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CFD : Development of numerical methods in a spectral difference code and application to DNS

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Required Education : PhD
Start date : 1 October 2019
Mission duration : 12 mois


The CFD team develops and maintains the most advanced codes for fluid mechanics dedicated to industrial problems. We currently work with ONERA, Airbus, SAFRAN (Snecma / Turbomeca) and EDF.

Maintaining our expertise on CFD needs to propose new solutions or to adapt solutions published in the literature to industrial meshes / configurations. It is clear that turbulence-averaged simulations (RANS and URANS approaches) with second-order schemes are dedicated to design but they are not well-adapted to compute off-design configurations. Off-design configurations need unsteady computations and high order (spatial and temporal) schemes to propagate flow physics inside the computational domain. Aeroacoustic, vortex-dominated flow and some turbulence-driven phenomena are examples of application of high order schemes.


The spectral difference approach is a high-order method to discretize equations on unstructured grids. It is based on a polynomial reconstruction of data inside each mesh cell (Spectral Approach). Conservative fields are not assumed to be continuous at the mesh interface and one or several Riemann problems need to be computed on an interface.

We are involved in the European project HIFITURB. The goal is to combine advanced and efficient high-order numerical schemes (HOMs) with innovative approaches for LES and DNS in order to resolve all relevant flow features on several tens of thousands of processors. This will pave the way to get close to a full LES/DNS solution for levels of 1 to 10 billion degrees-of-freedom (DOF) not exceeding turn-around times of one to two days.

In this project, the candidate will an H/P adaptation method and implicit temporal integration schemes (Runge-Kutta, exponential) in the solver JAGUAR and these methods will be applied to direct numerical simulation.

For this position, we look for someone with the following abilities:

  • Ability to work in a team
  • Ability to communicate (expression of needs, presentation of work, etc.)
  • Initiative.
  • Ability to work independently in codes (use of development tools, compilers, programming languages, etc.)
  • Good knowledge of fortran and programming
  • Good knowledge of applied mathematics in CFD
  • Knowledge of OpenMP and MPI parallelism.
  • Knowledge of version management (git).


Jean-François BOUSSUGE / boussuge@cerfacs.fr