Latest Results Gauss Centre for Supercomputing e.V.

LATEST RESEARCH RESULTS

Find out about the latest simulation projects run on the GCS supercomputers. For a complete overview of research projects, sorted by scientific fields, please choose from the list in the right column.

Astrophysics

Principal Investigator: Anna Therese Phoebe Schauer, Zentrum für Astronomie, Universität Heidelberg

HPC Platform used: SuperMUC and SuperMUC-NG of LRZ

Local Project ID: pr53ka

Before the first stars formed more than 13 billion years ago, the gas of the Universe consisted of hydrogen, helium, and lithium only. Elements necessary for life, eg carbon or oxygen, are produced by stars, and it is of fundamental importance to understand how the first stars formed. With a large allocation on SuperMUC and SuperMUC-NG, state-of-the-art numerical simulations were performed to mimic these first star formation regions. In these high-resolution simulations, two effects – a so-called Lyman-Werner background and streaming velocities – that delay star formation globally were included. It could be demonstrated for the first time that the combination of both effects results in an even more delayed formation of the first stars.

Computational and Scientific Engineering

Principal Investigator: Markus Uhlmann, Institute for Hydromechanics, Karlsruhe Institute of Technology (KIT)

HPC Platform used: Hazel Hen and Hawk of HLRS

Local Project ID: GCS-PASC

The quality of surface water typically depends upon a complex interplay between physical, chemical and biological factors which are far from being completely understood. Most practical water quality predictions for rivers or streams rely on various simplifications esp. with regards to the turbulent flow conditions. This project aims at pushing the modeling boundary further by performing massively-parallel computer simulations which resolve all scales of hydrodynamic turbulence in river-like flows, the micro-scale flow around rigid, mobile particles, and the concentration field of suspended bacteria. The data obtained helps quantifying the shortcomings of simpler currently used prediction models and will contribute to their improvement.

Astrophysics

Principal Investigator: Maarit Käpylä, Aalto University, Department of Computer Science, Astroinformatics Group, Finland, and Max Planck Institute for Solar System Research, SOLSTAR group, Germany

HPC Platform used: SuperMUC-NG of LRZ

Local Project ID: pn98qu

To understand solar and stellar magnetic field evolution combining local and global numerical modelling with long-term observations is a challenging task: even with state-of-the-art computational methods and resources, the stellar parameter regime remains unattainable. Our goal is to relax some approximations, in order to simulate more realistic systems, and try to connect the results with theoretical predictions and state-of-the-art observations. Higher resolution runs undertaken in this project will bring us into an even more turbulent regime, in which we will be able to study, for the first time, the interaction of small- and large-scale dynamos in a quantitative way.

Astrophysics

Principal Investigator: Sebastiano Bernuzzi, Bernd Brügmann, Friedrich-Schiller-Universität Jena

HPC Platform used: SuperMUC and SuperMUC-NG of LRZ

Local Project ID: pn56zo

The project developed multiscale 3+1D simulations of binary neutron mergers in numerical general relativity for applications to multi-messenger astrophysics. It focused on two aspects: (i) the production of high-quality gravitational waveforms suitable for template design and data analysis, and (ii) the investigation of merger remnants and ejecta with sophisticated microphysics, magnetic-fields induced turbulent viscosity and neutrino transport schemes for the interpretation of kilonova signals. The simulations led to several breakthroughs in the first-principles modeling of gravitational-wave and electromagnetic signal, with direct application to LIGO-Virgo's GW170817 and counterparts observations. All data products are publicly released.

Computational and Scientific Engineering

Principal Investigator: Klaus Hannemann, Institute of Aerodynamics and Flow Technology, Spacecraft Department. German Aerospace Center (DLR), Göttingen

HPC Platform used: SuperMUC and SuperMUC-NG of LRZ

Local Project ID: pr27ji

Combustion instabilities in rocket thrust chambers pose a serious risk for the development of future launch vehicles as they can’t be predicted reliably by numerical simulations. To better understand the interaction between the flames and acoustic waves inside a combustion chamber, this project numerically investigates the flame response to forced transversal excitation by using Detached-Eddy simulations. In a first step, the eigenmodes of a model combustion chamber are determined from an impulse-response and they are compared to experimental results. We then investigate a specific mode coupling scenario in which the oxygen injector longitudinal eigenmode is adjusted to match the dominant transversal combustion chamber eigenmode.

