“Reduction of detailed plasma kinetics using data-driven methods”
The numerical simulation of non-equilibrium plasma flows remains a challenge for a number of applications in aerospace engineering. Two examples that are currently being investigated in the FLOW research group are the simulation of the plasma surrounding spacecraft during atmospheric re-entry and the modelling of non-equilibrium plasma discharges to enhance combustion processes in supersonic flight.
The non-equilibrium plasma chemistry is modelled using detailed kinetic mechanisms that are used in CFD codes. These mechanisms contain hundreds of species and thousands of reactions in multi-temperature models. From a practical perspective, integrating large kinetics mechanisms is computationally burdensome due to the temporal stiffness of the non- linear plasma dynamics and the memory requirements associated with the high number of species. In order to alleviate computational costs, a dimensionality reduction approach should be considered.
In this project, we want to further investigate the applicability of graph-based techniques, such as Directed Relation Graph (DRG) for plasma flows, in combination with Uncertainty Quantification (UQ) and optimization techniques. The aim is to see if we can transfer the expertise, we have in combustion to plasma kinetics. The end objective of this PhD project is to develop a reliable graph-based technique for plasma flows, where the uncertainties in the reaction kinetics are identified and used to optimize the resulting reduced model.
We seek a candidate with a strong background in the following fields:
Modelling of reactive flows
Description of the team and the environment
The activities of the FLOW team at VUB are centered around thermo and fluid dynamics for various engineering applications ranging from sustainable energy, to aeronautics and
aerospace, robust optimization and data-driven modelling (https://flow.research.vub.be/en). Prof. Aurélie Bellemans is working on the integration of novel data-driven concepts in the field of thermo-fluids (i.e., thermodynamics, fluid mechanics, heat transfer and combustion) to understand and optimize challenging engineering applications in aerospace and renewable energy. The overarching topic of her research is to develop data-driven feature-extraction methods and build advanced surrogates using machine-learning algorithms.More on VUB: https://www.vub.be
This PhD project is a joint-PhD project between VUB and UC Louvain.
The Institute of Mechanics, Materials, and Civil Engineering (iMMC) of the Université catholique de Louvain (UCLouvain) is a leading international center for research in engineering. Its core strengths span several of the major engineering disciplines encompassing energy, thermodynamics, chemical and environmental engineering, materials and processes, structural engineering, geomechanics, manufacturing, fluid mechanics, mechatronics, robotics, biomechanics, numerical and computational sciences. Prof. Francesco Contino focuses his research effort on CFD simulations of internal combustion engines, pollutant emissions of vehicles, and the use of non-conventional fuels. He has developed chemistry reduction methods that enable the use of more detailed mechanisms in CFD simulations, hence providing a better description of the combustion. His expertise also extends to UQ and robust optimization.
More on UCLouvain: https://uclouvain.be/en/index.html
A Master of Science in engineering, chemistry, physics, or applied mathematics with a focus on either chemical kinetics, fluid dynamics, or plasma physics.
A qualification equivalent to first-class honors degree is preferred
Experience in numerical methods, excellent computational skills, expertise in programming (Python, Fortran, C++)
Interest in CFD programming, chemical kinetics, data-driven modeling, optimization
English language is mandatory
The selection process is based on two steps:
The list of documents to be provided:
Letter of motivation (approx. 1 page)
Copies of degree and academic transcripts (with grades and rankings)
Summary of the master’s thesis (approx. 1 page)
Short CV including a publication list (if any)
Two reference letters from academics.
Proof of English language skills
A short video in which you present yourself and motivate your application (2 min max.)
Please send your application through Euraxess or to:
Aurelie.email@example.com and firstname.lastname@example.org