A one-year (renewable) postdoc position is available at Inria Bordeaux, in the Cardamom team.
Understanding the dynamic of sea ice is of major importance in a global warming context. In particular, the withdrawal and thinning of Arctic sea ice is well documented, which has major environmental consequences as well as consequences on the ice dynamic itself. The aim of the project is to enhance the computational capabilities of a sea ice dynamic model by leveraging mesh adaptation methods.
The job is part of a collaboration between mathematicians from Bordeaux, France and geophysicists from Grenoble France.
The position is hosted in Bordeaux (France), but travels to Grenoble are expected.
We consider the sea ice model neXtSIM, developed at Nansen Environmental and Remote Sensing Center (Bergen, Norway) and Institute of Environmental Geosciences (Grenoble, France), a novel model aimed at modelling complex sea ice dynamic in a large range of scales, from local short-term predictions to global climate prediction simulations. The specificity of \nextsim is the use of a purely Lagrangian advection formalism on fully unstructured meshes coupled with a novel rheologic frameowrk, that has given promising results. Such a Lagrangian approach results in strongly deformed meshes over time, in particular in the vicinity of cracks due to the ice displacement. A remeshing step is thus necessary to locally replace stretched or invalid mesh elements and restore the quality of the mesh. However, unlike the rest of the code that was parallelised recently for distributed memory architectures using MPI, the remeshing stage remains sequential, thus strongly impacting the performance of the code. Besides, the current remeshing does not yet take advantage of modern anisotropic mesh adaptation techniques, that aim at optimising the size and orientation of the mesh elements to minimise a certain numerical error estimate, and makes it possible to reduce the computationnal cost while increasing the accuracy. The goal of the collaboration is to leverage these methods to accelerate simulations to be able to repeat easily large-scale high-resolution simulations and study the ice trajectories statistically.
The role of the postdoc will be to study theoretical, algorithmic and parallel implementation aspects of anisotropic mesh adaptation in neXtSIM. The main identified tasks are :
- Study error estimates for sea ice dynamics. The litterature contains many numerical error estimates to drive mesh adaptation in more or less specific contexts. Existing error models will have to be benchmarked, and a novel estimate derived if need be.
- Design and implement a mesh adaption algorithm in neXtSIM. This includes the development of a parallel version of remshing software MMG2D. MMG is an open-source remeshing software suite supported by an industrial consortium hosted at Inria. The 3D version is already parallelised and will be used as a basis for the parallelisation of the 2D code.
- Demonstrate the efficiency of the approach by running large scale simulations.
For a better knowledge of the proposed research subject :
F. Alauzet and A. Loseille. A decade of progress on anisotropic mesh adaptation for computational fluid dynamics. Computer-Aided Design, 2016.
L. Cirrottola, A. Froehly. Parallel unstructured mesh adaptation using iterative remeshing and repartitioning. Inria Research Report RR-9307, 2019.
P. Rampal, S. Bouillon, E. Ólason, and M. Morlighem. neXtSIM: a new lagrangian sea ice model. The Cryosphere, 2016.
A. Samaké, P. Rampal, S. Bouillon, and E. Ólason. Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice. Journal of Computational Physics, 2017.
Proficiency in numerical methods for fluids on unstructured grids is expected. Knowledge in error estimation and unstructured mesh generation/adaptation will be greatly appreciated.
Proficiency in scientific programming and skills in parallel computing is required.
Languages : Proficiency in English is a must.
Relational skills : ready to work in a truly international environnement, within a multicultural team, and to interact with collaborators from different horizons (applied math, engineering, computer science).
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