Materials Science and Chemistry

Principal Investigator: Mahdi Ghorbani-Asl, Institute of Ion Beam Physics and Materials Research Helmholtz-Zentrum Dresden-Rossendorf

HPC Platform used: Hazel Hen of HLRS

Local Project ID: PP18184458

Focused ion beams can be used to pattern 2D materials and ultimately to create arrays of nanoscale pores in atomically thin membranes for various technologies such as DNA sequencing, water purification and separation of chemical species. Among 2D materials, transition metal dichalcogenides, and specifically, MoS2, are of particular interest due to their spectacular physical properties, which make them intriguing candidates for various electronic, optical and energy conversion applications. Findings achieved by running large-scale molecular dynamics simulations to study the response of MoS2 monolayer to cluster ion irradiation suggest new opportunities for the creation of 2D nanoporous membranes with an atomically thin nature.

Computational and Scientific Engineering

Principal Investigator: Romuald Skoda, Lehrstuhl für Hydraulische Strömungsmaschinen, Ruhr-Universität Bochum

HPC Platform used: JUWELS of JSC

Local Project ID: chbo46, chbo48

While for the design point operation of centrifugal pumps an essentially steady flow field is present, the flow field gets increasingly unsteady towards off-design operation. Particular pump types as e.g. single-blade or positive displace pumps show a high unsteadiness even in the design point operation. Simulation results for the highly unsteady and turbulent flow in a centrifugal pump are presented. For statistical turbulence models an a-priori averaged turbulence spectrum is assumed, and limitations of these state-of-the-art models are discussed. Since the computational effort of a scale-resolving Large-Eddy-Simulation is tremendous, the potential of scale-adaptive turbulence models is highlighted.

Materials Science and Chemistry

Principal Investigator: Gerd Steinle-Neumann, Bayerisches Geoinstitut, Universität Bayreuth

HPC Platform used: SuperMUC-NG

Local Project ID: pn34wi

Metal hydrides have become of great scientific interest as high-temperature superconducting materials at high pressure, with hydrogen-hydrogen interactions suspected as critical in this behavior. Here, nuclear magnetic resonance experiments and electronic structure calculations are combined to explore the compression behavior of FeH and Cu2H, and results show that within the hydrides a connected hydrogen network forms at significantly larger H-H distances than previously assumed. The network leads to an increased contribution of hydrogen electrons to metallic conduction, and seems to induce a significantly enhanced diffusion of protons.

Computational and Scientific Engineering

Principal Investigator: Geert Brethouwer, Department of Engineering Mechanics, KTH, Stockholm, Sweden

HPC Platform used: JUWELS of JSC

Local Project ID: PRA108

Flows over the curved surface of wings, cars, turbine blades in gas turbines and impeller blades in pumps have curved streamlines. The influence of streamline curvature on flows, drag and also heat transfer in flows is substantial to large. However, engineering models have difficulties in correctly predicting flows over curved surfaces and our knowledge on streamline curvature influences on flows is still limited. In this project, turbulent flows in moderately to strongly curved channels are studied by highly accurate, large-scale numerical simulations fully resolving the turbulent fluid motions. These give important insights into streamline curvature influences on flows, and produce data that form the basis for better engineering models.

Computational and Scientific Engineering

Principal Investigator: Johannes Schemmel, Kirchhoff Institute for Physics, University of Heidelberg (Germany)

HPC Platform used: JUWELS of JSC

Local Project ID: chhd34

Impressive progress has recently been made in machine learning where learning capabilities at (super-)human level can now be produced in non-spiking artificial neural networks. A critical challenge for machine learning is the large number of samples required for training. This project investigated new high-throughput methods across various domains for biologically based spiking neuronal networks. Sub-projects explored tools and learning algorithms to study and enhance learning performance in biological neural networks and to equip variants of data driven models with fast learning capabilities. Applications of these learning techniques in neuromorphic hardware and design for their future application in neurorobotics were also included.

