Stochastic Downscaling Method : downscaling stochastic method for computational fluid dynamics

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
Higher Edu - Research dev card
  • Creation or important update: 13/02/10
  • Minor correction: 22/03/10
  • Index card author: Antoine Rousseau (Laboratoire Jean Kuntzmann Grenoble)
  • Theme leader: Violaine Louvet (Institut Camille Jordan)
General software features

This software is written in Fortran95. It is aimed to be used as a downscaling module for an existing software, as for example for a numerical weather forecaster. The inputs of SDM are :

  • the volume of interest (3D domain, for example an embedded sub-domain of an existing one)
  • the desired resolution inside this volume
  • the 3D velocity field of the fluid at the boundary of the domain
  • optional : other physical quantities if the model carries more than the 3D velocity (temperature, salinity, etc.)

The outputs are the fluid velocity (and other fields, depending on the inputs), inside the 3D domain, at the desired resolution, together with the turbulent kinetic energy.

Context in which the software is used

This software is a research tool that is made to compare probabilistic Langevin models to traditional techniques of mesh refinement (AMR, etc.). It is developed both by TOSCA and MOISE project-teams at INRIA, and is aimed to be used by the developers, together with a few physicists from the dynamic meteorology lab (LMD, Paris) for its validation on real cases.

Publications related to software

The following publications are associated to SDM:

  • F. Bernardin, M. Bossy, C. Chauvin, J-F. Jabir, A. Rousseau

    Stochastic Lagrangian Method for Downscaling Problems in Computational Fluid Dynamics. Submitted, 2010.

  • F. Bernardin, M. Bossy, C. Chauvin, P. Drobinski, A. Rousseau and T. Salameh.

    Stochastic downscaling method: Application to wind refinement. Stochastic Environmental Research and Risk Assessment, 23(6):851–859, 2009.

  • C. Chauvin, F. Bernardin, M. Bossy and A. Rousseau.

    Wind simulation refinement: some new challenges for parti- cle methods. In Proceedings of ECMI 2008. European European Con- sortium for Mathematics and Industry, 2008.

  • C. Chauvin, S. Hirstoaga, P. Kabelikova, F. Bernardin, M. Bossy and A. Rousseau.

    Solving the uniform density con- straint in a downscaling stochastic model. In Esaim, editor, CEMRACS 2007.

  • Rousseau, F. Bernardin, M. Bossy, P. Drobinski and T. Salameh.

    Stochastic particle method applied to local wind simulation. In IEEE, editor, International Conference on Clean Electrical Power, pages 526–528. Capri, Italy, 2007.