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PhD Position F/M Model reduction of turbulence flows through stochastic closure modeling
Du 1 octobre 2024 au 30 septembre 2027
Inria, Bordeaux, France
Contacts : tommaso.taddei@inria.fr; valentin.resseguier@inrae.fr
Contacts : tommaso.taddei@inria.fr; valentin.resseguier@inrae.fr
The aim of the project is to develop and analyze a model reduction technique for the simulation of parametric incompressible turbulent (chaotic) flows. The point of departure is the stochastic closure modeling procedure ; we shall consider the efficient treatment of parametric boundary conditions and the development of efficient hyper-reduction techniques to handle nonlinear terms.Application to large-scale three-dimensional problems will be pursued by integrating the methodology in the C++ solver Ithaca FV.
Contract type : Fixed-term contract
Level of qualifications required : Graduate degree or equivalent
Fonction : PhD Position
Level of experience : Recently graduated
About the research centre or Inria department
The Inria center at the University of Bordeaux is one of the nine Inria centers in France and has about twenty research teams.. The Inria centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute...
Context
This project is funded by the ANR project RedLUM, in collaboration with Inrae Rennes (Dominique Heitz, Valentin Resseguier), Sant’Anna School of Advanced Studies (Giovanni Stabile), and the companies Scalian DS and Weather Measures. The student will collaborate with the other partners of the team to ultimately devise an efficient simulation framework for agricultural frost control involving wind machines at an agricultural plot scale.
Keywords: model order reduction; data assimilation; stochastic closure modeling.
Assignment
Despite the many recent advances, rapid and reliable approximation of parametric high-Reynolds turbulent flows remains an outstanding task for state-of-the-art model reduction techniques. First, due to the slow decay of the Kolmogorov n-width, projection-based (Galerkin or Petrov-Galerkin) reduced-order models (PROMs) based on proper orthogonal decomposition (POD) need to cope with significantly under-resolved representations of the solution field, which prevent accurate approximations of the full system dynamics. Second, the chaotic nature of the system challenges the predictive abilities of state-ofthe-art projection methods. To address these issues, several authors have proposed stochastic closures of PROMs: the distinctive feature of these approaches is to approximate the deterministic chaotic dynamics through a system of stochastic ordinary differential equations for the dominant POD modes; the prediction of the solution is then obtained through a PROM ensemble forecast
Main activities
The aim of the project is to develop and analyze a model reduction technique for the simulation of parametric incompressible turbulent (chaotic) flows. The point of departure is the stochastic closure modeling procedure ; we shall consider the efficient treatment of parametric boundary conditions and the development of efficient hyper-reduction techniques to handle nonlinear terms.Application to large-scale three-dimensional problems will be pursued by integrating the methodology in the C++ solver Ithaca FV.
Skills
The candidate should have a strong background in numerical methods for PDEs.
Background in computational fluid dynamics, statistical learning, and good programming skills will be highly valued.
Benefits package
Subsidized meals
Partial reimbursement of public transport costs
Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
Possibility of teleworking and flexible organization of working hours
Professional equipment available (videoconferencing, loan of computer equipment, etc.)
Social, cultural and sports events and activities
Access to vocational training
Social security coverage
Remuneration
gross monthly salary : 2100€ (before social security charges and income tax deduction)
Instruction to apply
https://jobs.inria.fr/public/classic/en/offres/2024-07161
Level of qualifications required : Graduate degree or equivalent
Fonction : PhD Position
Level of experience : Recently graduated
About the research centre or Inria department
The Inria center at the University of Bordeaux is one of the nine Inria centers in France and has about twenty research teams.. The Inria centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute...
Context
This project is funded by the ANR project RedLUM, in collaboration with Inrae Rennes (Dominique Heitz, Valentin Resseguier), Sant’Anna School of Advanced Studies (Giovanni Stabile), and the companies Scalian DS and Weather Measures. The student will collaborate with the other partners of the team to ultimately devise an efficient simulation framework for agricultural frost control involving wind machines at an agricultural plot scale.
Keywords: model order reduction; data assimilation; stochastic closure modeling.
Assignment
Despite the many recent advances, rapid and reliable approximation of parametric high-Reynolds turbulent flows remains an outstanding task for state-of-the-art model reduction techniques. First, due to the slow decay of the Kolmogorov n-width, projection-based (Galerkin or Petrov-Galerkin) reduced-order models (PROMs) based on proper orthogonal decomposition (POD) need to cope with significantly under-resolved representations of the solution field, which prevent accurate approximations of the full system dynamics. Second, the chaotic nature of the system challenges the predictive abilities of state-ofthe-art projection methods. To address these issues, several authors have proposed stochastic closures of PROMs: the distinctive feature of these approaches is to approximate the deterministic chaotic dynamics through a system of stochastic ordinary differential equations for the dominant POD modes; the prediction of the solution is then obtained through a PROM ensemble forecast
Main activities
The aim of the project is to develop and analyze a model reduction technique for the simulation of parametric incompressible turbulent (chaotic) flows. The point of departure is the stochastic closure modeling procedure ; we shall consider the efficient treatment of parametric boundary conditions and the development of efficient hyper-reduction techniques to handle nonlinear terms.Application to large-scale three-dimensional problems will be pursued by integrating the methodology in the C++ solver Ithaca FV.
Skills
The candidate should have a strong background in numerical methods for PDEs.
Background in computational fluid dynamics, statistical learning, and good programming skills will be highly valued.
Benefits package
Subsidized meals
Partial reimbursement of public transport costs
Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
Possibility of teleworking and flexible organization of working hours
Professional equipment available (videoconferencing, loan of computer equipment, etc.)
Social, cultural and sports events and activities
Access to vocational training
Social security coverage
Remuneration
gross monthly salary : 2100€ (before social security charges and income tax deduction)
Instruction to apply
https://jobs.inria.fr/public/classic/en/offres/2024-07161