Computational and Scientific Engineering

Principal Investigator: Klaus Hannemann, Spacecraft Department, Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR)

HPC Platform used: SuperMUC and SuperMUC-NG of LRZ

Local Project ID: pr62po

The aerodynamics of generic space launch vehicles, in particular the flow field at the bottom of the vehicle, at transonic conditions are investigated  numerically using hybrid RANS-LES methods. The focus of the project is the investigation of the impact of hot plumes and hot walls on the flow field. It is found that both higher plume velocities and higher wall temperatures shift the reattachment location downstream, leading to a stronger interaction of shear layer and plume. An additional contribution in the pressure spectral content is observed that exhibits a symmetric pressure footprint. The increased wall temperature leads to reduced radial forces on the nozzle structure due to a slower development of turbulent structures.

Elementary Particle Physics

Principal Investigator: Dénes Sexty, Bergische Universität Wuppertal, IAS/JSC Forschungszenturm Jülich

HPC Platform used: JUQUEEN and JUWELS of JSC

Local Project ID: chwu32

At high temperatures the nuclear matter melts into a plasma state. This phase transition is expected to have a “critical point” for systems which have increasingly more protons than antiprotons. The search for this elusive critical point on the QCD phase diagram is one of the greatest challenges in today’s high energy physics, both in theory and in experiment. The calculations of the theory at non-zero densities in supercomputers are hampered by the sign-problem. In this project multiple research tracks were pursued and the methods that deal with the sign-problem and search for signals of the critical point on the phase diagram were developed.

Computational and Scientific Engineering

Principal Investigator: Sylvain Laizet, Imperial College London, United Kingdom

HPC Platform used: Hazel Hen of HLRS

Local Project ID: PRACE4381

The need to reduce the skin-friction drag of aerodynamic vehicles is of paramount importance. Nominally 50% of the total energy consumption of an aircraft or high-speed train is due to skin-friction drag. Reducing skin-friction drag reduces fuel consumption and transport emissions, leading to vast economic savings and wider health and environmental benefits. In this project, wall-normal blowing is combined with a Bayesian Optimisation framework in order to find the optimal parameters to generate net energy savings over a turbulent boundary layer. It is found that wall-normal blowing with amplitudes of less than 1% of the freestream velocity of the boundary layer can generate a drag reduction of up to 80% with up to 5% of energy saving.

Elementary Particle Physics

Principal Investigator: Jeremy Green, Theoretical Physics Department, CERN, Geneva, Switzerland

HPC Platform used: JUQUEEN and JUWELS of JSC

Local Project ID: chmz37

Protons are composite particles: bound states of quarks and gluons, as described by the theory of quantum chromodynamics (QCD). Using lattice QCD, we know in principle how to use supercomputers to compute various properties of the proton such as its radius and magnetic moment, however this is very challenging in practice. A major part of this project was devoted to developing and studying methods for more reliable calculations, in particular for obtaining more accurate results in a finite box and for better isolation of proton states.

Materials Science and Chemistry

Principal Investigator: Karsten Reuter, Lehrstuhl für Theoretische Chemie, Technische Universität München

HPC Platform used: JUWELS of JSC

Local Project ID: tmcscat

As most notorious greenhouse gas, CO2 emissions prevail as high as about 364 million tons carbon with the concentration reaching over 400 ppm in the atmosphere. A drastic reduction of CO2 is urgently necessary for sustainable growth and to fight climate change. The electrochemical reduction of CO2 (CO2RR) is a promising approach to utilize renewable electricity to convert CO2 into chemical energy carriers at ambient conditions and in small-scale decentralized operation. Researchers from Technical University of Munich have employed an active-site screening approach and proposed carbon-rich molybdenum carbides as a promising CO2RR catalyst to produce methanol.

