Post Doctoral Position: Stochastic modeling of firebrand ignition

International Association for Fire Safety Science; Marseille; Sur place; EUR 35 000 - 50 000; Plein temps

Avantages offerts par ce poste

Additional support budget of 5k euros/year for symposiums and missions

Résumé du poste

The International Association for Fire Safety Science in Marseille is seeking a Post Doc to research firebrand spotting, crucial for understanding wildfire dynamics. As part of a project funded by Aix-Marseille University, the role will last up to 24 months with potential renewal.

The ideal candidate must hold a PhD in combustion or fire research and demonstrate autonomy and proactivity. Practical experience in CFD modeling and numerical simulation is expected.

Qualifications

• PhD holder in combustion, fire research, or numerical simulation.
• Experience in research laboratory environments.
• Demonstrated autonomy and proactive approach.

Responsabilités

• Investigate firebrand spotting in wildfires and develop models.
• Combine CFD modeling with statistical methods for stochastic models.
• Work on developing ignition models for fuel beds.

Connaissances

Autonomy

Numerical simulation

Research in combustion

Formation

PhD related to combustion or fire research

Outils

CFD modeling software

FireStar3D

Description du poste

Description of the Post Doc

This research project will be funded by the “Institute for Mechanical Engineering” of Aix-Marseille University. Once selected by the research team, the project and the candidate will be evaluated in a single round in November 2025 for a final decision in December 2025. The Post Doc will last up to 24 months (12 months renewable once). He/she will benefit from an additional support budget of 5k euros/year (for symposium participation, international collaboration missions, small equipment, etc.).

This research project investigates firebrand spotting, a critical mechanism for fire spread in both wildland and wildland-urban interface (WUI) fires. Firebrands, which are flaming or glowing embers, are generated by burning vegetation or structures. These embers are then lifted by fire plumes and carried downwind, where they can ignite new fires or structures far from the main flame front [1]. Spotting significantly influences fire spread patterns because it acts over much longer distances than heat transfer mechanisms from flames to unburnt vegetation. In addition, it is estimated that more than half of the homes destroyed in WUI fires are due to firebrands [2].

The complex problem of spot wildfires can be broken down into three individual processes:

1. Firebrand generation and thermochemical state: How firebrands are produced and their initial chemical and thermal properties [3].
2. Transport and thermochemical evolution during flight [4].
3. Ignition upon landing: the initiation of smoldering or flaming combustion in a receptive fuel bed after the firebrand lands [1].

Stochastic models have been developed to model firebrand spotting [5, 6, 7]. These models are particularly well-suited for integration into operational fire spread models, providing real‑time capabilities that are highly valuable for operational use and decision‑making in fire management [7]. The stochastic spotting models rely on model parameters that are difficult to estimate and strongly depend on the weather (wind speed, ambient temperature, relative humidity) and the vegetation (type, moisture content).

This project research aims to combine CFD modeling of the spotting process with statistical learning methods to explore how the stochastic model parameters evolve with the most sensitive input data. The CFD simulations of the spotting process will be performed with FireStar3D, a fully physical, three‑dimensional wildfire simulation model, co‑developed at M2P2, the Lebanese American University and Toulon University [8]. A particular fundamental focus of this project will be on developing ignition models for fuel beds by firebrands. This aspect is crucial as it represents the least understood of the three processes previously described in firebrand spotting.

Candidate profile

The desired candidate must hold a PhD related to combustion, fire research, and numerical simulation. The candidate will work in a research laboratory environment and will have to demonstrate autonomy, pragmatism, and a proactive approach.

Contact and candidature: Jean-Louis Consalvi ([e-mail]).

References

[1] A. C. Fernandez-Pello, Wildland fire spot ignition by sparks and firebrands, Fire Safety J. 91 (2017) 2–10.

[2] S. E. Caton, R. S. P. Hakes, D. J. Gorhan, A. Zhou, M. J. Gollner, Review of pathways for building fire spread in the wildland urban interface part i: exposure conditions, Fire Technol. 53 (2017) 429–473.

[4] N. Sardoy, J. L. Consalvi, B. Porterie, A. C. Fernandez-Pello, Modeling transport and combustion of firebrands from burning trees, Combust. Flame 150 (2007) 151–169.

[5] B. Porterie, N. Zekri, J. P. Clerc, J. C. Loraud, Modeling forest fire spread and spotting process with small world networks, Combust. Flame 149 (2007) 63–78.

[6] E. Mastorakos, S. Gkantonas, G. Efstathiou, A. G. b, A hybrid stochastic lagrangian – cellular automata framework for modelling fire propagation in inhomogeneous terrains, Proc. Combust. Inst. 39 (2023) 3853–3862.

[7] G. Efstathiou, S. Gkantonas, A. Giusti, E. Mastorakos, C. M. Foale, R. F. c, Simulation of the december 2021 marshall fire with a hybrid stochastic lagrangian-cellular automata model, Fire Safety J. 138 (2023) 103795.

[8] N. Frangieh, G. Accary, D. Morvan, S. Meradji, O. Bessonov, Wildfires front dynamics: 3d structures and intensity at small and large scales, Combust. Flame 211 (2020) 54–67.