2 was applied. The smoke concentration was deduced from the mass fraction of combustion products and a constant smoke yield factor.

The excess temperature predicted by the eddy-break-up combustion model is between the temperatures predicted by a conic and parallelepipedic volume source. Note that, in the combustion model, the temperature at the floor underneath the fire was set at a value to reflect that needed to pyrolyse the fuel. This explains the difference in excess temperature between the models at ground level. Importantly, however, the temperature predicted with this

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application of an eddy-break-up model is still under-estimated. The peak temperature is just 440oC, compared to 750oC for the prescribed conic volume and an expected 900oC .

Smoke seems to be produced in a larger quantity by applying an eddy break-up combustion model. This is believed to be due to the slightly different way smoke was treated in the two models: in the combustion model, an equation was solved for a smoke mass fraction which was later transformed into a concentration by multiplying it by density. In the volumetric heat source model, it is smoke concentration that was solved directly. The smoke concentration profile is also smoother near the ground - Figure G.2, which is certainly to be due to the higher wall temperature at the source.

Away from the fire, the differences between the models rapidly diminish. A hot layer of essentially the same temperature and depth is predicted with both a combustion model or a volumetric heat source model - irrespective of the shape of the volume. Smoke is more concentrated in this layer with the combustion model, but this is purely due to the higher production of smoke at the source. The rates of propagation of smoke on the third floor and in the atrium also look similar (Figures G.6 and G.7).

Xue et al. (2001) have compared different fire modelling approaches - that included a volumetric heat source and an eddy break-up model - in three configurations: a room, a shopping mall and a tunnel. The relative performance of the different approaches was evaluated by comparing the predictions against experimental data. However, no consistent performance of any one model was found: one model was found to be better for one situation but gave poor results in another scenario. The shopping mall simulation was the most similar to the building considered in this work, yet it had a much smaller volume. Its simulation was two-dimensional although the authors recognised that the mechanisms involved were likely to be three-dimensional. All modelling approaches were found to agree well with measured temperatures.

In the present case, it is not certain why use of an eddy break-up combustion model has led to predicted temperatures which are far lower than expected. It could be a consequence of a relatively coarse grid resolution in the fire source region; just 4 x 4 cells were used to introduce the fuel. It could be the use of a diffuse, first-order accurate , convection scheme - but in that case it could be expected that all modelling approaches would be similarly affected unless there is a more marked sensitivity to convection discretisation in the modelling of transport equations for fuel and product mass fractions. It may be that the turbulent equations for fuel and product mass fractions. It may be that the turbulent time-scale - which the eddy break-up model uses to compute the rate of reaction, is in error. However, in the absence of experimental data, there is no easy way to check this. It is interesting that other applications of the eddy break-up combustion model at HSL (Ivings, 1999; Ledin et al., 2002) report temperatures which tend to be too high.

The eddy break-up model is actually a very crude combustion model. It also still relies on a certain amount of user input; area/volume into which fuel vapour is introduced, rate of fuel vapour supply so as to match a prescribed heat release rate. In common with other combustion models, its performance relies heavily on the ability of the turbulence model to return realistic predictions.

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Nevertheless the eddy break-up combustion model can give realistic predictions (Kumar and Cox, 2001). In particular, its ability to predict the region over which heat is released is an important feature. This is particularly where circumstances could result in a tilted fire plume or one affected by the presence of nearby surfaces. In these cases it would be difficult to set up and check the adequacy of a prescribed heat source approach. Use of a combustion model then has distinct advantages. In the present scenario the fire plume rises eventually undisturbed (see Figure G.1), since the source is located in a large open-plan office. Hence in these circumstances it could perhaps be expected that a prescribed volumetric heat source approach would be adequate.

What is clear, however, is that irrespective of whether a combustion model or a prescribed volumetric heat source is used to represent the fire source, the resulting properties of the source should be checked (Kumar and Cox, 2001). It also does appear to be the case in the present scenario that other factors in the prescription of the fire source can be more significant, i.e. fire growth rate.

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5. CONCLUSIONS AND RECOMMENDATIONS

5.1. Conclusions

CFD has been successfully applied to three real scenarios of interest to HSE sponsoring divisions: an underground station, a building under construction and an offshore accommodation module. The three-dimensional nature of the technique meant that it was possible to closely represent the complex geometries, and to predict the temperature, air velocities and smoke concentration at a large number of points inside the domain. This, therefore, resulted in predictions of spatial and temporal variations of the flow caused by the presence of the fire taking into account the complex shapes of the geometries: a large plan area hall surmounted by a dome; building with multiple floors connected by an atrium and/or stairwells, etc... In each scenario investigated the transport of smoke predicted by CFD looked realistic and could, in principle, he used as a basis of a fire safety engineered approach.

However, when creating a model, the CFD practitioner has to make assumptions and select ‘appropriate’ parameters. The choice depends on the scenario modelled, as well as on the expertise of the user. As a result, various CFD modelling methods are being used amongst the Fire Safety Engineering community to predict the transport of smoke. In this study, a range of modelling approaches typically employed was applied, and results compared to an initial set of simulations. This demonstrated that, in some circumstances, the parameters chosen can have a significant influence on the predicted rate of smoke propagation and its distribution.

