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:
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:
M. Sc. Nima Fard-Afshar

Investigation of the Flow in a linear high pressure compressor Cascade using scale resolving Simulations

Principal Investigators:
Dr. Stefan Henninger
Affiliation:
RWTH Aachen University
HPC Platform used:
NHR4CES@RWTH: CLAIX

Hybrid RANS/LES (HRLES) is one SRS category, which bridges the gap between RANS and LES in regard to prediction accuracy of the results and required computing resources. The HRLES methods (i.e. various Detached Eddy Simulation (DES) formulations), with RANS modelling of the flow near the wall, and eddy-resolving simulation away from the wall, are believed to represent the mixing in turbulent flows

Project Manager:
Sebastian Strönisch

Digital thread-based Design of turbo Engines with embedded AI and high precision Simulation (DARWIN)

Principal Investigators:
Dr. Andreas Knüpfer
Affiliation:
TU Dresden, BTU Cottbus-Senftenberg, University of Surrey
HPC Platform used:
CPU and GPU Clusters

In the joint BMWi Lufo VI project DARWIN, the Center for Information Services and High Performance Computing (ZIH) and the Chair of Turbomachinery and Aero Engines (TFA) at the TU Dresden are working in cooperation with Rolls Royce Germany on the further development, application and validation of innovative digital simulation and design methods to improve the interdisciplinary understanding of engine systems. Work includes improving load balancing of highly parallelized coupled simulation codes, measuring surface roughness and wear effects and feeding them back into simulation models, as well as applying machine learning (ML) methods to predict flow fields.

Project Manager:
Prof. Dr. habil. Michael Breuer

Flow around a Wind Turbine Blade at Reynolds Number 1 Million

Principal Investigators:
Prof. Dr. habil. Michael Breuer
Affiliation:
Helmut-Schmidt-Universität Hamburg
HPC Platform used:
NHR@FAU: Fritz

The cost of energy produced by wind turbines has been undergoing a steady reduction. Wind energy supplied 15% of the electricity demand of the European Union in 2019. Since rotor blades are the determining component for both performance and loads, they are the objective of further optimizations. To obtain high efficiencies, an increased use of special aerodynamic profiles is observed possessing large areas of low-resistance, which means laminar flow is maintained. In order to design such profiles, it is necessary to include the laminar-turbulent transition in CFD simulations of wind turbine blades. Thus, the objective of the project is to carry out high-fidelity numerical simulations of the flow around a wind turbine blade at a realistic

Project Manager:
Chen Shen

CCDCGAN: Inverse design of crystal structures

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

Autonomous materials discovery with desired properties is one of the ultimate goals for modern materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modelling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backwards propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that

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

Project Manager:
Daniel Bauer

Molecular Dynamics Study of the Sodium/Potassium Channels HCN

Principal Investigators:
Prof. Dr. Kay Hamacher
Affiliation:
Technische Universität Darmstadt
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

Ion channels play a fundamental key role in all living organisms and are crucial for the signal transduction of neurons in higher animals. The hyperpolarization-activated cyclic nucleotide-gated (HCN) family of sodium/potassium channels are members of this protein family that are characterized by slow and weakly potassium selective inward current at hyperpolarizing voltages. HCN channels are expressed in a broad set of tissues in mammalia and are involved in an equally broad range of biological processes: in sinoatrial node cells of the heart, they are molecular facilitators of the pacemaker current (also known as ”funny current” If or Ih) which is required for subsequent generation of action potentials and ultimately leads to the

Project Manager:
Matthias Steinhausen

LES-Based Investigation of Flame-Wall-Interactions

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

In the context of global warming the necessity of efficient and low emission combustion applications arises. In addition to the use of alternative fuels, the current tendency is towards smaller internal combustion engines, which enable higher pressure ratios and, therefore, reach higher efficiencies. However, this evolution increases the surface to volume ratio, which leads to a growing influence of near wall phenomena on the overall combustion process.
The interaction of the flame with the surrounding walls has a crucial influence on the overall efficiency. Due to heat-losses at the cold walls, the chemical reaction within the flame stagnates. This leads to incomplete combustion in close vicinity to the walls, which has a major impact on

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