Jobs Posted on the Whova Community Board of 17th OpenFOAM Workshop
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Postdoc on screw extruders simulations using OpenFOAM
IFP Energies Nouvelles Numerical Simulations of Twin-Screw Extruders. Application to polymers recycling.
Plastics recycling is a huge environmental challenge for future years, especially as the worldwide production of plastics is increasing continuously and will triple by 2050. Today, less than 10% of plastics are recycled. IFPEN has so started to investigate the chemical recycling of polymers, which requires developing new processes and thus new experimental installations to test them. Among the projected experimental tools, screw extruders will be widely used. The objective of this postdoc is to determine numerically optimal configurations of screw elements, either on single screw or twin-screw configurations. An extrusion line is usually constituted by different types of elements, for transporting, melting, compressing, mixing, degassing, etc. Optimizing the line is a task that can require many trial-errors experiments. The expected main outputs of the simulations tools to develop are: • Heat transfer in the fluid, and eventually in the solid. • Pressure and temperature fields • Forces and moments applied on the screws • Residence time distribution (RTD) using mean age theory (see Liu and Tilton, 2010). RTD is an important parameter for product quality. It should thus be assessed numerically.
The different steps in this modelization project will be: • Address meshing issues using Pointwise or OpenFOAM automatic meshing tools (eg. snappyHexMesh) • Numerical models for polymeric non-Newtonian fluids (rheology and shear laws) • Quasi-steady state approximation of heat and momentum transfers • Fully unsteady flow simulations using deforming mesh • Increasing complexity by adding L/L mixing (with solvent), or G/L (degassing) • Reactive screw extruders
OST, Institute for Computational Engineering ICE Be open for multiphysics simulations using various tools, including OpenFoam. Knowledge of German language is required.
PhD student in Multi-Fidelity Physics-Informed Neural Network for fast CFD solutions
Chalmers University of Technology This project will assess and implement the current state of the Physics-Informed Neural Networks (PINN) technology for solving general non-linear PDEs with application in fluid dynamics and wave equations. The main aim of this project is to exploit the recent developments in machine learning and multi-fidelity deep learning algorithms to accelerate and improve the efficiency of PINN algorithms. The current study will answer the research question of how to efficiently and accurately solve PDEs that describe fluid dynamics and wave evolutions by combining PINN and multi-fidelity algorithms. Do not send any documents by mail! They must be submitted through the application system.
Senior Researcher, Quantum CFD
Engys Italy Engys is currently seeking a Senior Researcher to join our European research and development team in the context of the Horizon Europe research and innovation action project QCFD (Quantum Computational Fluid Dynamic). The main objective of the researcher during the project will be the development of an interface between the QCFD quantum solver and the HELYX/OpenFOAM software library. The four year project offers the opportunity to work alongside leaders in the exciting fields of quantum computing and computational fluid dynamics, and, if successful, will undoubtedly be a game changer for the CAE industry.
Full job listing can be found on the Engys website.
Computational Scientist in Extreme Scale Computing
UKRI STFC Hartree Centre Your responsibilities include:
Developing scientific applications and software technologies for extreme-scale and energy-efficient computing. Developing mathematical modelling for complex scientific problems. Designing algorithms to improve the performance of scientific applications. Researching digital and post-digital computer architectures for science. Advancing extreme-scale scientific data management, analysis, and machine-learning. Developing next-generation machine learning and AI approaches for science. Developing large-scale visualization and analytics technologies; and managing scientific data in distributed environments Contributing and writing project applications for funding from national and international competitions Project management across a range of project sizes and types, typically involving members from other groups within Hartree (including Research Software Engineering, HPC, AI and Data Science) Make site visits to commercial and academic partners. Representing Hartree Centre at external and international meetings and scientific conferences to present the outcome of the research