The sensitivity of the results to the following CFD parameters was investigated:

� numerical parameters: grid resolution and convection discretisation scheme;

� physical parameters: compressibility of the flow, inclusion of buoyancy effects in the k-e turbulence model, volumetric heat source model Vs an eddy break-up combustion model;

� boundary conditions of heat transfer at the walls.

It was not practical to undertake all of these sensitivity tests for all three scenarios. Where there may, as a consequence, be bias in the resulting predictions, this is indicated below. In any case, general lessons learnt are stated below, rather than conclusions specific to a particular scenario.

In examining the effect of grid resolution, two very different mesh sizes were employed for the offshore accommodation module. Both grids led to similar results. This indicates that the results are here relatively insensitive to the level of grid resolution employed. However, in general, if the grid is too coarse compared to the length scales of the key flow phenomena, it could easily result in the generation of misleading results. Unfortunately, it is quite often difficult to foresee what may be an appropriate resolution for the mesh. Ideally, the grid would be refined until no significant difference is found. This process is, however, time-consuming, often impractical and therefore usually neglected. Thus even in the present study, such a test was carried out only for one simulation, whilst ideally a similar test should have been undertaken for all three scenarios. Unless such a check is undertaken, or has already been

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done for a closely similar flow and geometry, the results will be uncertain to an unquantifiable degree.

The use of a high order convection discretisation scheme for the accommodation module resulted in the prediction of more flow detail and a more rapid rate of smoke spread. It is therefore recommended that first-order accurate schemes are not used - or if they are, that the resulting error is shown to be conservative.

The use of the Boussinesq approximation to account for the thermal effects on flow compressibility, as opposed to calculating the air density from an equation of state, is appealing since it reduces the computing time. This is an especially attractive feature when applying CFD to complex and large spaces. This approximation, sometimes used on the grounds that compressibility effects will be limited to a small portion of the whole computational domain i.e. in the vicinity of the fire, has however here been shown to ultimately result in significant differences in the transport of smoke compared to a more soundly-based approach which solves an equation of state for the density. Again, unless otherwise shown to be conservative, this approach should not be used for modelling smoke movement from flaming fires.

As regard to the effects of buoyancy on turbulence, it was found important that, in the case of unstable density gradients, an additional buoyancy-related production term be included in the e equation. The conclusion is consistent with previous work (Cox, 1995) and recent advice (Kumar and Cox, 2001). When this term was omitted, the simulated fire plume for the underground station was more strongly affected by a background ventilation flow.

The effect of differing prescribed heat source volume was investigated for the building under construction. The choice of the shape of the volume led to significant differences in predicted source temperature. If this approach to the modelling of the fire source is taken, the volume should therefore be prescribed such that it leads to representative temperatures. Nevertheless, for this scenario, temperatures became similar for the two prescriptions as the distance from the source increased. This may be specific to this scenario. The choice of a fire growth curve was also investigated for the building under construction. This led to significant differences during the growth period and these had an impact on smoke movement at later times. If no experimental information is available, constant fire heat output at a credible maximum value should be a conservative assumption in most cases.

Two methods of modelling the fire source were also investigated for the building under construction; an eddy-break-up combustion model and a prescribed volumetric heat source. In this case the combustion model predicted a fire plume which was no more credible than that using a prescribed volumetric heat source. However, in other circumstances to those investigated here - particularly those where the plume may be tilted, the use of a combustion model even a crude model such as an eddy break-up model, should result in a more realistic prediction of the fire source. There are fewer user assumptions of the source in the combustion model, but as with the volumetric heat source model, the input values need to be chosen with care in order to lead to realistic temperatures in the plume region. These should be checked.

Two radically differing boundary conditions for heat transfer at walls (adiabatic and constant temperature) were found to have a significant influence on smoke transport for the offshore accommodation module, but not for the underground station. Clearly the modelling of wall

34

heat transfer is important, but the degree of importance depends on the particular scenario. The present study indicates that it is more likely to be significant for situations in which smoke movement is constrained by the presence of nearby walls or in the absence of forced ventilation.

5.2. Recommendations

This study investigated the influence of a range of CFD modelling approaches on the prediction of smoke movement in three real complex enclosed spaces. The results could only be compared qualitatively, since no measurements were available: another phase of the present project consists of small-scale experiments to allow a quantitative evaluation of CFD. The outcome is described in the HSL report CM/01/18-FS/01/13 (Ledin et al., 2002).

The present phase of research has provided an insight into both the capabilities, and limitations, of CFD modelling approaches for real complex fire scenarios. However, due to time and budget constraints, the investigation of the performance of any particular CFD modelling approach was usually limited to one scenario. This means that the findings and conclusions from this work must be interpreted with caution, since other could give very different behaviour.

