Faster prediction of wildfire behaviour by physical models through application of proper orthogonal decomposition
Elisa Guelpa A , Adriano Sciacovelli A , Vittorio Verda A C and Davide Ascoli B
+ Author Affiliations
- Author Affiliations
A Energy Department, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy.
B Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Via da Vinci 44, 10095 Grugliasco (TO), Italy.
C Corresponding author. Email: vittorio.verda@polito.it
International Journal of Wildland Fire 25(11) 1181-1192 https://doi.org/10.1071/WF15150
Submitted: 13 August 2015 Accepted: 26 July 2016 Published: 20 October 2016
Abstract
Physical models of wildfires are of particular interest in fire behaviour research and have applications in firefighting, rescue and evacuation. However, physical models present a challenge as a result of the large computational resources they often require, especially for the analysis of large areas or when multiple scenarios are investigated. The objective of this paper is to explore the opportunity to reduce the computation time requested by physical wildfire models through application of a model order reduction technique, specifically the proper orthogonal decomposition (POD) technique. POD is here applied to a simple one-dimensional physical model. The full physical model for illustration of the concept is first tested with experimental data to check its ability to simulate wildfire behaviour; it is then reduced using the POD technique. It is shown that the reduced model is able to simulate fire propagation with only small deviations in results in comparison with the physical model (~6.4% deviation in the rate of spread, ROS) and a drastic reduction (~85%) in computational cost. The results demonstrate the advantages of applying effective reduction techniques to create new generations of fire models based on reduced physical approaches. The potential applicability of POD to more complex models is also discussed.
Additional keywords: computational cost reduction, model reduction, physical model, POD.
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