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PhD position in engineering at Vrije Universiteit Brussel (FLOW-BURN) and Université Libre de Bruxelles (ATM-BURN) “Machine learning for turbulent reacting flows”

“Machine learning for turbulent reacting flows”

New technological challenges in combustion science require reliable ignition and flame stabilization in demanding conditions. Some examples are ultra-lean combustion, fuel flexibility (using alternative, low- carbon fuels), supersonic combustion (scramjets), and the active control of thermo-acoustic instabilities. Non-thermal plasma discharges have been proposed as an innovative solution to ensure efficient and stable operation in these particular regimes. Nanosecond discharges are introduced to obtain a more favourable ignition of reactive mixtures, where conventional methods fail. However, an in-depth knowledge of the effects of non-equilibrium plasma on the initiation and stability of these challenging combustion processes is still lacking in the field. The current literature on the subject is incomplete and mostly experimental. The modelling and numerical simulation of plasma discharges and their influence on combustion therefore remains a critical need to understand and support experiments working towards the development of the next combustion technologies.

The numerical simulation of plasma-assisted combustion (PAC) problems remains a key challenge in the community because of (1) the multi-scale nature of the flow: plasma chemistry occurs at the nanosecond time scale and combustion at millisecond scales; (2) non-equilibrium effects: combustion chemistry and transport are coupled with detailed plasma chemistry in non-equilibrium thermodynamics; and (3) the large dimensionality of the mechanisms: hundreds of species are coupled tightly in large and stiff kinetic mechanisms. It is therefore key to develop reliable reduced-order representations of the detailed chemistry in order to alleviate the aforementioned numerical challenges.

Previous work at ULB and VUB has dealt with the development of reduced-order combustion models using modal decomposition (Principal Component Analysis and similar approaches) and approaches based on species and reaction elimination (graph-based methods such as directed relation graph – DRG). The objective of the present doctoral position is to extend this framework to include classification and non-linear regression tools to (1) improve the compression potential of the method and (2) use the reduced-order model to optimise and design new simulations and experiments.

We seek a candidate (PhD level) with a background in one of the following areas:

  1. Turbulence chemistry interactions & combustion modelling. Expertise in Reynolds-Averaged Navier Stokes simulations, Large Eddy Simulation, turbulence modelling, turbulence-chemistry interaction modelling.

  2. Model reduction and feature extraction. Expertise in machine learning techniques, Principal Component Analysis/Proper Orthogonal Decomposition, Vector quantization methods, Neural networks, t-distributed Stochastic Neighbor Embedding.

  3. Optimization and Uncertainty Quantification (with application to turbulent flows). Expertise in surrogate model generation (e.g. using polynomial chaos expansion and Gaussian Process/Kriging), parameter estimation using deterministic and Bayesian approaches, data assimilation, ...

The positions are readily available. The choice will be based on the candidates’ profiles. The duration of the PhD is 4 years.

Description of the team and the environment

A joint PhD position is available.

Aero-Thermo-Mechanics Department of the Faculty of Applied Sciences, Brussels School of Engineering, Université Libre de Bruxelles (ULB).

The department is composed of four professors and approximately 40 researchers. The department is active in many Belgian and European research projects with strong national and international collaborations. The promoter of the project is Professor Alessandro Parente. Prof. Parente’s research activity includes turbulent/chemistry interaction in turbulent combustion and reduced-order models; non- conventional fuels (hydrogen ammonia and other solar fuels) and pollutant formation; novel combustion technologies, e.g. MILD combustion; numerical simulation of atmospheric boundary layer flows; and verification, validation and uncertainty quantification in computational fluid dynamics.

Professor Parente has authored more than 80 journal papers and 2 patents. In January 2015, Prof. Parente founded the BURN group ( The group involves 7 full time professors and around 40 researchers between ULB and VUB and aims at developing a world-class research group in combustion simulations and experimental investigations. This project is aligned with the research goals of the BURN group and will complement work currently be undertaken by ULB and partners.

More on ULB:

Thermo and Fluid Dynamics (FLOW), Department of Mechanical Engineering (MECH), Faculty of Engineering, Vrije Universiteit Brussel (VUB)

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 ( 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:

Offer requirements

  • 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

Selection process

The selection process is based on two steps:

  • Evaluation of the documents provided by the applicant
  • Interview of each candidate having the eligibility requisites (evaluated through the first step). Interviews will be organized remotely.

The list of documents to be provided:

  • Motivation letter (approx. 1 page)

  • Copies of degree and academic transcripts (with grades and rankings)

  • Summary of the Master thesis (approx. 1 page)

  • Short CV including a publication list (if any)

  • Two reference letters from academics.

  • Proof of English language skills (TOEFL, IELTS or equivalent)

  • A short presentation video (2 min max., optional)

Please send your application to: and