Research topics

Advanced Numerical Methods and HPC

(V. Daru, O. Le Maître, F. Lusseyran, L. Mathelin, L. Pastrur, B. Podvin, C. Tenaud)

The increasing capabilities of computational infrastructures constantly provide new opportunities for both numerical and experimental flow studies, by allowing for high fidelity simulations and the storage and analysis of massive numerical or experimental data sets. In order to fully benefit from the opportunities offered by modern computational infrastructures, it is necessary to conduct researches on new numerical methods and algorithms. The topic "Advanced Numerical Methods and HPC" concerns a broad spectrum of challenges, including the development of numerical methods and schemes for more accurate and physically realistic simulations of complex dynamics, the implementation of existing solvers and algorithms on modern parallel computers, and the design of new algorithms anticipating the next generation of computational platforms. The outputs of these researches benefit to the topics of the AERO group (Unsteady Flows, Flow Control and Uncertainty Quantification) as well as to other projects of the ME Department and external partners / collaborators.

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Unsteady Flows

(V. Daru, N. Delprat, Y. Fraigneau, F. Lusseyran, L. Pastur, S. Pellerin, B. Podvin, D. Sciamarella, C. Tenaud)

This theme focuses on the physics of fundamental unsteady flows. It aims at understanding basic phenomena through the characterization of coherent structures for a better understanding and meaningful analysis of the flow dynamics. This is achieved by associating efficient numerical simulations and innovative experimental methods with advanced dynamical analysis tools. Extraction of coherent structures, characterization of their dynamics and the coupling between turbulent structures and unsteady pressure field are mainly studied, within highly unsteady or largely separated flows.

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Flows Manipulation and Control

(B. Noack, F. Lusseyran, S. Pellerin)

We develop new strategies for open- and closed-loop flow control. Starting point are first principle methods, like control-oriented POD Galerkin methods.Increasingly, powerful methods of artificial intelligence / machine learning are employed as game changers for complex flows and turbulence. One avenue is model-free multiple-input multiple-output control, both, for simulations and experiments. Here, the control law is automatically learned in the plant optimizing a given cost. Enablers include genetic programming and reinforcement learning. A second avenue is model-based control, ideally based on dynamic human-interpretable models. The associated challenges for nonlinearity are addressed with a rich arsenal of theories and methods from traditional fluid mechanics to computer science. Our control strategies are tested in a local wind-tunnel with an optically accessible test section Investigated benchmark configurations include cavity flows and the fluidic pinball. Studies with industry-related large-scale experiments are performed  within national and international collaborations. The team has developed an expertise in all aspects of flow control, covering plasma actuators, wind tunnel experiments, sophisticated visualization techniques (time- and space-resolved PIV, Laser Doppler Anemometry), numerical simulations and methodological developments.

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Uncertainty Quantification (UQ)

(O. Le Maître, D. Lucor, L. Mathelin)

Real physical systems are generally incompletely characterized or subjected to irreducible variability, which makes their numerical modeling uncertain. As examples of uncertainty sources, we can mention the geometry, the external forcing and the physical properties of system. As simulation tools are progressing, both in terms of accuracy and complexity, it becomes more and more important to account for these uncertainties in order to fairly assess the validity of model-based numerical predictions, performing for instance global sensitivity analyses to hierarchize the importance of different sources of uncertainty. Uncertainty Quantification methods have been developed at LIMSI over the last decade, relying on stochastic (probabilistic) approaches where uncertainty sources are treated as random input of the numerical model.

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Scientific report

LIMSI in numbers

8 Research Teams
100 Researchers
40 Technicians and Engineers
60 Doctoral Students
70 Trainees

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