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Research & more

The SDL Highly Scalable Fluids & Solids Engineering carries on several research projects in distinct categories:

  • Highly scaling parallel mesh generation methodologies;
  • Highly scaling task-based lattice-Boltzmann solvers;
  • Multi-physics coupling technologies;
  • Development of technologies using high-performance computing for personalized medicine;
  • Deep learning methods for various simulation-based applications

Funded projects

AM-SIT - Data-Driven Analysis of Medi- cal and Simulation Data for Improved Patient Treatment in Rhinology

AM-SIT is a collaborative work between the SDL Highly Scalable Fluids & Solids Engineering, the Cross-Sectional Team Deep Learning (CST-DL), and clinical centers sharing medical data and expertise. In this project ML algorithms are developed towards automatic pathology detection and classification in rhinology.

More information: JSC project website
Project website at JARA-CSD : AM-SIT
Contacts: Mario Rüttgers, Andreas Lintermann

Rhinodiagnost - Morphological and Functional Precision Diagnostics of the Nose

The SDL is partner in the international IraSME project “Rhinodiagnost” in which the researchers cooperate with the Institute of Aerodynamics and Chair of Fluid Mechanics (RWTH Aachen University), SUTTER Medizintechnik (medical device company), and Angewandte Informationstechnik Forschungsgesellschaft (AIT) to enhance medical treatment in the field of ENT.

More information: JSC project website
Project website: Rhinodiagnost
Contacts: Moritz Waldmann, Andreas Lintermann


EuroCC - European Competence Centers

JSC participates in the EuroCC project in which National Competence Centers for the collaboration between HPC centers and industry and academia are established.

More information: JSC project website
Project website: EuroCC
Contacts: Andreas Lintermann


Further projects

CFS-Dyn - DNS of Cerebrospinal Fluid Dynamics

This project deals with lattice-Boltzmann simulations of the cerebrospinal fluid (CSF) inside the human central nerve system (CNS) to understand the CSF pathophysiology and to improve CNS therapeutics.

More information: JSC project website
Contacts: Seong-Ryong Koh, Andreas Lintermann


DNN-CFD - Deep Neural Networks for CFD Simulations

Together with partners from the Complex Phenomena Unified Simulation Research Team, RIKEN-CSS, Japan, the SDL Highly Scalable Fluids & Solids Engineering performs research on employing machine learning (ML) techniques to accelarate numerical flow field predictions. The work is performed within the frame of the Joint Laboratory for Extreme Scale Computing (JLESC).

More information: JSC project website
Project website at JLESC: DNN-CFD
Contacts: Mario Rüttgers, Andreas Lintermann


DL-Aero - Prediction of Acoustic Fields using a Lattice-Boltzmann Method and Deep Learning

The main objective of this project is the prediction of acoustic fields via training a robust machine learining (ML) model based on a deep encoder-decoder-based convolutional neural networks (CNNs).

More information: JSC project website
Contacts: Mario Rüttgers, Andreas Lintermann


DLR-Exa - Exascale Readiness for Aeronautics and Space Applications

In this project, the SDL Highly Scalable Fluids & Solids Engineering and the Institute of Aerodynamics and Flow Technology (DLR-AS) at DLR jointly work together to bring DLR’s computational fluid dynamics (CFD) code CODA to modular supercomputing architectures (MSAs) at exascale.

More information: JSC project website
Contacts: Andreas Lintermann