LATEST RESEARCH RESULTS

Quantum Chromodynamics (QCD) is without any reasonable doubt the correct fundamental theory of the strong interactions between quarks and gluons. Thus, it describes exactly all properties of particles like the proton, which is a bound state of quarks and gluons, so-called hadrons. However, while it is rather straight­forward to calculate all properties of atoms, i.e. bound states of electrons and nuclei, from Quantum Electrodynamics, the theory which describes the electromagnetic force, this is not the case for QCD. In fact, the QCD­equations are so difficult to solve that many or even most properties of hadrons are only poorly understood at the QCD level.

Biologics offer significant therapeutic advancements, but their complex nature poses formulation challenges due to their sensitivity to environmental factors. Molecular dynamics (MD) simulations can accelerate formulation development by investigating biophysical behavior and guiding the design of stable and effective formulations. However, MD simulations face limitations in computation-al resources and force field accuracy. This report details the use of supercomputing capabilities for large-scale MD simulations, exploring various formulation parameters using coarse-grained (CG) models.

The idea of a thin, weak layer beneath the Earth’s rigid outer shell is over a century old, long predating the widespread acceptance of plate tectonics. This layer, termed the asthenosphere, was originally proposed in order to account for Earth’s internal support of topographic loads such as mountain ranges (isostasy). Our view of this layer expanded following the plate tectonic revolution of the late 1960’s, after which it came to be viewed as a lubricating layer to facilitate plate motion; passively flowing in response to the overlying plates. In the past two decades, it has become clear that the asthenosphere is a much more exciting and active part of the convecting mantle than previously imagined.

Invented and patented by Nikola Tesla in 1913 [1], the Tesla turbine is a unique type of turbomachine that features a rotor made from stacked disks with small gaps in between, as shown in Figure 1. It is driven by a fluid that enters the disk gaps at the outer circumference and travels on a spiral path radially inward towards central exit openings. Figure 2 visualizes the path of the fluid. Rotor torque is generated purely from friction between the working fluid and the rotor disk surfaces.

To make Tesla turbines more accessible to engineers, researchers in the past developed and validated methods for predicting turbine performance mathematically.

The scientific breakthrough associated to the LIGO-Virgo observation of gravitational waves (GWs) and electro-magnetic (EM) counterparts from a binary neutron star merger (BNSM) has been crucially supported by theoretical predictions provided by simulations in numerical general relativity (NR). Simulating the spacetime and the neutron-star matter fields in 3 spatial dimensions (plus time) is the only way to connect the strong-field dynamics to the observable gravitational and electromagnetic spectra. Crucially, these HPC simulations provide precise calculations for the GWs and for mass outflows of neutron rich material. The former are necessary to detect the signals and identify the properties of the source (masses).

Intracranial aneurysms, affecting approximately 3% of the western population, pose a serious threat due to the risk of rupture, leading to irreversible disabilities or death. Leveraging the computational power of modern supercomputers, our project employs the lattice Boltzmann method (LBM) to delve into the complexities of hemodynamics within intracranial aneurysms, utilizing our in-house numerical solver, ALBORZ.

Our research addresses the challenge of complex geometry in patient-specific aneurysms by introducing a curved boundary condition, enhancing the accuracy of LBM simulations.

Stratocumuli are low level clouds at the top of the atmospheric boundary layer, at altitudes of about 1 km and with thicknesses of about 100 m. They are key elements of the climate system. On the one hand, they extend over thousands of kilometers in the eastern boundaries of the subtropical oceans, e.g., off the coasts of California, Peru, and Namibia, favored by the temperature contrast between the cold upwelling water and the warm subsiding air. On the other hand, they reflect more incoming solar radiation (higher albedo) than the underlying surface of the ocean, while they emit similarly in the long-wave range. This combination of large coverage and net cooling effect substantially affects the Earth's radiative budget.

Combustion in most engineering applications, such as spark ignition engines and gas turbines, often involves elevated pressure conditions and non-unity Lewis number Le fuel-blends. However, under these extreme conditions, the flame morphology becomes increasingly complex and turbulent, persistent with convoluted structures arising due to the presence and interactions of inherent flame instabilities [1]. In this project, direct numerical simulation (DNS) analysis is performed to evaluate and complement existing modelling approaches to account for realistic operating conditions for combustion applications.

Over the last few decades, ab-initio methods such as density-functional theory have become sufficiently accurate to allow for the prediction of many properties of new crystal structures. However, these predictions come at a significant cost, and due to the vastness of the space of possible materials, theoretical material discovery remains one of the most challenging questions in materials science. Machine learning methods, trained on existing databases of ab-initio calculations, have the potential to massively accelerate the process of theoretical materials discovery. One of the most important properties targeted is thermodynamic stability, which is used as a proxy to estimate the probability that a given compound can be synthesized.

The VIRTUALLAB project made use of the JUWELS cluster to perform high-resolution atmospheric simulations based on measurements from various meteorological sites and field campaigns. The resolution of these numerical experiments is high enough to resolve small-scale phenomena such as turbulence, convection and associated clouds. These simulations create a "virtual laboratory", allowing scientists to fill existing data gaps to increase our insight into these phenomena and further improve their representation in models used for weather- and climate prediction. Various climate regimes were simulated including the marine subtropics, mid-latitude continental areas, and the high Arctic.

