PhD position in engineering on the development of state-to-state plasma-assisted combustion solvers

Doctorat / PhD Profil chercheur : First Stage Researcher (R1) Type de contrat : Temporary Date limite de candidature : 16 Nov 2026 - 10:00 (Europe/Brussels) Date de début : 1 Feb 2027 Programme UE : Horizon Europe - ERC Référence : 101302707 Département : Mechanical Engineering Domaine : Engineering » Other

We offer a full-time PhD position in the framework of the ERC Starting Grant project “Energising the future with clean plasma-based technologies” (ESCAPADE, proposal number 101302707). Plasma-assisted combustion combines non-equilibrium plasma discharges with combustion processes to improve ignition, flame stability, and pollutant emissions for sustainable fuels. While detailed state-to-state plasma-combustion models provide an unprecedented description of the underlying physics, their computational cost remains prohibitively high for multidimensional simulations and industrial applications. This PhD project aims to develop the next generation of computational tools for plasma-assisted combustion by combining detailed state-to-state kinetic modelling, mechanism reduction, and physics-informed machine learning. The researcher will contribute to the development of advanced plasma-assisted combustion simulation codes capable of explicitly modelling plasma discharges, non-equilibrium chemistry, and combustion processes across a wide range of operating conditions. A key objective of the project is the development of novel reduction methodologies for large state-to-state kinetic mechanisms containing hundreds to thousands of species and reactions. Building upon recent advances such as plasma-directed relation graph techniques (P-DRGEP), principal component analysis, and machine-learning-based lumping strategies, the PhD candidate will construct high-fidelity reduced-order models that preserve the essential plasma-combustion interactions while significantly reducing computational costs. The developed methodologies will be integrated into in-house plasma-assisted combustion solvers and validated against experimental benchmark datasets generated by leading international partners of the project. Emphasis will be placed on ammonia/hydrogen combustion systems, which are emerging as key carbon-free fuels for future energy and propulsion applications. The resulting reduced models will enable direct integration of detailed plasma chemistry into advanced CFD environments, including DNS and LES simulations of realistic combustion systems. References: -Rekkas-Ventiris, G., Giuntini, L., Sabia, P., Sorrentino, G., & Bellemans, A. (2026). Numerical investigation of plasma-assisted MILD combustion of ammonia using a hybrid modeling approach. Fuel , 406 , 136751. -Rekkas-Ventiris, G., Gomez, A. D., Deak, N., Kincaid, N., Pepiot, P., Bisetti, F., & Bellemans, A. (2024). A novel machine learning based lumping approach for the reduction of large kinetic mechanisms for plasma-assisted combustion applications. Combustion and Flame , 260 , 113252.

We offer a full-time PhD position in the framework of the ERC Starting Grant project “Energising the future with clean plasma-based technologies” (ESCAPADE, proposal number 101302707).

Plasma-assisted combustion combines non-equilibrium plasma discharges with combustion processes to improve ignition, flame stability, and pollutant emissions for sustainable fuels. While detailed state-to-state plasma-combustion models provide an unprecedented description of the underlying physics, their computational cost remains prohibitively high for multidimensional simulations and industrial applications.

This PhD project aims to develop the next generation of computational tools for plasma-assisted combustion by combining detailed state-to-state kinetic modelling, mechanism reduction, and physics-informed machine learning. The researcher will contribute to the development of advanced plasma-assisted combustion simulation codes capable of explicitly modelling plasma discharges, non-equilibrium chemistry, and combustion processes across a wide range of operating conditions.

A key objective of the project is the development of novel reduction methodologies for large state-to-state kinetic mechanisms containing hundreds to thousands of species and reactions. Building upon recent advances such as plasma-directed relation graph techniques (P-DRGEP), principal component analysis, and machine-learning-based lumping strategies, the PhD candidate will construct high-fidelity reduced-order models that preserve the essential plasma-combustion interactions while significantly reducing computational costs.

The developed methodologies will be integrated into in-house plasma-assisted combustion solvers and validated against experimental benchmark datasets generated by leading international partners of the project. Emphasis will be placed on ammonia/hydrogen combustion systems, which are emerging as key carbon-free fuels for future energy and propulsion applications. The resulting reduced models will enable direct integration of detailed plasma chemistry into advanced CFD environments, including DNS and LES simulations of realistic combustion systems.

References:

-Rekkas-Ventiris, G., Giuntini, L., Sabia, P., Sorrentino, G., & Bellemans, A. (2026). Numerical investigation of plasma-assisted MILD combustion of ammonia using a hybrid modeling approach. Fuel , 406 , 136751.

-Rekkas-Ventiris, G., Gomez, A. D., Deak, N., Kincaid, N., Pepiot, P., Bisetti, F., & Bellemans, A. (2024). A novel machine learning based lumping approach for the reduction of large kinetic mechanisms for plasma-assisted combustion applications. Combustion and Flame , 260 , 113252.

Where to apply Website https://jobs.vub.be/go/EN_ACADEMIC-PERSONNEL-&-RESEARCHERS/3775501/