Section: Partnerships and Cooperations

International Initiatives

Inria Associate Teams

AQUARIUS associated team is a research project dealing with uncertainty quantification and numerical simulation of high Reynolds number flows. It represents a challenging study demanding accurate and efficient numerical methods. It involves the Inria team BACCHUS and the groups of Pr. Charbel Farhat from the Department of Aeronautics and Astronautics and Pr. G. Iaccarino from the Department of Mechanical Engineering at Stanford University. The first topic concerns the simulation of flows when only partial information about the physics or the simulation conditions (initial conditions, boundary conditions) is available. In particular we are interested in developing methods to be used in complex flows where the uncertainties represented as random variables can have arbitrary probability density functions. The second topic focuses on the accurate and efficient simulation of high Reynolds number flows. Two different approaches are developed (one relying on the XFEM technology, and one on the Discontinuous Enrichment Method (DEM), with the coupling based on Lagrange multipliers). The purpose of the proposed project is twofold : i) to conduct a critical comparison of the approaches of the two groups (Stanford and Inria) on each topic in order to create a synergy which will lead to improving the status of our individual research efforts in these areas ; ii) to apply improved methods to realistic problems in high Reynolds number flow.

A summary of research activities, publications, visits can be found on http://www.stanford.edu/group/uq/aquarius/index3.html

Inria International Partners

Informal International Partners

• von Karman Institute for Fluid Dynamics (Belgium). With Pr. H. Deconinck we work on the design of high order methods, including goal oriented mesh adaptation strategies

• Leeds University, School of Computing : Dr. M.E. Hubbard (as of January 2014 in University of Nottingham, Department of Mathematics). Collaboration on high order schemes for time dependent shallow water flows

• Technical University of Crete, School of Production Engineering & Management : Pr. A.I. Delis. Collaboration on high order schemes for depth averaged free surface flow models, including robust code to code validation

• LEGI, Grenoble : Collaboration with C. Corre, E. Goncalves and G. Balarac on uncertainty quantification methods, multiphase flows, cavitation and turbulence.

• CWI, The Netherlands : Collaboration with J. Witteveen about the Simplex2 methods for robust design optimization.

• University of Trieste : Collaboration with V. Pediroda and L. Parussini concerning robust optimization methods.

• Politecnico di Milano, Aerospace Department (Italy) : Pr. A. Guardone. Collaboration on ALE for complex flows (compressible flows with complex equations of state, free surface flows with moving shorelines), and on robust optimization methods for morphing helicopter blades.

Inria International Labs

JLPC

In the context of the JLPC (Joint Laboratory for Petascale Computing), people involved in the development of graph partitioning algorithms in Scotch collaborate with several US partners (UIUC, Argonne) so as to improve partitioning run time and quality for large scale simulations. Sébastien Fourestier has been attending the Inria-UIUC meeting of last September and has delivered two talks, one regarding Scotch and the other regarding PaMPA .

Inria@SILICONVALLEY

People involved in the development of graph partitioning algorithms in Scotch have a loose collaboration with Sherry Li and her team at Berkeley, regarding sparse matrix reordering techniques.

Participation In other International Programs

Inria-CNPq

In the context of the HOSCAR project jointly funded by Inria and CNPq, coordinated by Stéphane LANTERI on the French side, François Pellegrini and Pierre Ramet have participated in a joint workshop in Petrópolis last September. A collaboration is envisioned regarding parallel graph partitioning algorithms for data placement in the context of big data applications.