Gas flow through porous materials is crucial in various industrial and environmental systems, such as chemical reactors, filtration units, cooling systems, and natural environments. These materials, with void spaces called pores, create complex pathways for gas flow, especially under turbulent conditions. Researchers at the Chair of Thermal Turbomachines and Aeroengines used advanced numerical simulations to study these effects. They replicated industrial packed beds

and used the SuperMUC-NG supercomputing system to model gas flow through structured arrays of particles. Their simulations enhance the current understanding of gas flow in porous media and impact the current mathematical models used to study such systems.

Stars are the building blocks of the visible Universe, which drive the galactic chemical evolution and act as observational tracers of the evolution of the cosmos as a whole. Yet models of stellar structure and evolution rely on parametric models of multi-dimensional phenomena, because many multi-dimensional processes operate on timescales that are up to 10 orders of magnitude smaller than the nuclear timescale, which is dominating stellar evolution during most of a stars' lifetime. The parametrizations of multi-dimensional processes are often based on simplistic assumptions and include free parameters that are adjusted to match observational properties of stars. In this project, we performed three-dimensional simulations of convective core…

For the design of sustainable hypersonic flight at Mach numbers above 5 or re-entry vehicles like space capsules or the Space Shuttle, heat management is one of the crucial aspects of engineering. Besides the application of insulating materials and their development to be used in the heat shield, the generation of the heat through fluid dynamics processes in the boundary layer along the vehicle has to be known. As experiments for these flight conditions are either extremely expensive or subject to large errors, numerical simulations can bridge the gap between the lab environment and the full-scale application.

Two billion light-years wide, the local Universe, is a formidable observational playground for astrophysicists. This tiny bit of the Universe hosts billions of billions of stars, planets gathered in galaxies, including our very own: the Milky Way. We now have a huge amount of data at different wavelengths of tons of galaxies and galaxy clusters in our neighborhood to understand better the Universe, its formation and evolution as well as that of its constituents. However, analyzing and interpreting properly all the observations of our Cosmic Home requires sophisticated cosmological simulations that need to be run on powerful supercomputers.

Reverse piezoelectricity describes the deformation of materials in the presence of an external electric field. To act piezoelectric, the material has to belong to the ferroelectrics, which have non-centrosymmetric crystal phases. The missing inversion symmetry allows that a stable net dipole moment or remanent polarization can form. Well known is PZT (Lead Zirconate Titanate), which is used in a broad range of actuator and sensor applica- tions. But for most ferroelectrics the remanent polariza- tion becomes lost in small structures and thin films. Therefore, the discovery of ferroelectric crystal phases in mixed HfO₂ and ZrO₂ thin film capacitors a few years ago was a breakthrough in micro- and nanoelectronics.

In quantum many-body physics, correlation functions, usually abbreviated to correlators, are the quantum expectation values of operators acting on different space-time points. When a correlator involves f space-time points, it is called an f-point correlator, and when f is larger than two, it is a multipoint correlator. When the system of interest is electrons in a solid and the operators are electron creation and annihilation operators, the correlators are also called electron Green's functions. (Here "creation" means the introduction of an electron to a solid from the outside; "annihilation" is the opposite operation.) The Green's functions are important since they determine various dynamical responses and spectral properties of the…

Sintering is a physically complex process that includes various mechanisms interacting and competing with each other. The obtained densification and microstructure of the sintered packing are of key interest. The accurate prediction of the powder coalescence for a given material and heating profile is a challenging multiphysics problem that couples mass transport and mechanics and involves multiple distinct stages: "early stage" vs. "later stage" (see Fig. 1). These rheological differences justify the application of specialized numerical models and methods with different computational costs for each of the stages.

This project aims to accelerate the search for new materials (e.g., for thermoelectric applications, battery materials, magnets, and other materials classes) based on ab initio high-throughput studies. High-throughput searches are typically restricted to known materials. This project explores strategies (data-driven chemical heuristics in subproject 1 and machine-learned inter- atomic potentials in subproject 2) to go beyond current database entries and include such computationally demanding properties in high-throughput searches. To accomplish each subproject, we develop automated workflows for high-throughput computations and provide large open databases of computed materials properties to the research community.

Nanoparticles (NPs) play an important role in various applications, such as drug delivery, detection of proteins, photocatalysis and optics. The size of the NPs are crucial parameters that significantly impact their properties. Therefore, samples of monodisperse NPs are highly desired. However, achieving homogeneous batches of NPs during fabrication is a challenge. The self-assembly methods used for nanoparticle formation inherently result in higher heterogeneity due to the complex thermodynamics and kinetics involved. Therefore, it is necessary to develop methods and techniques for the size-based separation and purification of NPs after their assembly.

Understanding lubrication at extreme conditions is key to efficient, sustainable mechanical systems. In this context, this project deals with nanoscale lubrication, revealing how molecular dynamics simulations guide better models for friction and lubrication. Breakthroughs include a novel viscosity-pressure relationship for hydrocarbons, a lubrication model with improved boundary slip laws, and molecular insights into lubricant behavior, offering transformative tools for engineering high-performance machinery.