This review paper presents a comprehensive analysis of the computational tools of modern fire protection engineering (FPE). The paper categorizes and examines the primary tools used for fire-related calculations, providing a critical evaluation of their theoretical underpinnings, applications, and limitations. The review comprises five principal domains: (1) Fire and Smoke Dynamics Modelling, including a comparative analysis of Computational Fluid Dynamics (CFD) field models such as the Fire Dynamics Simulator (FDS) and zone models like the Consolidated Fire and Smoke Transport (CFAST) model, along with their associated graphical user interfaces (PyroSim) and visualization tools (Smokeview); (2) Egress and Evacuation Modelling, focusing on agent-based simulation platforms like Pathfinder, buildingEXODUS, and STEPS that are used to assess human behaviour and movement during emergencies; (3) Hydraulic Calculation Software, covering specialized tools for fire suppression system design (HASS, AutoSPRINK) and general water network analysis programs (Pipe Flow Professional, EPANET); (4) Consequence and Risk Assessment Tools, which are essential for analyzing high-hazard industrial scenarios using software such as PHAST, FLACS, and ALOHA; and (5) Foundational and Ancillary Resources, including the indispensable SFPE Handbook of Fire Protection Engineering and the utility and inherent risks of custom spreadsheets.
Introduction
1. Historical Evolution of Fire Protection Engineering
Origins: Rooted in ancient prescriptive building codes using specific materials and dimensions.
Key Events:
Great Fire of London (1666) led to regulations mandating brick and stone.
Triangle Shirtwaist Factory Fire (1911, US) triggered the formation of fire safety committees like NFPA.
Transition: Industrialization and complex buildings exposed the limitations of prescriptive codes, prompting a shift to performance-based design (PBD) and scientific approaches.
NFPA (1896): Founded to standardize fire safety, largely driven by the insurance industry.
Modern FPE: Informed by fire dynamics, human behavior, and structural responses, supported by computational tools.
2. Rise of Computational Fire Engineering
Performance-Based Design (PBD): Focuses on outcomes (e.g., safe evacuation) rather than strict compliance with prescriptive codes.
Computational Needs: Emergence of models to simulate fire growth, smoke spread, structural behavior, and human response.
3. Fire & Smoke Dynamics Modelling
A. Two Main Model Types:
Feature
Zone Models (e.g., CFAST)
Field Models / CFD (e.g., FDS)
Domain
Simplified (2-layer zones)
Detailed grid (thousands to millions of cells)
Speed
Fast (seconds-minutes)
Slow (hours to weeks)
Detail
Basic (averaged properties)
High-fidelity (3D, time-dependent)
Use Case
Multi-compartment risk analysis
Complex spaces like tunnels, atria
Expertise Required
Moderate
High (fluid dynamics, thermodynamics)
B. Key Tools:
CFAST (by NIST): Rapid, zone-based, ideal for simple risk scenarios but limited in spatial detail.
FDS (Fire Dynamics Simulator): High-resolution CFD tool using Navier-Stokes equations and LES turbulence models. Requires advanced expertise.
C. Supporting Tools:
PyroSim: GUI for FDS; simplifies model creation with CAD imports and mesh control.
Smokeview (SMV): Visualization tool for simulation outputs—3D smoke, temperature, and flow visualizations.
4. Human Behavior & Evacuation Modelling
Key Concepts:
ASET vs. RSET:
ASET (Available Safe Egress Time): Time before conditions become life-threatening.
RSET (Required Safe Egress Time): Total time occupants need to evacuate.
Design aims to ensure RSET < ASET.
Pre-evacuation delay (e.g., confusion, alerting others) is a major uncertainty in egress modelling.
Leading Egress Tools:
Tool
Key Features
Pathfinder
Most used; offers both SFPE (hydraulic) and agent-based simulation modes.
buildingEXODUS
Advanced behavioural modelling with sub-models for movement, toxicity, group behavior, and psychological traits.
STEPS
Vector-based, agent-adaptive model ideal for large venues (airports, stadiums). Handles complex crowd dynamics like merging and contra-flow.
5. Final Insights
FPE has evolved from reactive, rule-based systems to predictive, science-driven modelling.
Modern engineers must choose tools based on building complexity, fire scenario, desired accuracy, and available computational power.
User interfaces (e.g., PyroSim) have made complex modelling more accessible, but expert judgment remains critical to avoid misuse.
The integration of fire and evacuation models allows for a comprehensive understanding of fire safety in modern buildings.
Conclusion
This review has systematically categorized and analysed the primary software and resources used for fire-related calculations, from simulating the fundamental physics of fire and smoke to modelling the complex dynamics of human evacuation and the performance of suppression systems.
In fire dynamics, the choice between rapid zone models like CFAST and high-fidelity CFD models like FDS allows engineers to scale their analysis to the problem\'s complexity. Specialized hydraulic calculators like HASS and AutoSPRINK have become indispensable for the efficient and accurate design of suppression systems, while sophisticated consequence models like PHAST and FLACS provide the means to quantify and manage risks in high-hazard industries.
The core conclusion of this review is that computational tools are aids to, not substitutes for, engineering judgment. The validity of a simulation\'s output is inextricably linked to the user\'s understanding of the underlying physical phenomena, the model\'s inherent assumptions and limitations, and the quality of the input data.
References
[1] Wang, H., Chen, Q., Yan, J., Yuan, Z., & Liang, D. (2014). Emergency guidance evacuation in fire scene based on Pathfinder. International Conference on Intelligent Computation Technology and Automation.
[2] Zhao, J., Xu, Z., Ying, H., Guan, X., Chu, K., Sakepa Tagne, S.M., et al. (2022). Study on smoke spread characteristic in urban interval tunnel fire. Case Studies in Thermal Engineering, 30, 101755.
