Team Tosca

Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Other Grants and Activities

Section: Contracts and Grants with Industry

Keywords : down-scaling methods, particle in mesh models, 3D-Navier-Stokes equations.

Collaboration with ADEME: local modeling for the wind velocity

Participants : Mireille Bossy, Jean-François Jabir.

Started in 2005, our joint collaboration with the Laboratoire de Météorologie Dynamique (Université Paris 6, École Polytechnique, École Normale Supérieure) is funded by the French Environment and Energy Management Agency (ADEME) and concerns the modeling and the simulation of local wind energy resources. We collaborate with É. Peirano (ADEME), P. Drobinski and T. Salameh (LMD). The second phase of this collaboration has just begun (October 2007) and include as partners A. Rousseau (MOISE Inria Grenoble – Rhône-Alpes) and F. Bernardin (CETE Clermont-Ferrand). We investigate a new method for the numerical simulation of the wind at small scales. Thanks to boundary data provided at large scales by the weather forecasting code MM5 , we propose a Langevin model that rules the behavior of stochastic particles. This model called SDM (Stochastic Downscaling Method) is adapted from previous works introduced by S.B. Pope [63] and has been presented in [Oops!] , [Oops!] .

This year we have finished a beta version of our SDM code and we have started the validation phase of our simulation on a simple meteorological framework using observations from measurements campaigns, namely the campaign FETCH that took place in 1998 in Southern France and runs of the MM5 solver with two different resolutions. The first mesh will be coarse, and will be devoted to feed our stochastic model. The second mesh will be finer, and we hope that our simulations will provide comparable results, with a smaller computational cost (CPU time) than the one corresponding to the same resolution, performed with a classical refinement of MM5 . The very first results are promising.


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