PhD Position in the Development of State-to-State Plasma-Assisted Combustion Solvers

PhD Position in the Development of State-to-State Plasma-Assisted Combustion Solvers

Category: PhD

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- Working at the VUB

For more than 50 years, the Vrije Universiteit Brussel has stood for freedom, equality and solidarity, and this is very much alive on our campuses among students and staff alike.

At the VUB, you will find a diverse collection of personalities: innovators pur sang, but above all people who are 100% their authentic selves. With some 4,000 employees, we are the largest Dutch-speaking employer, in the private sector, in Brussels; an international city with which we are only too happy to connect and where (around) our 4 campuses are located.

Add to this our principle of free research
- in which self-reflection, a critical attitude and an open, creative mind around scientific and social issues are central
- and you have a university that is fundamentally groundbreaking and pioneering in education and research. In short: the VUB all over again.

Moreover, the VUB is a member of EUTOPIA, an alliance of like-minded European universities, all ready to reinvent themselves.

The Faculty of Engineering, Department Applied Mechanics, Research Group Thermodynamics and Fluid Mechanics is looking for a PhD-student with a doctoral grant.

More concretely your work package, for the preparation of a doctorate, contains:

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 (PAC) offers a promising pathway towards cleaner and more efficient combustion of sustainable fuels. However, progress towards industrial deployment is limited by the absence of predictive computational tools capable of capturing the complex interactions between plasma discharges, chemistry, and flame dynamics. ESCAPADE addresses this challenge by developing an innovative multi-physics simulation framework that combines first-principles modeling, machine learning, reduced-order techniques, and computational fluid dynamics. The project will deliver the next generation of predictive tools for the design and optimization of low-emission combustion systems operating with both sustainable and conventional fuels.

The FLOW research group at the VUB has a strong expertise in both fundamental and applied research topics related to the modelling of thermal-fluid systems, with applications in aeronautics (unsteady aerodynamics, flow control), aerospace (atmospheric re-entry, aerospace plasmas) and energy (wind energy, bioenergy, hydrogen, heat networks, combustion).

Since 2021, FLOW joined forces with the ATM research group at ULB, working in similar fields of expertise, to create the VUB-ULB Joint Research Group BRITE (Brussels Institute for Thermal-fluid Systems and clean Energy, https://brite-research.be ).

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