[3] Klote, J.H. (2014). AtriumCalc Atrium Smoke Control Calculator Technical Information and User Guide.
[4] Ayala, P., Cantizano, A., Sanchez-beda, E.F., & Gutierrez-Montes, C. (2017). The use of fractional factorial design for atrium fires prediction. Fire Technology, 53, 893-916.
[5] Zhang, X., Wu, X., Park, Y., Zhang, T., Huang, X., Xiao, F., et al. (2021). Perspectives of big experimental database and artificial intelligence in tunnel fire research. Tunnelling and Underground Space Technology, 108, 103691.
[6] Gales, J., Chorlton, B., & Jeanneret, C. (2020). The historical narrative of the standard temperature-time heating curve for structures. Fire Technology.
[7] Park, J.W., Lim, O.K., & You, W.J. (2020). Effect of ignition heat source on design fire curve of polyethylene foam in a compartment fire. Case Studies in Thermal Engineering, 22, 100790
[8] On, M., Zhou, H., Ying, H., & Lee, S. (2022). A voice-driven IMU-enabled BIM-based multi-user system for indoor navigation in fire emergencies. Automation in Construction, 135, 104137.
[9] Qin, T.X., Guo, Y.C., Chan, C.K., & Lin, W.Y. (2009). Numerical simulation of the spread of smoke in an atrium under fire scenario. Building Environment, 44, 566-575.
[10] Ayala, P., Cantizano, A., Gutierrez-Montes, C., & Rein, G. (2013). Influence of atrium roof geometries on the numerical predictions of fire tests under natural ventilation conditions. Energy and Buildings, 65, 382-390.
[11] Drysdale, D. (2011). An Introduction to Fire Dynamics (3rd ed.). John Wiley & Sons.
[12] Baum, H., & McCaffrey, B. (1989). Fire induced flow field - theory and experiment. Fire Safety Science 2, 129–148.
[13] Li, Z., Huang, H., Li, N., Ling, M., Zan, C., & Law, K. (2020). An agent-based simulator for indoor crowd evacuation considering fire impacts. Automation in Construction, 120, 103395.
[14] Chadderton, D.V. (2013). Building Services Engineering. Routledge.
[15] Peacock, R.D., Jones, W.W., Reneke, P.A., & Forney, G.P. (2005). CFAST – Consolidated Model of Fire Growth and Smoke Transport Version 6 Users Guide. NIST.
[16] Wang, Y., Chatterjee, P., & De Ris, J.L. (2011). Large eddy simulation of fire plumes. Proceedings of the Combustion Institute, 33, 2473-2480.
[17] Hostikka, S., Kokkala, M., & Vaari, J. (2001). Experimental Study of the Localized Room Fires.
[18] Gutierrez-Montes, C., Sanmiguel-Rojas, E., Burgos, M.A., & Viedma, A. (2012). On the fluid dynamics of the make-up inlet air and the prediction of anomalous fire dynamics in a large-scale facility. Fire Safety Journal, 51, 27-41.
[19] Chow, W.K., Li, S.S., Gao, Y., & Chow, C.L. (2009). Numerical studies on atrium smoke movement and control with validation by field tests. Building and Environment, 44, 1150-1155.
[20] Khan, M.M., & Tewarson, A. (2016). Combustion Characteristics of Materials and Generation of Fire Products, SFPE Handbook of Fire Protection Engineering (5th ed.), pp.1143–1232.
[21] Gutierrez-Montes, C., Sanmiguel-Rojas, E., & Viedma, A., Rein, G. (2009). Experimental data and numerical modelling of 1.3 and 2.3 MW fires in a 20 m cubic atrium. Building and Environment, 44, 1827-1839.
[22] Su, L.C., Wu, X., Zhang, X., & Huang, X. (2021). Smart performance-based design for building fire safety prediction of smoke motion via AI. Journal of Building Engineering, 43, 102529.
[23] Stollard, P. (2014). Fire from First Principles. Routledge.
[24] Hurley, M.J., & Rosenbaum, E.R. (2015). Performance-based Fire Safety Design. CRC Press.
[25] Zeng, Y., Zhang, X., Su, L.-C., Wu, X., & Huang, X. (2022). Artificial Intelligence tool for fire safety design (IFETool): Demonstration in large open spaces. Case Studies in Thermal Engineering, 40, 102483.
[26] The Chartered Institution of Building Services Engineers (CIBSE). (2019). Guide E Fire Safety Engineering (4th ed.).
[27] Chen, H., Hou, L., Zhang, G.K., & Moon, S. (2021). Development of BIM, IoT and AR/VR technologies for fire safety and upskilling. Automation in Construction, 125, 103631.
[28] Deal, S. (1995). Technical Reference Guide for FPEtool Version 3.2. NIST.
[29] Al-Waked, R., Nasif, M., Groenhout, N., & Partridge, L. (2021). Natural ventilation of residential building Atrium under fire scenario. Case Studies in Thermal Engineering, 26, 101041.
[30] Dimyadi, J., Clifton, C., Spearpoint, M., & Amor, R. (2014). Computer-aided compliance audit to support performance-based fire engineering design. Proceedings of the 10th International Conference on Performance-Based Codes and Fire Safety Design Methods.
[31] Dumoulin, V., & Visin, F. (2016). A Guide to Convolution Arithmetic for Deep Learning. arXiv preprint arXiv:1603.07285.
[32] Building Department. (2011). Code of Practice for Fire Safety in Buildings.
[33] Law, M. (1995). The origins of the 5MW design fire. Fire Safety Engineering, 2, 17.
[34] National Fire Protection Association (NFPA). (2018). NFPA 92 Standard for Smoke Control Systems, vol. 229.