For example, Kumar and Cox (2001) found that the prescribed shape of the fire in the volumetric heat source model can strongly influence the CFD results. They instead recommend the use of a combustion model that will be able to predict the volume of the flaming region. In the present study, however, no significant advantage was found between using a more carefully prescribed volumetric heat source model and an eddy-break-up combustion model, at least when applied to the building under construction. However, in a situation such as the underground station where a ventilation flow exists and is likely to tilt the flame, there could be expected to be a noticeable difference between the two models, with the combustion model providing more realism. Similarly, the flow induced by the fire inside the laundry of the offshore accommodation module is quite complex due to the proximity of the walls. This is likely to affect the flaming region and air entrainment into the fire plume: these effects can not be readily represented when using a prescribed volumetric heat source.

Likewise the test of heat transfer condition at the walls was undertaken for the underground station and repeated with the offshore accommodation module. However, the conclusions were different and this was believed to be due to the specific details of each scenario: the fire outputs were different; the fire was located in a small room in one scenario whilst it was in a large hall in the other; the presence or absence of a ventilation flow.

So extrapolation from the present findings and conclusions should be undertaken with caution and only for scenarios with similar flow characteristics, particularly in the near-source region. For instance, if the fire in the underground station was instead assumed to have started in a shop next to the ticket hall, this scenario would be physically closer to the fire in the laundry of the offshore accommodation module.

35

This scenario-dependent sensitivity to the detail of the modelling approaches means that it is vital that the user of CFD for smoke movement applications is knowledgeable and welltrained. Understanding of the physical and numerical modelling approaches in CFD - their basis, capabilities and limitations, must be coupled with an understanding of fire science: In particular, what is needed is an understanding of how the fire dynamics and smoke movement dynamics is affected by the CFD approach employed.

All of the existing CFD modelling approaches could not be reviewed in this work. It was therefore decided to concentrate on those most commonly employed and therefore the most likely to be presented in safety cases submitted to HSE. Amongst the models not included in this study are those for simulation of radiation. In principle, these can lead more realistic predictions of temperature distribution. Some models are available in commercial CFD codes. They are, however, often quite a crude representation of the real mechanisms. More sophisticated turbulence models, such as Large Eddy Simulation (L.E.S.) do exist. They are, however, very demanding in computer resources and therefore their application is typically limited to a simple geometry, i.e. a fire in a single room (McGrattan et al, 1998).

CFD is under constant development. As faster computers are developed, CFD users are provided with more and more sophisticated models that embody more physics, and thus are in principal more accurate. CFD is already playing an important role in Fire Safety Engineering. It thus seems set to play a wider role in the future.

However, CFD clearly has its limitations, not least that of sensitivities which are not always evident a-priori. New models or modelling approaches made available to the Fire Safety community will need to be carefully assessed before trust in their predictions can be gained. As regards existing modelling approaches for smoke movement in complex spaces, the sensitivities demonstrated in the present study serve to illustrate the need for a set of CFD simulations to be undertaken, rather then a one-off case; which could be misleading at best. This set of CFD simulations should focus on the potential key sensitivities.

36

6. REFERENCES

Atkinson G.T. (2000)Personal communication.

Buckland I. (2000)Personal communication.

Connolly S. (2000)Personal communication.

Cox G. (1995)Chapter 1 - Basic considerations.Combustion fundamentals of fire.Ed. G. Cox, Academic Press.

Drake S.N., Meeks K.R. (2001)Computer simulation of an emergency tunnel ventilation design.Tunnel Management International, Vol. 4, No 3, pp. 31-36.

Drysdale D. (1998)An introduction to fire dynamics.Chapter 4. 2nd edition. Ed. John Wiley & Sons.

Fennel D. (1988)An investigation into the Kings Cross underground fire.HMSD.

Gobeau N., Ledin H.S., Lea C.J. (2003)‘Guidance for HSE Inspectors - Smoke movement in complex enclosed spaces: assessment ofComputational Fluid Dynamics.’HSL report 02-19http://www.hse.gov.uk/research/hsl_pdf/2002/hsl02-29.pdf

Gobeau N., Ledin H.S., Ivings M.I., Lea C.J., Allen J.T., Bettis R.J. (2004)‘Evaluation of Computational Fluid Dynamics for the prediction of smoke movement incomplex enclosed spaces - Executive summary’HSL report CM/03/15. To be published.

Grant G., Lea C.J. (2001)‘A review of the modelling of fire in buildings and structures.’HSL report CM/01/01.

Hadjisophocleous G.V., Lougheed G.D., Cao S. (1999)‘Numerical study of the effectiveness of atrium smoke exhaust systems’ASHRAE Transactions: Symposia. CH-99-8-3 (RP-899) pp.699-715.

37

Heskestad G. (1983)‘Luminous heights of turbulent diffusion flames.’Fire Safety Journal, Vol. 5, pp. 103-108.

Hiorns N., Sinai Y. (1999)‘Comfort and safety in the Millennium Dome.’CFXUpdate, Vol. 20, Spring.

Ivings M.J. (1999)‘Computational Fluid Dynamics of the generation and transport of smoke’HSL report CM/99/02.

Kumar, S. And Cox, C. (2001)‘Some guidance on “correct” use of CFD models for fire applications with examples’9th International Interflam Conference Proceedings, Vol 2, pp. 823-834.