Environment and Energy

Principal Investigator: Clemens Simmer, Institute for Geosciences, University of Bonn

HPC Platform used: JUQUEEN and JUWELS of JSC

Local Project ID: chbn29, chbn37

A multi-institutional team of researchers is developing a data assimilation framework for coupled atmosphere-land-surface-groundwater models. These coupled models, which potentially allow a more accurate description of the coupled terrestrial water and energy fluxes, in particular fluxes across compartments, are affected by large uncertainties related to uncertain input parameters, initial conditions and boundary conditions. Data assimilation can alleviate these limitations and this project is focused in particular on the value of coupled data assimilation which means that observations in one compartment (e.g., subsurface) are used to update states, and possibly also parameters, in another compartment (e.g., land surface).

Life Sciences

Principal Investigator: Wolfgang Wenzel, Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT)

HPC Platform used: SuperMUC-NG of LRZ

Local Project ID: pr27wi

GPCRs sit in the cell membrane and transmit signals from the outside of the cell to its interior. Currently, drugs targeting these receptors only work by mimicking ligands, i.e. they activate or inhibit the receptors by changing their conformation. If the GPCR adopts an active conformation, it can bind proteins on the intracellular side of the cellular membrane, which then transmit the signal inside the cell. In this study, we investigated how a protein that stops the GPCR from signaling, interacts with a prototypical GPCR. We discovered that specific lipids can modify how signals are transmitted by modifying the way of interaction between the GPCR and arrestin. In the future this could enable the discovery of a new kind of drugs for GPCRs.

Computational and Scientific Engineering

Principal Investigator: Manuel Keßler, Institute of Aerodynamics and Gasdynamics, University of Stuttgart

HPC Platform used: Hazeln Hen of HLRS

Local Project ID: GCS-CARo

Helicopters and other rotorcraft like future air taxis generate substantial sound, placing a noise burden on the community. Advanced simulation capabilities developed at IAG over the last decades enable the prediction of aeroacoustics together with aerodynamics and performance, and thus allow an accurate and reliable assessment of different concepts long before first flight. Consequently, this technology serves to identify promising radical configurations initially as well as to further optimize designs decided on at later stages of the development process. Conventional helicopters may benefit from these tools as much as breakthrough layouts in the highly dynamic Urban Air Mobility sector.

Computational and Scientific Engineering

Principal Investigator: Panagiotis Stathopoulos, Hermann-Föttinger-Institut, Technische Universität Berlin

HPC Platform used: SuperMUC-NG of LRZ

Local Project ID: pr27bo

Hydrogen-enriched fuels can reduce the CO2 emissions of gas turbines. However, the presence of hydrogen in fuel mixtures can also lead to undesirable phenomena like flashback. Swirling combustors can take advantage of an axial air injection to increase their resistance against flashback. Such an example is the swirl-stabilized presented in experiments at the TU Berlin. The axial momentum ratio between the fuel jets and the air was found to control flashback resistance. This experimental hypothesis motivates the present study where large-eddy simulations of the combustion system are carried out to study the physics behind flashback phenomena in hydrogen gas turbine combustors.

Astrophysics

Principal Investigator: Hubert Klahr, Max-Planck-Institut für Astronomie, Heidelberg (Germany)

HPC Platform used: JUQUEEN and JUWELS of JSC

Local Project ID: chhd19

MPIA scientists have developed a planetesimal formation model based on high-resolution hydro-dynamical simulations performed on JSC HPC systems. The simulations were used to model disk turbulence and its two effects on the dust, the mixing and diffusion of the dust on large scales but also the concentration of dust on small scales. This research helped to better understand the efficiency of these processes and to derive initial mass functions for planetesimals and gas giant planets to predict when and where planetesimals and Jupiter-like planets should form and of which size they will be. This is a fundamental step forward in understanding the formation of our own solar system as well as of the many planetary systems around other stars.

For a complete list of projects run on GCS systems, go to top of page and select the scientific domain of interest in the right column.