Forschung

An unseren NHR-Zentren werden Forschungsprojekte aus allen Wissenschaftsbereichen gerechnet. Eine Auswahl finden Sie hier:

Category:
Project Manager:
Dr. Ana-Catalina Plesa

Thermal Evolution and Dynamics of the Interior of Planets and Moons

Principal Investigators:
Dr. Ana-Catalina Plesa
HPC Platform used:
NHR@KIT: HoreKa

Over the past decades, large-scale computer simulations have grown to become one of the most powerful approaches to study the interior of Earth-like planets. Geodynamical models are used to investigate the evolution and distribution of the temperature inside the planet that ultimately affects its structure and the way the planet cools over time. Combined with data obtained from planetary missions and laboratory experiments, these models help us to improve our understanding of the history and current state of planets in our Solar System and beyond. These models can teach us about the formation and evolution of planetary environments

Project Manager:
Prof. Uwe Naumann

CFD Simulations Ecurie Aix

Principal Investigators:
Prof. Uwe Naumann
Affiliation:
RWTH Aachen University
HPC Platform used:
NHR4CES@RWTH: CLAIX

Every year we, as the Formula Student Team of RWTH Aachen University, develop a completely new electric race car and revise a previous car to be able to drive autonomously. For our Aerodynamics team, the electric vehicle is the main focus. We try to find the best geometries for our car within the regulatory constraints and while keeping performance compromises with other design areas in mind.

Project Manager:
Marius Trollmann

Resolving the Structure of mRNA-Vaccine Lipid Nanoparticles

Principal Investigators:
Prof. Dr. Rainer Böckmann
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen
HPC Platform used:
NHR@FAU: Alex GPU cluster

Lipid nanoparticles (LNPs) are very successfully employed as novel transport vehicles for mRNA vaccines. A major gap in our understanding and thus obstacle for future developments of nanoparticle-mRNA drugs, however, is the lack of a molecular picture and molecular insight into LNPs. In this project we aim to provide unique insight at the atomistic scale into the structure and mechanisms of these carriers.

Project Manager:
Harish Kumar Singh

High Throughput Screening for Spin-Polarized Current in Noncollinear Magnetic Materials

Principal Investigators:
Prof. Hongbin Zhang
Affiliation:
Technische Universität Darmstadt
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

The spin-dependent transport phenomena in magnetic materials can provide spin-polarized charge current and large pure spin current, which could be achieved premised on two fundamental properties, i.e., anomalous Hall conductivity (AHC) and spin Hall conductivity [1]. The AHC is characterized as a generation of transverse voltage drop or current density (depending on the boundary conditions) originating from the longitudinal electric currents. The existence of finite AHC in noncollinear antiferromagnets has attracted noticeable attention due to possible applications in antiferromagnetic spintronics for information storage and data processing [2], where the kagome lattice turns out to be an intriguing prototypical lattice to host giant AHC

Project Manager:
Driss Kaddar

Direct numerical simulation of Flame-Wall-Interactions of DME flames

Principal Investigators:
Prof. Dr.-Ing. Christian Hasse
Affiliation:
Technische Universität Darmstadt
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

The energy transition is a global challenge with major economic and social impact. Future combustion devices will have to adapt to enable low-carbon or carbon-free sustainable power generation. The design of high efficiency devices and the use of alternative fuels is heavily supported by computational fluid dynamics simulation. However, detailed simulations of complete combustion systems are still computationally unfeasible to this day. This illustrates the need for accurate but less computationally demanding models. In this project, a direct numerical simulation (DNS) of turbulent flame wall interaction was conducted to provide further physical insight to near wall combustion processes and to contribute to the development of novel

Project Manager:
Markus Hundshagen

Gas-liquid flow Delivery with centrifugal Pumps

Principal Investigators:
Prof. Dr.-Ing. Romuald Skoda
Affiliation:
Ruhr University Bochum
HPC Platform used:
NHR4CES@RWTH: CLAIX

Centrifugal pumps are employed in various industrial and engineering applications to transport two-phase mixtures as liquid and non-condensable gas. Several examples of the two-phase pump operation can be found, e.g., in the chemical and process industry or geothermal power stations. Predicting two-phase flows in centrifugal pumps with state-of-the-art computational fluid dynamic (CFD) methods is only possible by accepting significant uncertainties.

Project Manager:
Marcel Sadowski

Ab-initio Modeling of Battery Materials

Principal Investigators:
Prof. Dr. rer. nat. Karsten Albe
Affiliation:
Technische Universität Darmstadt
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

One approach to the realization of safer batteries relies on all solid-state batteries (ASSB) which use a non-flammable solid electrolyte (SE) instead of the commercial flammable liquid organic electrolytes. While many obstacles to the successful production of these batteries have already been overcome, the inner and outer interfaces in a real battery setup remain a major challenge. Thus, a thorough understanding of the interfacial atomistic processes is crucial, highlighting the value of interface simulations on the atomic scale. Currently, these are only possible via ab-initio methods, such as density functional theory (DFT) calculations, because no classical interatomic potentials exist, which can simultaneously describe the SE and

Category:
Project Manager:
Charlotte Gallenkamp

Quantum Chemical Investigation of Spectroscopic Properties of Iron Complexes as Models for Fe-N-C Fuel Cell Catalysts

Principal Investigators:
Prof. Dr. Vera Krewald
Affiliation:
TU Darmstadt
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

For a climate-friendly automotive sector, fuel cell technology becomes increasingly important. A promising step towards accessibility and commercialization of this technology may be reached using Fe-N-C catalyst materials for the cathode reaction of the fuel cell. With their high activity, Fe-N-C catalysts can potentially substitute currently used, expensive platinum catalysts. Fe-N-C catalysts however lack stability and their active site composition is not sufficiently well understood for systematic improvements. This project combines spectroscopy and quantum chemical calculations in order to uncover the structure of the active site(s) in Fe-N-C catalysts and better understand their electronic structures.

Category:
Project Manager:
Dr. Tobias Zier

Color center formation induced by femtosecond lasers

Principal Investigators:
Prof. Dr. Martin E. Garcia
Affiliation:
University of Kassel
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

With the help of ab-initio molecular dynamics simulations we were able to show that it is possible to use femtosecond laser pulses to induce the ultrafast nonthermal formation of NV-centers in diamond. NV centers are of fundamental importance in quantum technologies.

Project Manager:
Dr. Noelia Ferruz

A deep unsupervised Model for Protein Design

Principal Investigators:
Dr. Noelia Ferruz
Affiliation:
Universität Bayreuth
HPC Platform used:
NHR@FAU: ALEX - GPGPU cluster

The design of new functional proteins can tackle many of the problems humankind is facing today but so far has proven very challenging1. Analogies between protein sequences and human languages have been long noted and a summary of their most prominent similarities is described. Given the tremendous success of Natural Language Processing (NLP) methods in recent years, its application to protein research opens a fresh perspective, shifting from the current energy-function centered paradigm to an unsupervised learning approach based entirely on sequences. To explore this opportunity further we have pre-trained a generative language model on the entire protein sequence space. We find that our language model, ProtGPT2, effectively speaks the