Launder B.E. (1975)'On the effect of a gravitational field on the turbulent transport of heat and momentum'Journal of Fluid Mechanics, Vol. 67, No 3, pp. 569.

Ledin H.S., Allen J.A., Bettis R., Ivings M. (2004)‘Evaluation of Computational Fluid Dynamics for the prediction of smoke movement incomplex enclosed spaces - Production of small-scale experimental data and assessment ofCFD’HSL report CM/04/07. To be published.

McCaffrey B.J. (1979)‘Purely buoyant diffusion flames: some experimental results.’National Bureau of Standards, NBSIR 79-1910.

McGrattan K.B., Baum H.R., Rehm R.G. (1998)‘Large Eddy Simulations of Smoke Movement’Fire Safety Journal, Vol. 30, pp. 161-178.

Rho J.S., Ryou H.S. (1999)‘A numerical study of atrium fires using deterministic models’.Fire Safety Journal, Vol. 33, pp. 213-229.

Rodi W. (1979)‘Influence of buoyancy and rotation on equations for the turbulent length scale.’2nd Symposium Turbulent Shear Flows proceedings, London, July 1979.

Sinai, Y.L. (2001)‘Validation of CFX5 for a compartment fire’.CFX Update, Vol. 21, Winter

Sinclair R. (2001)CFD simulation in atrium smoke management system design.

38

ASHRAE Transactions. AT-01-11-1. pp. 711-719.

The SFPE Handbook of Fire Protection Engineering (1995)2nd edition. Ed. National Fire Protection Association and Society of Fire ProtectionEngineers.

Thyer A. (1999)‘Comparison of the behaviour of cold artificial smoke used in the JLE station tests and hotsmoke from real fires.’HSL report FS/99/18.

den Tonkelaar, E. (2001)‘Breakthrough in Fire Safety assessment.’CFX Update, Vol. 20, Spring.

Versteeg H.K. and Malalasekera W. (1995)An introduction to Computational Fluid Dynamics.Ed. Longman

Viollet P. L., Benque J. P., and Goussebaile, J. (1983)‘Two-dimensional numerical modelling of nonisothermal flows for unsteady thermalhydraulic analysis.’Nuclear science and engineering, Vol. 84, pp. 350-372.

Xue, H., Ho J. C., and Cheng, Y. M. (2001)‘Comparison of different combustion models in enclosure fire simulation.’Fire Safety Journal, Vol. 36, pp. 37-54.

Yau R., Cheng V., Lee S., Luo M., Zhao L. (2001)‘Validation of CFD models for room fires and tunnel fires.’Interflam 2001, pp. 807-817.

39

7. ACKNOWLEDGEMENTS

Acknowledgements are due to Mr Barry Hodges, London Underground Ltd; Mr David Martin, FOD and Graham Moon, Canary Wharf Management who made it possible for HSL staff to visit respectively the underground station and the building under construction. Their help in providing the information necessary to carry out this work was greatly appreciated.

HSL is also grateful to Mr Dennis Streeter, London Fire Brigade, who helped in defining a realistic scenario of fire in the underground station.

The support received from AEA Technology in implementing the CFD models in the CFX codes and to run simulations on their computers was greatly appreciated. Special thanks are due to Dr Y. Sinai, Dr C. Hope, Mr P. Everitt and Ms S. Simcox.

40

Appendix A - Summary of scenarios and initial CFD models

41

Tab

le A

.1 -

Sum

mar

y of

the

scen

ario

s an

d in

itial

CFD

mod

ellin

g ap

proa

ches

SCE

NA

RIO

S P

rem

ises

U

nder

grou

nd s

tati

on

Off

shor

e ac

com

mod

atio

n m

odul

e B

uild

ing

unde

r co

nstr

ucti

on

Fir

e co

mbu

stib

le

Suitc

ase

cont

aini

ng c

loth

es

mad

e of

var

ious

fab

rics

50

% c

otto

n -

50%

pol

yest

er li

nen

PU F

oam

GM

23

Fir

e lo

cati

on

Tic

ket h

all -

unp

aid

side

L

aund

ry o

n fi

rst f

loor

O

pen

spac

e on

thir

d fl

oor

Fir

e po

wer

0.

2 M

W

1 M

W

1MW

V

enti

lati

on

Bac

kgro

und

vent

ilatio

n du

ring

fi

ve m

inut

es;

Forc

ed v

entil

atio

n af

ter

five

m

inut

es, o

nce

fire

det

ecte

d.

