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Machine learning for turbulent reacting flows

Machine learning for turbulent reacting flows

Belgium 20 Feb 2021
Université libre de Bruxelles (ULB)

Université libre de Bruxelles (ULB)

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OPPORTUNITY DETAILS

Total reward
0 $
State University
Area
Host Country
Deadline
20 Feb 2021
Study level
Opportunity type
PhD
Specialities
Opportunity funding
Not funding
Eligible Countries
This opportunity is destined for all countries
Eligible Region
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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:

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 (http://burn-research.be). 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: https://www.ulb.be/en/ulb-homepage

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 (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

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