Xi, X;
Torero, JL;
Guibaud, A;
(2022)
Data driven forecast of concurrent flame spread in micro-gravity.
Combustion and Flame
, 241
, Article 112078. 10.1016/j.combustflame.2022.112078.
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Abstract
This paper presents a methodology that combines data assimilation and physical modelling of flame spread for fire growth forecast. The concurrent flame spreading over a flat solid sample in the absence of buoyant flows is considered as a simple canonical fire spread configuration. To predict flame spread rate and flame length evolution, the analytical solution to the two-dimensional boundary layer non-premixed combustion problem is combined with a CFD model which delivers the structure of the flow away from the fuel surface. In the process, two coefficients which intervene in the gas and solid heat transfer equations are assimilated using the pyrolysis length data as a flame spread rate surrogate input to absorb the shortcomings of the modelling. The robustness of the overall method is evaluated through convergence assessment of these two assimilated variables for different initial guesses, and for different values of the input invariants. Validation over large-scale microgravity data provides confidence in this approach, and demonstrates its potential to deliver flame spread predictions from initial measurements at a reduced computational cost. However, discrepancies between flame length measurements and predictions question the degree of correlation between pyrolysis and flame lengths.
| Type: | Article |
|---|---|
| Title: | Data driven forecast of concurrent flame spread in micro-gravity |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.combustflame.2022.112078 |
| Publisher version: | https://doi.org/10.1016/j.combustflame.2022.112078 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Data assimilation, Concurrent flame spread, Hybrid model, Microgravity |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10145279 |
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