Simulation and Data Lab Computational Fluid Dynamics

Bergmann received his B.Sc. in Chemical Engineering from the University of Iceland in 2021 and his M.Sc. in Advanced Chemical Engineering from the University of Leeds in 2022. There his final project used the spectral element, Direct Numeral Simulations code, Nek5000 coupled with the Immersed Boundary method to study solid-particle collision dynamics in turbulent flows. Currently, he is working on a Ph.D. degree in Mechanical Engineering at the University of Iceland, which he started in the autumn/fall of 2023. His research focuses on turbulence modelling using, CaNS (Canonical Navier-Stokes, a fluid simulation code designed for massive-parallelisation/parallelization). His interest in CFD stems from industrial process design and modelling where he has experience from beer brewing and bio-fuel production.

Turbulent multiphase flows abound in the environment and industry. These flows are three-dimensional, chaotic and multiscale by nature, giving rise to remarkable and at times dramatic phenomena. Direct Numerical Simulations of turbulent flows are first-principles simulations that resolve all spatial and temporal scales of the system. Simulating turbulent multiphase flows poses the additional challenge of imposing appropriate kinematic/dynamic compatibility conditions at fluid-fluid or particle-fluid interfaces, while the interface itself moves and deforms with the flow. These challenges make these first-principles simulations difficult, resulting in an unbalance between the limited fundamental knowledge of the physics of these flows, and their prevalent nature. Our research revolves precisely around the development of numerical methods to tackle these flows with high-fidelity, and their exploitation using HPC to unveil the underlying physics of these complex systems.

Development and implementation of numerical methods for partial differential equations with applications in Fluid Dynamics, Heat Transfer and Bio Engineering is my main research focus. Those applications call for governing equations that are often nonlinear and may have an irregular interface. The location of the interface needs to be accurately known to correctly enforce the boundary conditions at it. This may be a challenge, especially if the interface is moving. These problems generally have multiple scales, meaning that the difference between the smallest scale that needs to be resolved and the largest scale is vast. This calls for immense computational power where HPC comes to the rescue.

I received my B.Sc. degree in Mechanical Engineering (Heat & Fluids) from South Tehran Branch of Islamic Azad University, Tehran, Iran in 2013 and M.Sc. degree from the University of Sistan and Baluchestan, Zahedan, Iran in 2018. My M.Sc. thesis dealt with CFD simulation of Phase Change Materials in a shell-and-tube system, and my Ph.D. project is on CFD simulation of multiphase flow in geothermal wells.

Reza Hassanian received the B.Sc. and M.Sc. degrees in mechanical engineering from the Chamran University and the Technical University, Iran, in 2009 and 2012, respectively. He participated in Lagrangian particle tracking (LPT) application in straining turbulence studies performed in Experimental technique at the Laboratory for Fundamental Turbulence Research (LFTR) funded by the Icelandic Center for Research (Rannís) at Reykjavik University. As well He was a member of the research team in wind turbine blade erosion studies and Constant Temperature Anemometry (CTA) application at the wind tunnel in wind energy research at Reykjavik University. He is a member of the ‘‘Simulation and Data Lab Computational Fluid Dynamics’’ (SimDataLab CFD) research group at the University of Iceland, Iceland. He is leading SimDataLab CFD on RAISE and EuroCC projects at European projects Horizon 2020. He is currently pursuing a Ph.D. degree in computational engineering at the University of Iceland, Iceland. His research interest is mainly in turbulence flow, computational fluid dynamics applications, and machine learning methods. His particular focus on Machine Learning and High-Performance Computing (HPC) for computational fluid dynamics applications.

Dr.-Ing. Andreas Lintermann is a postdoctoral researcher at Forschungszentrum Jülich, a member of the Helmholtz Association, Germany. At the Jülich Supercomputing Centre (JSC), he is heading the Simulation and Data Laboratory ‘‘Highly Scalable Fluids & Solids Engineering’’, which is also part of the Jülich Aachen Research Alliance Center for Simulation and Data Science (JARA-CSD). His research focuses, amongst others, on lattice-Boltzmann methods, artificial intelligence, high-performance computing, heterogeneous computing on modular supercomputing architectures, high-scaling meshing methods, efficient multi-physics coupling strategies, and bio-fluidmechanical analyses of respiratory diseases. He is the coordinator of the European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE).