Forschung

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

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

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:
Dr. Tobias Kenter

Acceleration of Shallow Water Simulations on FPGAs

Principal Investigators:
Prof. Dr. Christian Plessl
Affiliation:
Paderborn University, University of Bayreuth
HPC Platform used:
PC2: Noctua 1, in particular Bittware 520N cards with Stratix 10 FPGAs

Shallow water simulations are important for climate models, flood or tsunami predictions and other applications. Performing such simulations on unstructured meshes with the Discontinuous Galerkin method is numerically attractive, but a performance challenge on conventional architectures. With a customized dataflow architecture implemented on FPGAs, we have improved performance and power efficiency on a single FPGA and achieved promising initial results when scaling to multiple FPGAs via direct FPGA-to-FPGA interconnects.

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Project Manager:
Prof. Dr. Siegfried Raasch

Evaluation of a Novel City Climate Model – Evaluation of PALM-4U for big German Cities against Data from intensive Observation Periods

Principal Investigators:
Prof. Dr. Siegfried Raasch, Prof. Dr. Björn Maronga
Affiliation:
Leibniz Universität Hannover, Karlsruhe Institute of Technology, Freie Universität Berlin, Humboldt Universität Berlin, Technische Universität Berlin
HPC Platform used:
NHR@Göttingen, NHR@ZIB: HLRN Clusters Lise and Emmy

PALM-4U is a newly developed high-resolution urban-climate model. It is designed as a tool for researchers and city planners to simulate and analyze the urban climate and its effects on city dwellers. The key feature of PALM-4U is its capacity to directly resolve turbulence effects and provide highly accurate simulation results. Apart from that, PALM-4U offers further features such as sophisticated bio-climate and air chemistry analysis or a multi-agent model that simulates individual city dwellers wandering across the city. To gain confidence in PALM-4U, extensive evaluation is necessary.

Project Manager:
Dr. Uwe Gerstmann

Photonic Materials from ab-initio Theory

Principal Investigators:
Prof. Dr. Wolf Gero Schmidt
Affiliation:
Paderborn University
HPC Platform used:
PC2: CPU cluster

Accurate parameter-free calculations of optical response functions for real materials and nanostructures still represent a major challenge for computational materials science. Our project focusses on the development and application of efficient but accurate ab-initio methods that give access to the linear and nonlinear optical spectra. We explore, on the atomistic level, how the material structure, its composition and defects, but also external parameters like stress, temperature or magnetic fields influence the optical response. It thus leads to a better understanding of existing materials and contributes to the design of new photonic materials.

Project Manager:
Dr. José Calvo Tello

Semi-Automatic Subject Classification with Basisklassifikation

Principal Investigators:
Dr. José Calvo Tello
Affiliation:
Universität Göttingen
HPC Platform used:
NHR@Göttingen

In this project the goal is to use algorithms to predict classes of the library classification system “Basisklassifikation” (which can be translated as basic classification). A library classification system is a taxonomy of predefined classes that represent disciplines, subdisciplines, themes or types of publications. Subject librarians assign one or more of these classes to each publication, allowing both final users or retrieval system to use this annotated information for finding publications. As input data we observe mainly bibliographic data, such as for example the title, the name of the publisher, the year of publication and the language of the publication. The algorithms should suggest several classes, which are then analyzed by

Project Manager:
Dr. Marijn van Jaarsveld

Expansion and optimal Exploitation of individual neoepitope Repertoire

Principal Investigators:
Prof. Ugur Sahin
Affiliation:
Johannes Gutenberg-Universität Mainz
HPC Platform used:
NHR Süd-West: Mogon/Mogon 2

Cancer mutanome vaccines targeting neoepitopes derived from somatic mutations have ideal properties to become an essential part of modern multimodal cancer therapy. Our goal is to fully realize this personalized cancer immunotherapy concept by addressing the key genomic and immunological challenges for successful application of this approach in patients with any type of cancer.

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Project Manager:
M.Sc. Andreas Bolke

Dedicated Monte Carlo Simulations and Image Reconstruction Algorithms for range Verification in Particle Therapy using Compton Cameras

Principal Investigators:
Prof. Dr. Magdalena Rafecas
Affiliation:
Universität zu Lübeck
HPC Platform used:
NHR@ZIB: Lise

We work on imaging for particle therapy. Particle beams (e.g. protons) can precisely destroy tumors while sparing healthy tissue. To verify the irradiation, Compton cameras (CC) can be used to detect gamma rays emerging from the patient. CC require complex algorithms to reconstruct images. We employ Monte Carlo simulations to recreate the irradiation and test reconstruction algorithms. The simulations and development of reconstruction approaches require much computing power. Thanks to HLRN, we simulate realistic therapeutic beams, the emerging rays and its detection with CC, and can test our reconstruction. Our approaches notably improve image quality. Further improvements are planed using a-prori information and refined data selection.

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Project Manager:
Dr. Roland Ruhnke

Seamless atmospheric Composition Modelling with ICON-ART

Principal Investigators:
Dr. Roland Ruhnke
Affiliation:
Karlsruhe Institute of Technology (KIT)
HPC Platform used:
NHR@KIT: HoreKa

ICON-ART is the next generation model for seamless simulation of numerical weather forecast, climate prediction and atmospheric composition modelling. It is a joint development project of DWD (German Weather Service), MPI-M (Max-Planck-Institute of Meteorology), DKRZ (German Climate Computing Center), and KIT (Karlsruhe Institute of Technology). At IMK-ASF the simulations focus on the understanding of composition-climate interactions as well as atmospheric chemical and microphysical processes on different scales

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