M/F researcher "Goal-Oriented Multi-Fidelity Strategies for the Prediction of Turbulent Flows"

Job Description

Missions

The candidate's mission will be to develop a methodology capable of automatically selecting the simulation strategy best suited to a given prediction objective, while optimizing the trade-off between accuracy and computational cost.
The work will build on multi-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical simplification. Particular attention will be paid to uncertainty quantification and to the definition of criteria that assess the contribution of each simulation to the improvement of the prediction.
The developments will also aim to extend existing approaches, which are often restricted to integrated quantities, to the reconstruction of complete statistical fields. The ultimate goal is to design a goal-oriented strategy capable of determining, for a given quantity of interest (efficiency, forces, losses, fluctuations, etc.), which simulations should be performed and at what fidelity level in order to achieve the desired accuracy at minimal cost.

Activity

• Study the interactions between different modeling levels (RANS, RANS/LES hybrid approaches, LES).
• Develop multi-fidelity methodologies that enable the effective combination of simulations with different levels of cost and accuracy.
• Quantify the uncertainties associated with different levels of accuracy.
• Develop criteria for evaluating the contribution of each simulation to improving the prediction.
• Extend existing approaches to the reconstruction of complete statistical fields, rather than just integrated quantities.
• Develop strategies for automatically selecting simulations to run based on a given prediction objective.
• Implement and validate these developments in the YALES2 code.
• Utilize high-performance computing resources to run the simulations.
• Disseminate research results through peer-reviewed publications and presentations at international conferences.

Your Profil

Skills

Expected skills :
• Strong background in computational fluid dynamics.
• Knowledge of RANS and/or LES methodologies.
• Interest in uncertainty quantification and multi-fidelity modeling.
• Ability to analyze and exploit large simulation datasets.
• Ability to work in a collaborative multidisciplinary environment.
Desired experience :
• Experience in computational fluid dynamics.
• Experience in modeling, uncertainty quantification, or statistical methods.
• Experience in data science or machine learning is considered an asset.
• Experience with high-performance computing is appreciated.
Required degree :
• PhD in Fluid Mechanics, Physics, Applied Mathematics, Scientific Computing, or a related field.
Application package :
• a detailed curriculum vitae and a letter of motivation.

Your Work Environment

Joint Research Unit (UMR 5519) of the Centre National de la Recherche Scientifique (CNRS), the Institut National Polytechnique de Grenoble (Grenoble INP) and the University Grenoble-Alpes (UGA). LEGI carries out a wide range of research activities with a common ground: fluid mechanics and related transport phenomena.
Scientific Background
Numerical simulation of turbulent flows always involves a trade-off between accuracy and computational cost. Depending on the objectives, different modeling approaches can be employed, including RANS simulations, hybrid RANS/LES methods, Large-Eddy Simulation (LES), and various geometric or physical simplifications. Each of these approaches has its own level of fidelity, computational cost, and range of applicability.
In many practical applications, several simulation strategies may be used to address the same scientific or industrial question. For example, in rotor/stator configurations, one may rely on RANS simulations with mixing planes, hybrid RANS/LES approaches, LES on simplified geometries, or LES of the full geometry. In practice, the choice of the most appropriate strategy is still often based on user experience rather than on a quantitative methodology.
For several years, the MOST team at LEGI has been developing advanced simulation methods within the YALES2 framework, covering a wide spectrum from RANS approaches to high-fidelity LES with automatic mesh adaptation. In parallel, recent work has led to the development of multi-fidelity modeling strategies that combine numerous low-cost simulations with a smaller number of more accurate but computationally expensive simulations.
The recruited person will be assigned to the MOST team. The research activities of the MOST (Modelling and Simulation of Turbulence) team focus on the numerical prediction of turbulent and multiphase flows with a broad range of objectives from fundamental understanding of flow properties to technologies optimization. The research team has the ambition to address all the needed scientific fields to understand turbulent and multiphase flows from simulation: numerical methods, turbulence models, physics of fluids, flow control...
The main objective is to develop numerical tools to efficiently predict and to deeply understand flows in more and more physically and geometrically complex configurations. This activity is inherently multidisciplinary with strong collaborations with other scientific fields, as applied mathematics or statistical physics. Fluid mechanics is ubiquitous in geophysical and industrial applications. Better understanding of flows will help to address major challenges to deal with new energy and environmental constraints. Collaborations with experts in geosciences and in renewable energy development have been set-up to respond to these societal issues.
Scientific supervisor: Thomas Berthelon - MOST team (thomas.berthelon@univ-grenoble-alpes.fr)

Constraints and risks

No risk identified.

Compensation and benefits

Compensation

between 3041,58€ and 4216,70€ gross monthly depending on experience

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions