Postdoc: Exascale Multi-Physics CFD for Solar Receivers

CNRS; France; On-site; EUR 36,000 - 48,000; Full time

Job summary

The PROMES-CNRS Laboratory invites applications for a postdoctoral researcher focused on high-fidelity modeling of wall-to-bed heat transfers in high-temperature solar receivers. The project aims to develop advanced simulations bridging experiments and simulations using the LBM-based waLBerla framework with DEM for particles and CFD for gas.

You will model radiative heat transfer between particles and walls, account for complex particle shapes, and enable large-scale simulations on CPU clusters

Qualifications

• PhD in Fluid Mechanics, Computational Engineering, Thermal Sciences, or related field.
• Strong background in numerical methods, multiphase flow, and heat transfer.

Responsibilities

• High-Fidelity Simulations of Solar Receivers: Develop fully resolved simulations with waLBerla (DEM for particles + LBM for fluid).
• Multi-Physics Coupling: Investigate couplings between temperature, velocity, and two-phase flow in asymmetrically heated receivers.
• Scalability and Performance Optimization: Perform large-scale simulations (>10^6 particles) and optimize for exascale computing; collaborate with European partners.

Skills

Numerical methods (DEM, LBM, CFD)

Multiphase flow modeling

Heat transfer (conduction, convection,

High-performance computing (HPC)

Scientific programming (C++, Python, F

waLBerla / CFD tools

Particle-resolved simulations

Education

PhD in Fluid Mechanics, Computational Engineering, Thermal Sciences, or related field

Tools

waLBerla

OpenFOAM

Neptune_CFD

TrioCFD

Job description

The PROMES-CNRS Laboratory invites applications for a postdoctoral researcher focused on high-fidelity modeling of wall-to-bed heat transfers in high-temperature solar receivers. The project aims to develop advanced simulations bridging experiments and simulations using the LBM-based waLBerla framework with DEM for particles and CFD for gas.

You will model radiative heat transfer between particles and walls, account for complex particle shapes, and enable large-scale simulations on CPU clusters