Imagine a technology able to forecast fires. It would lead to a paradigm shift in the response to emergencies and provide the Fire Services with essential information about the ongoing blaze with some lead time (i.e. seconds or minutes ahead of the event). It would also allow for the future of infrastructure protection to be implemented in smart buildings.
|316 s after ignition. could this be forecasted ahead of time? [Rein 2012]|
|Paleofuture: forecast made in 1900 of the fire-fighting in the year 2000. |
By Villemard, 1910, National Library of France
Our method consists on combining a simplified spread mechanism with a fire model, and use sensor data to find the fire parameters that dominate the spread. This way, the model automatically recovers information lost by approximations in the physics, chemistry and the maths.
|Concept of data assimilation and the sensor steering of model predictions [Cowlard et al 2010]|
For the simple two-zone forecast model, the firepower and the growth rate were estimated correctly up to 30 s ahead of the event: the model was faster than the fire. This was the very first time a fire forecast technology was demonstrated and the first time positive lead times were reached. The results show that the simple model is able to deliver fast and useful information about the ongoing fire thanks to the sensor data. This initial work demonstrated that the new methodology is effective, and allowed us to move to the next level of complexity.
|Computational domain of the fire|
compartment [Jahn et al 2012]
|Dalmarnock Fire Test One conducted on July 25th.|
The results are a fundamental step towards the development of forecast technologies able to lead the fire emergency response. The work opens the door to forecasting fire dynamics, but it is an on-going research topic.
We are happy that the work has been featured in the media and people is being exposed to this novel idea:
- Interview for Scottish TV News (go to minute 19 here). Aired on 29 Nov 2010.
- Interview for BBC Radio Scotland (or go minute 42.20 here). Aired on 29 Nov 2010.
- Articles in CORDIS-EU and Xinhuanet (in Chinese).
Our research resources on the topic (in reverse chronological order):
1) Jahn, Rein and Torero (2012), Forecasting fire dynamics using inverse
Computational Fluid Dynamics and Tangent Linearisation, Advances in
Engineering Software 47 (1), pp. 114-126. doi:10.1016/j.advengsoft.2011.12.005
2) Rein (2012), Plenary Keynote: Numerical forecasting of fire dynamics: tomorrow's infrastructure protection - Young Investigators Conference of the European Community on Computational Methods in Applied Sciences (ECCOMAS), Aveiro. See below.
3) Jahn, Rein and Torero (2011), Forecasting Fire Growth using an Inverse CFD Modelling Approach in a Real-Scale Fire Test, Fire Safety Science 10, pp 1349-1358, doi:10.3801/IAFSS.FSS.10-1349
4) Jahn, Rein and Torero (2011), Forecasting Fire Growth using an Inverse Zone Modelling Approach, Fire Safety Journal 46, pp. 81–88. doi:10.1016/j.firesaf.2010.10.001. Paper shortlisted for 2010 Lloyd's Science of Risk Prize.
5) Jahn (2010), Inverse Modelling to Forecast Enclosure Fire Dynamics, PhD Thesis, School of Engineering, University of Edinburgh.
6) Cowlard, Jahn, Empis, Rein and Torero (2010), Sensor Assisted Fire Fighting, Fire Technology 46 (3), doi:10.1007/s10694-008-0069-1
Numerical forecasting of fire dynamics (Plenary YIC ECCOMAS)