Direction Mobilité et Systèmes Internship R1040R /2019/n°6
Machine learning applied to Computational Fluid Dynamics Internship duration:
5 to 6 month, from January 2019
Job summary
Among all CFD methods, the RANS (Reynolds-Averaged Navier-Stokes) approach remains the standard in industry and research centers. It allows to model flows in complex geometries involving multi-physical phenomena in timescales compatible with industrial constraints. This method has however a limited accuracy, while industry expects an increasing predictivity. In particular, the RANS approach is based on turbulence models to represent the entire spectrum of the flow turbulence.
These turbulence models rely on strong simplifying hypotheses which reduce their ability to account for all phenomena and their complex interactions in realistic applications. This limits the accuracy of the simulation prediction in terms of flow mixing, thus strongly impacting the quality of the results.
The increasing interest for Machine Learning (ML) techniques has recently triggered pioneering research showing the potential of ML to improve RANS turbulence models based on the knowledge gained from experimental data or Direct Numerical Simulations. In this very promising context, this internship aim is to select, tune and integrate a ML based model in the CFD code CONVERGE, thus bringing a better predictivity of 3D RANS simulations performed at IFPEN (aerodynamic for gas turbines, internal combustion engines, etc).
A first step of bibliography will aim at identifying relevant ML algorithms and at proposing methods to use them in combination with turbulence models in order to extend their domain of application. Tests will then be conducted on 1D academic cases, before considering more complex 2D/3D flows.
This internship will offer you:
- a topic applying machine learning to applied physics, in an acknowledged research center - a supervision by research engineers in CFD and applied mathematics
- it might be continued with a PhD (in collaboration with Argonne National Lab. / University of Illinois at Chicago)
Required skills
- Master's degree or third year of engineering school, with a strong background either in CFD or machine learning, and the willingness to learn the other topic
- Computational and programming skills (LINUX, Python).
- Interest for academic research, fluent in English