Non

e N

one

PH

YSI

CA

L

MO

DE

LL

ING

St

eady

/ Tra

nsie

nt

Tra

nsie

nt

Tra

nsie

nt

Tra

nsie

nt

Com

pres

sibi

lity

Bou

ssin

esq

appr

oxim

atio

n W

eakl

y co

mpr

essi

ble

Wea

kly

com

pres

sibl

e T

urbu

lenc

e St

anda

rd k

-e

Stan

dard

k-e

with

buo

yanc

y ef

fect

s (C

3=1)

St

anda

rd k

-e

with

buo

yanc

y ef

fect

s (C

3=1)

W

all b

ound

ary

cond

itio

ns

No

slip

T

urbu

lent

loga

rith

mic

wal

l fu

nctio

ns

Adi

abat

ic

No

slip

T

urbu

lent

loga

rith

mic

wal

l fu

nctio

ns

Con

stan

t am

bien

t tem

pera

ture

No

slip

T

urbu

lent

loga

rith

mic

wal

l fu

nctio

ns

Con

stan

t am

bien

t tem

pera

ture

In

let

boun

dary

co

ndit

ions

B

ackg

roun

d na

tura

l ven

tilat

ion

(t=0

to 5

min

utes

):

u=0.

1 m

/s f

rom

esc

alat

ors

to

plat

form

s T

rans

ition

fro

m b

ackg

roun

d to

fo

rced

ven

tilat

ions

(t

=5 to

6 m

inut

es):

lin

ear

incr

ease

fro

m b

ackg

roun

d to

fo

rced

ven

tilat

ion

valu

es

Forc

ed v

entil

atio

n (t

>6 m

inut

es):

u=

0.36

m/s

fro

m N

orth

esc

alat

or

u=0.

21 m

/s f

rom

Sou

th e

scal

ator

Non

e N

one

SCE

NA

RIO

S P

rem

ises

U

nder

grou

nd s

tati

on

Off

shor

e ac

com

odat

ion

mod

ule

Bui

ldin

g un

der

cons

truc

tion

P

HY

SIC

AL

M

OD

EL

LIN

G

Out

let

boun

dary

co

ndit

ions

Pr

essu

re b

ound

ary

at e

xits

N

one

Non

e

Init

ial c

ondi

tion

B

ackg

roun

d ve

ntila

tion

at a

mbi

ent

tem

pera

ture

(ob

tain

ed f

rom

an

initi

al s

tead

y-st

ate

calc

ulat

ion)

Qui

esce

nt f

low

at a

mbi

ent

tem

pera

ture

Q

uies

cent

flo

w a

t am

bien

t te

mpe

ratu

re

FIR

E S

OU

RC

E

Fir

e po

wer

C

onve

ctiv

e he

at r

ate

= 0.

2 M

W

Con

vect

ive

heat

rat

e =

0.

7 M

W

Con

vect

ive

heat

rat

e =

0.

55 M

W

Fir

e gr

owth

Q

= a

t ov

er 6

0 se

cond

s Q

= b

t2

over

122

sec

onds

Q

= b

t2

over

109

sec

onds

Fir

e si

ze

0.5

x 0.

5 x

0.6

= 0.

15 m

3 1.

5 x

1.5

x 1.

9 =

4.3

m3

1 x

1 x

2.2

= 2

.2 m

3

Smok

e di

ffus

ivit

y 10

-6 m

2 /s

10-5 m

2 /s

10-5 m

2 /s

NU

ME

RIC

AL

M

OD

EL

LIN

G

CF

D c

ode

CFX

5.4

C

FX 4

.3

CFX

4.3

Com

puta

tion

al

volu

me

17,9

54 m

3 1,

701

m3

97,2

77 m

3

Com

puta

tion

al

grid

U

nstr

uctu

red

380,

963

elem

ents

St

ruct

ured

54

blo

cks

- 28

,214

cel

ls

Stru

ctur

ed

109

bloc

ks -

155

,734

cel

ls

Dis

cret

isat

ion

sche

mes

Fi

rst o

rder

for

all

vari

able

s Se

cond

ord

er f

or a

ll va

riab

les

Firs

t ord

er f

or a

ll va

riab

les

Lin

ear

alge

brai

c eq

uati

ons

solv

er

Def

ault

Mul

tigri

d al

gori

thm

for

pre

ssur

e M

ultig

rid

algo

rith

m f

or p

ress

ure

Appendix B

Figures related to the initial simulation of the underground station

Figure B.1 - Layout of the underground station

Figure B.2 - Computational domain and grid

Figure B.3 - Background ventilation inside the underground station

Figure B.4 - Airflow field and iso-surfaces of smoke concentration 3 minutes after ignition (forced ventilation off)

Figure B.5 - Airflow field and iso-surfaces of smoke concentration 5 minutes after the ignition, just before forced ventilation is switched on.



42

Appendix B Initial simulation of the underground station

Fig

ure

B.1

- L

ayou

t of

the

unde

rgro

und

stat

ion.


Fig

ure

B.2

- C

ompu

tatio

nal d

omai

n an

d gr

id f

or th

e un

derg

roun

d st

atio

n(T

icke

t hal

l roo

f is

rem

oved

to a

llow

vis

ualis

atio

n of

the

geom

etry

insi

de th

e st

atio

n)

(Red

sur

face

s ill

ustr

ate

area

s w

ith im

pose

d fl

ow b

ound

ary

cond

ition

s; b

lue

surf

aces

are

as w

ith p

ress

ure

boun

dari

es.)


