M/F researcher "Prediction of Bistable Flows and Extreme Events through High-Fidelity Simulations and Dynamic Mesh Adaptation"

Job Description

Missions

The candidate's mission will be to better understand the physical mechanisms leading to regime changes and extreme events in turbulent flows, and to use these insights to develop predictive numerical simulation strategies based on dynamic mesh refinement.
A detailed analysis of high-fidelity numerical simulations will be carried out to identify coherent structures, multiscale interactions and precursors leading to bifurcations and extreme events. This analysis will seek to identify the dominant mechanisms controlling the appearance of such phenomena and to identify the regions of the flow which need to be finely resolved numerically.
A second objective will be to develop new dynamic mesh refinement criteria, based on instantaneous quantities rather than converged statistics. This will ultimately result in a methodology allowing to automatically focus the computational effort on the critical spatio-temporal regions, enabling the predictive simulation of bistable flows and extreme events at a controlled numerical cost.

Activity

• Perform and analyse high-fidelity numerical simulations (DNS/LES) of turbulent flows exhibiting bifurcations or bistable behaviour.
• Identify coherent structures, multiscale interactions and precursor signatures associated with rare events and regime transitions.
• Develop and implement new dynamic mesh adaptation criteria based on instantaneous flow quantities.
• Assess and validate predictive simulation strategies.
• Disseminate results through scientific publications and conference presentations.

Your Profil

Skills

Expected skills :
• Strong background in computational fluid dynamics.
• Knowledge of DNS and LES methodologies.
• Scientific programming and high-performance computing.
• Ability to analyse turbulent flow physics.
• Interest in collaborative research.
Desired experience :
• Experience in numerical simulation and/or turbulence modelling.
Required degree :
• PhD in Fluid Mechanics, Physics, Applied Mathematics, 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 context :
A wide number of turbulent flows present complex behaviour characterised by regime transitions, bifurcations, bistable dynamics or high-amplitude rare events. These phenomena often play a decisive role on the performance and the safety of the studied systems, as is the case in hydraulic fluid machinery, aerodynamic systems or geophysical phenomena. Such turbulent flows raise fundamental fluid mechanics questions: which physical mechanisms govern the transitions between different flow states? Are there any precursors allowing to predict a change of regime or the occurrence of an extreme event?
Answering these questions can require high fidelity numerical simulations capable of finely describing the spatio-temporal dynamics of complex turbulent flows. Mesh quality is a key element enabling such simulations. The MOST team (Turbulence Modelling and Simulation) at LEGI laboratory develops state-of-the-art adaptive mesh refinement strategies in the YALES2 code for DNS and LES simulations. These approaches currently rely on statistical quantities, and have already enabled dramatic reduction of computational time when simulating complex flows, while at the same time being largely independent of user choices at mesh creation time.
However, in the case of bistable or rare-event driven flows, these strategies may miss certain essential transitional flow structures, precisely because they are based on averaged quantities. The development of truly predictive numerical simulations of such phenomena thus requires a better understanding of the physical mechanisms driving the observed transitions, as well as the conception of dynamic mesh adaptation criteria capable of capturing such mechanisms in real time.
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 : Guillaume Balarac - MOST team

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

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