Data driven forecast of concurrent flame spread in micro-gravity

This paper presents a methodology that combines data assimilation and physical modelling of flame spread for fire growth forecast. The concurrent spreading over flat solid sample in the absence buoyant flows is considered as simple canonical configuration. To predict rate length evolution, analytical solution to two-dimensional boundary layer non-premixed combustion problem combined with CFD model which delivers structure flow away from fuel surface. In process, two coefficients intervene gas heat transfer equations are assimilated using pyrolysis surrogate input absorb shortcomings modelling. robustness overall method evaluated through convergence assessment these variables different initial guesses, values invariants. Validation large-scale microgravity provides confidence this approach, demonstrates its potential deliver predictions measurements at reduced computational cost. However, discrepancies between question degree correlation lengths.