Fig

ure

B.3

- B

ackg

roun

d ve

ntila

tion

insi

de th

e un

derg

roun

d st

atio

nT

he a

rrow

s re

pres

ent v

eloc

ity v

ecto

rs a

bove

the

ticke

t hal

l flo

or a

nd a

t the

exi

ts; t

heir

leng

ths

and

colo

urs

are

linke

d to

thei

r st

reng

ths.


Fig

ure

B.4

- A

irfl

ow f

ield

and

sm

oke

iso-

surf

aces

3 m

inut

es a

fter

igni

tion.

In

dica

tive

valu

es o

f sm

oke

conc

entr

atio

n (g

/m3)

for

the

thre

e is

o-su

rfac

es: r

ed: 3

e-5;

pin

k:1e

-7; g

rey:

1e-1

0


Fig

ure

B.5

- A

irfl

ow f

ield

and

sm

oke

iso-

surf

aces

5 m

inut

es a

fter

igni

tion,

just

bef

ore

forc

ed v

entil

atio

n is

sw

itche

d on

.In

dica

tive

valu

es o

f sm

oke

conc

entr

atio

n (g

/m3)

for

the

thre

e is

o-su

rfac

es: r

ed: 3

e-5;

pin

k:1e

-7; g

rey:

1e-1

0


Fig

ure

B.6

- A

irfl

ow f

ield

and

sm

oke

iso-

surf

aces

46

seco

nds

afte

r fo

rced

ven

tilat

ion

star

ted

Indi

cativ

e va

lues

of

smok

e co

ncen

trat

ion

(g/m

3) f

or th

e th

ree

iso-

surf

aces

: red

: 3e-

5; p

ink:

1e-7

; gre

y:1e

-10


Fig

ure

B.7

- A

irfl

ow f

ield

and

sm

oke

iso-

surf

aces

65

seco

nds

afte

r fo

rced

ven

tilat

ion

star

ted

Indi

cativ

e va

lues

of

smok

e co

ncen

trat

ion

(g/m

3) f

or th

e th

ree

iso-

surf

aces

: red

: 3e-

5; p

ink:

1e-7

; gre

y:1e

-10

Appendix C

Figures related to the initial CFD simulation of theoffshore accommodation module

Figure C.1 - Layout of the offshore accommodation module : ground and first floors.

Figure C.2 - Layout of the offshore accommodation module : first and second floors.

Figure C.3 - Computational domain and grid.

Figure C.4 - Fire source in the laundry.








43

Appendix C Initial simulation of the offshore module

Figure C.1 - Layout of the offshore module: ground and first floors. Highlighted areas are the parts included in the computational domain.


Figure C.2 - Layout of the offshore module: second and third floors. Highlighted areas are the parts included in the computational domain.


- C

ompu

tatio

nal d

omai

n an

d gr

idF

igur

e 3


- Fi

re s

ourc

e in

the

laun

dry

Fig

ure

C.4


Fig

ure

C.5

Indi

cativ

e va

lues

of

- I

so-s

urfa

ces

of s

mok

e co

ncen

trat

ion

60 s

econ

ds a

fter

igni

tion.

smok

e m

ass

frac

tions

: pin

k 0.

0025

; gre

y: 0

.001

.


Fig

ure

C.6

- I

so-s

urfa

ces

of s

mok

e co

ncen

trat

ion

90 s

econ

ds a

fter

igni

tion.

In

dica

tive

valu

es o

f sm

oke

mas

s fr

actio

ns: r

ed: 0

.005

; blu

e:0.

004;

pin

k 0.

0025

; gre

y: 0

.001

.


Fig

ure

C.7

- I

so-s

urfa

ces

of s

mok

e co

ncen

trat

ion

120

seco

nds

afte

r ig

nitio

n.

Indi

cativ

e va

lues

of

smok

e m

ass

frac

tions

: red

: 0.0

05; b

lue:

0.00

4; p

ink

0.00

25; g

rey:

0.0

01.


Fig

ure

C.8

- I

so-s

urfa

ces

of s

mok

e co

ncen

trat

ion

150

seco

nds

afte

r ig

nitio

n.

Indi

cativ

e va

lues

of

smok

e m

ass

frac

tions

: red

: 0.0

05; b

lue:

0.00

4; p

ink

0.00

25; g

rey:

0.0

01.


Fig

ure

C.9

- I

so-s

urfa

ces

of s

mok

e co

ncen

trat

ion

180

seco

nds

afte

r ig

nitio

n.

Indi

cativ

e va

lues

of

smok

e m

ass

frac

tions

: red

: 0.0

05; b

lue:

0.00

4; p

ink

0.00

25; g

rey:

0.0

01.


Fig

ure

C.1

0 -

Iso-

surf

aces

of

smok

e co

ncen

trat

ion

210

seco

nds

afte

r ig

nitio

n.

Indi

cativ

e va

lues

of

smok

e m

ass

frac

tions

: red

: 0.0

05; b

lue:

0.00

4; p

ink

0.00

25; g

rey:

0.0

01.


Fig

ure

C.1

1 -

Iso-

surf

aces

of

smok

e co

ncen

trat

ion

450

seco

nds

afte

r ig

nitio

n.

Indi

cativ

e va

lues

of

smok

e m

ass

frac

tions

: red

: 0.0

05; b

lue:

0.00

4; p

ink

0.00

25; g

rey:

0.0

01.

Appendix D

Figures related to the initial CFD simulation of the building under construction

Figure D.1 - Schematic diagram of the building under construction, side elevation.

Figure D.2 - Computational domain and grid.

Figure D.3 - Fire source and computational grid on the third floor.






44

Appendix D Figures related to the building under construction

Figure D.1 - Schematic diagram of the building under construction, side elevation. Highlighted areas are the parts included in the computational domain.


Figure D.3 - Fire location and computational grid on third floor: The fire source is the red volume.

Appendix E

Comparison between CFD modelling approaches for the underground station

Figure E.1 -Temperature distribution near the fire 115 seconds after ignition.



Figure E.4 -Smoke concentration at the walls 115 seconds after ignition

Figure E.5 - Time-dependent smoke fluxes across vertical planes at entrance, mid-span and exit of the bridge.

Figure E.6 - Time-dependent smoke fluxes across vertical planes at the entrance of the corridor leading to exit 2 and at exit 2.

45

Appendix E Comparison for the underground station l

Initial

Run 2

Run 3

Run 4

Mesh

Figure E.1 - Temperature distribution near the fire 115 seconds after ignition

Appendix E Comparison for the underground station

Figure E.2 - Smoke concentration near the walls 60 seconds after ignition


Figure E.5 - Time-dependent smoke flux across the bridge (top: entrance; middle: mid-length; bottom: exit)


Figure E.6 - Time-dependent smoke flux along the corridor to exit 2(top: entrance; bottom: exit )

Appendix F

Comparison between CFD modelling approaches for the offshore accommodation module

Figure F.1 - Comparison of the two different grids employed.

Figure F.2 - Temperature distribution and velocities in the laundry, 120 seconds after ignition.

Figure F.3 - Profiles of excess temperature and vertical velocity in the fire source, 120 seconds after ignition.

Figure F.4 - Profiles of excess temperature, smoke mass fraction and lateral veloci ties near the fire, 120 seconds after ignition.

Figure F.5 - Temperature distribution and velocity vectors on the first floor - location of the fire - 45 centimetres above the ground, 120 seconds after ignition.

Figure F.6 - Temperature distribution and velocity vectors on the first floor - location of the fire - 2 metres above the ground, 120 seconds after ignition .

Figure F.7 - Profiles of excess temperature, smoke mass fraction and velocity in the doorway of the laundry opening to a corridor, 120 seconds after ignition.

Figure F.8 - Profiles of excess temperature, smoke mass fraction and velocity in the doorway of the laundry near a stairwell, 120 seconds after ignition.

Figure F.9 - Smoke iso-surfaces 120 seconds after ignition.

46

Temperature (K)

Appendix F Comparison for the offshore accommodation module

Initial

Run 2

Run 3

Run 4

Figure F.2 - Temperature contours and velocities in the laundry 120 seconds after ignition.


3.2

1.9

i

i i

i l

iile

z (m)

Pro

file

acro

ss th

e fr

e

(Fgu

re F

.4)

(Fgu

re F

.3)

vert cal y upwards

Hor

zont

al p

rof

-4.2 -3.6 -0.75 0 0.75 1.8 y (m)

A) Excess temperature B) Vertical velocity

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5

z(m

)


0

0.5

1

1.5

2

2.5

3

0 50 100 150 200 250 300 350

z(m

)


Temperature (K) Vertical velocity (m/s)

Figure F.3 - Profiles of excess temperature and vertical velocity in the fire source 120 seconds after ignition

250

Appendix F Comparison for the offshore accommodation module A) Excess temperature B) Smok e mass fraction


0.014


0.012

200

0.01

150 0.008

Sm

oke

Exc

ess

tem

pera

ture

0.006 100

0.004

50

0.002

0 -3 -2 -1 0 1

y(m)

0 -3 -2 -1 0 1

y(m)

1.5

1

0.5

-1.5

-1

-0.5

0

Ver

tical

vel

ocity

(m

/s)


C) lateral velocity

-3 -2 -1 0 1

y(m)

Figure F.4 - Profiles of excess temperature, smoke mass fraction and lateral velocities near the fire, 120 seconds after ignition.


Initial Run 2

Temperature (K)

Run 3 Run 4

Figure F.5 - Temperature distribution and velocity vectors 45 centimetres above the ground in the laundry, 120 seconds after ignition.


Initial Run 2

Temperature (K)

Run 3 Run 4

Figure F.6 - Temperature distribution and velocity vectors 2 metres above the ground, 120 seconds after ignition.

3

Appendix F Comparison for the offshore accommodation module z(

m)

A) Excess temperature B) Smoke mass fraction


3

z(m

)


2.5 2.5

2 2

1.5 1.5

1 1

0.5 0.5

0 0 50 100 150 200

Excess temperature

0 250 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008

Smoke

z(m

)

3

2.5


C) Velocity normal to the doorway

2

1.5

1

0.5

0-1.5 -1 -0.5 0 0.5 1

Velocity normal to doorway (m/s)

Figure F.7 - Profiles of excess temperature, smoke mass fraction and velocity in the doorway of the laundry opening to the corridor, 120 seconds after ignition.

