Dr John Ewer PhD, MSc, BSc.

Senior Lecturer

Dr John Ewer is a core member of the Fire Safety Engineering Group (FSEG) and project leader for SMARTFIRE research and software development.

He joined the University of Greenwich as a PhD researcher in 1994 after completing his MSc in the, then, Thames Polytechnic.

He has recently worked in support of the Grenfell Tower Fire Inquiry using fire models to examine smoke infiltration and the effects of fire fighter/resident actions and the refurbishment (2017-2020).

He also continues his leadership role as principal software developer and project manager for the SMARTFIRE Fire Field Modelling commercial software. SMARTFIRE is a licensed product that has 27 customers in 14 countries.

Past major projects he has worked on include: EU Getaway (EU FP7, 2011) (developing a system for active dynamic signage in smart buildings), EU AUGGMED (EU HORIZON 2020) (developing a serious game training system for first responders), as well as several CBRN related projects for UK Home Office and EU AircraftFire (EU FP7, 2011).

Since 2015, he has developed a new strand of enterprise consultative work for 3rd party fire strategy reviews of fire modelling submissions for building regulation (7 completed to date).

He has successfully supervised 3 PhD students and is currently supervising a PhD student researching modelling techniques for the dynamics of fires in baled waste.


PhD. in re-engineering and development of an object-oriented, interactive, event-driven CFD code from a legacy FORTRAN code. University of Greenwich, 2020.

MSc. in Scientific and Engineering Software Technology. University of Greenwich, 1991.

BSc. in Physics. Reading University, 1989.

Responsibilities within the university

Senior Lecturer (Researcher) in Fire Modelling


2014: EU FP7 “GETAWAY” Project (Intelligent Active Dynamic Signage Systems) awarded the prestigious Research Impact Award as part of The Guardian University Awards.

2010: Co-authored Journal paper: “Fire and evacuation analysis in BWB aircraft configurations: computer simulations and large-scale evacuation experiments” won best paper in The Aeronautical Journal.

Research / Scholarly interests

Dr Ewer is currently working on:

Research and development leading to the commercialisation of modelling capabilities related to SARS-CoV-2/Covid-19 aerosol and droplet dispersion and infection risk assessment (Innovation Fund for Proof of Concept),

Leading a research team developing a VR based Virtual Training Environment for Aircraft Crew (HEIF, 2019), and

Leading the SMARTFIRE software development for a research project developing a fully coupled Fire and Evacuation VR based framework, Integrated Ship Survivability Assessment Capability (ISSAC), for the Australian DSTG (2013 – Date).

(HEIF) funded review project concerning the fire safety risks and regulation of Green/Living Walls.

He also coordinates and lectures on the annual Principles and Practice of Fire Modelling (PPFM) CPD short course for fire professionals, with optional Master of Science by Research. A distance learning version of the course has just been finalised for deployment during the Covid-19 pandemic.

Among his many interests are: Computational Fluid Dynamics (CFD), Object Oriented Programming, C++, Fire Field Modelling (FFM), Fire Strategy Reviews, Fire Safety Consultancy, Optimization techniques, Knowledge Based Solution Control, Human Computer Interaction techniques, Visualization, VR/AR and Mixed Reality, Serious Gaming, Building Information Modelling (BIM), Software Re-engineering & Re-use and Algorithm development.

Recent publications


Stettler, Marc E. J. , Nishida, Robert T., de Oliveira, Pedro M., Mesquita, Léo C. C. , Johnson, Tyler J. , Galea, Edwin , Grandison, Angus , Ewer, John , Carruthers-Jones, David , Sykes, David (2022), Source terms for benchmarking models of SARS-CoV-2 transmission via aerosols and droplets. The Royal Society - Royal Society of Chemistry (RSC). In: , , , . The Royal Society - Royal Society of Chemistry (RSC), Royal Society Open Science, 9: 212022 (5) 2054-5703 (Online) (doi: https://doi.org/10.1098/rsos.212022).

Wang, Zhaozhi , Galea, Edwin R., Grandison, Angus, Ewer, John , Jia, Fuchen (2021), A coupled computational fluid dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains. Elsevier. In: , , , . Elsevier, Safety Science, 147: 105572 ISSN: 0925-7535 (Print), (doi: https://doi.org/10.1016/j.ssci.2021.105572) NB Item availability restricted.

Siddiqui, Asim A. , Ewer, John A., Lawrence, Peter J., Galea, Edwin R. , Frost, Ian R. (2021), Building information modelling for performance-based fire safety engineering analysis – a strategy for data sharing. Elsevier. In: , , , . Elsevier, Journal of Building Engineering, 42: 102794 2352-7102 (Online) (doi: https://doi.org/10.1016/j.jobe.2021.102794).

Wang, Zhaozhi , Galea, Edwin R., Grandison, Angus, Ewer, John , Jia, Fuchen (2021), Inflight transmission of COVID-19 based on experimental aerosol dispersion data. Oxford University Press. In: , , , Inflight Transmission of COVID-19 Based on Aerosol Dispersion Dat. Oxford University Press, Journal of Travel Medicine, 28: taab023 (4) ISSN: 1195-1982 (Print), 1708-8305 (Online) (doi: https://doi.org/10.1093/jtm/taab023).

Woolley, Anthony , Ewer, John, Lawrence, Peter, Deere, Steven , Travers, Anthony , Whitehouse, Tom , Galea, Edwin Richard (2020), A naval damage incident recoverability toolset: Assessing naval platform recoverability after a fire event. Elsevier. In: , , , . Elsevier, Ocean Engineering, 207: 107351 ISSN: 0029-8018 (Print), (doi: https://doi.org/10.1016/j.oceaneng.2020.107351) NB Item availability restricted.

Galea, E.R. , Wang, Z., Jia, F., Lawrence, P.J. , Ewer, J. (2016), Fire safety assessment of open wide gangway underground trains in tunnels using coupled fire and evacuation simulation. Wiley. In: , , , . Wiley, Fire and Materials, 41 (6) . pp. 716-737 ISSN: 0308-0501 (Print), 1099-1018 (Online) (doi: https://doi.org/10.1002/fam.2413) NB Item availability restricted.


Ewer, J. , Galea, E., Grandison, A., Patel, M. (2017), SMARTFIRE v4.4: technical reference manual. University of Greenwich. In: , , , . University of Greenwich, Greenwich, London, UK (doi: ).