3

Appendix F Comparison for the offshore accommodation module A) Excess temperature B) Smoke mass fraction

3

z(m

)

2.5


2.5

2 2

1.5 1.5

1 1

z(m

)

0.5 0.5

0


0 50 100 150 200

Excess temperature

0 250 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008

Smoke

z(m

)

3

2.5

2


C) Velocity normal to the doorway

1.5

1

0.5

0-1 -0.5 0 0.5 1 1.5

Velocity normal to doorway (m/s)

Figure F.8 - Profiles of excess temperature, smoke mass fraction and velocities in the doorway of the laundry near a stair well, 120 seconds after ignition.

Appendix G

Comparison between CFD modelling approaches for the building under construction

Figure G.1 - Excess temperature and mesh near the fire 150 seconds after ignition

Figure G.2 - Vertical profiles of excess temperature and smoke concentration in the centreline of the fire 150 seconds after ignition

Figure G.3 - Vertical profiles of excess temperature and smoke concentration ten metres away from the fire, 150 seconds after ignition

Figure G.4 - Horizontal profiles of excess temperature and smoke concentration 4.55 metres above the fire, 0.45 metres from the ceiling 150 seconds after ignition.

Figure G.5 - Excess temperature distribution on the third floor - location of the fire,150 seconds after ignition.

Figure G.6 - Smoke concentration distribution on the third floor -location of the fire,150 seconds after ignition.

Figure G.7 - Smoke concentration distribution in the atrium, 150 seconds after ignition.

47

Appendix G Comparison for the building under construction

Initial

Run 2

Run 3

Run 4

Mesh

Figure G.1 - Excess temperature distribution near the fire 150 seconds after ignition and mesh (bottom picture)

(the red cells correspond to the volume where heat is prescribed in the volumetric heat source model).


Figure G.2 - Vertical profiles of excess temperature and smoke concentration along the centreline of the fire 150 seconds after ignition

Comparison for the building under constructionAppendix G

-5

0

5

10

15

20

25

30

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

z (m)

Excess temperature


Figure G.3 - Vertical profiles of excess temperature and smoke concentration ten metres away from the fire 150 seconds after ignition

(top: on the side nearest the atrium; bottom: furthest away from the atrium)


Figure G.4 - Horizontal profiles of excess temperature and smoke concentration 4.55 metres above the fire, 0.5 m below the ceiling

150 seconds after ignition


Initial Run 2

Run 3 Run 4

Figure G.5 - Excess temperature distribution on the third floor - location of the fire -at 4.55 m above the floor, 150 seconds after ignition.

(The temperature scale has been fixed between 0 and 25 oC.)


Initial Run 2

Run 3 Run 4

Figure G.6 - Smoke concentration (kg/m3) distribution on the third floor - location ofthe fire - at 4.55 m above the floor, 150 seconds after ignition.(The temperature scale has been fixed between 0 and 25 oC.)


Initial Run2 Run3 Run4

Figure G.7 - Smoke concentration (kg/m3) distribution in the atrium150 seconds after ignition


Z = 5.2 m.

Z= 31.3 m.

Z=51.6 m.

Figure G.8 - Time-dependent smoke flux rising into the atrium at different heights

Appendix H

Buoyancy modified turbulent equations in CFX

48

Appendix H Buoyancy modified equations in CFX The transport equations for the turbulent kinetic energy k and turbulence dissipation rate ε are (CFX4 and CFX5 manuals, Ed. AEA Technology, CFX International, UK):

( ) ρεσμμρρ −+=⎟

⎟⎠

⎞⎜⎜⎝

⎛∇⎟⎟⎠

⎞⎜⎜⎝

⎛+•∇−•∇+

∂∂ GPU kkkt k

t

( ) ( )( )k

CCk

Ct

t2

231 0,max ερεεσμμερρεε

−+=⎟⎟⎠

⎞⎜⎜⎝

⎛∇⎟⎟⎠

⎞⎜⎜⎝

⎛+•∇−•∇+

∂∂ GPU

where: ρ is the fluid density U is the fluid velocity μ is the fluid viscosity μt is the turbulent viscosity C1, C2, C3, σk, σε are constants P is the shear production term G is the production due to the body force The values of the constants employed for the simulations presented in this report were the well-accepted standard values:

C1 1.44

C2 1.92

C3 0 (no buoyancy modification) or 1 (with buoyancy modification)

σκ 1

σε 1.3

The influence of buoyancy on turbulence is accounted for by the production term G, which is expressed by:

ρρσμ

∇−= g.t

tG where g is the gravitational force.

Printed and published by the Health and Safety ExecutiveC30 1/98

Printed and published by the Health and Safety ExecutiveC1.10 09/04

RR 255

£25.00 9 78071 7 62881 0

ISBN 0-7176-2881-7

research report 255 · computational fluid dynamics (cfd) to predict the transport of smoke in...

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