Experience
- Research Associate University of Göttingen May 2023 - Present
- Visiting PhD Student NORCE Norwegian Research Centre Apr 2019 - May 2019
- Research Associate Clausthal University of Technology June 2017 - May 2023
- Project Engineer RWTH Aachen University May 2015 - May 2017
- Research Associate Luleå University of Technology 2011 - 2014
Education
- PhD Student Technische Universität Clausthal - Drilling Simulator Celle 2017 - 2024
- Luleå University of Technology - Master of Science (MSc), Space Engineering 2008 - 2010 Erasmus Mundus SpaceMaster programme
- Université Paul Sabatier Toulouse III - Master of Science (MSc), Physics and Astrophysics 2008 - 2010 Erasmus Mundus SpaceMaster programme
- Dipl. Ing. (FH), Mechanical Engineering 2003 - 2008
Honors and Awards
- Scholarship for Research Exchange at NORCE Norwegian Research Center - E.ON Stipendienfonds Dec 2018
A VAST Storage system will be installed as part of the new KISSKI data center. VAST storage systems offer different protocol flavours to access the storage backend, i.e. NFS, S3, SMB, and mixed. Since projects at the new data center should be executed in an efficient way, it is important to gain some insights in the potential performance of machine learning workloads. The proposed thesis will fill this gap and provide recommendations for future projects.
LIGGGHTS is a common code used for the simulation of macroscopic particles. It is based on the well-known molecular dynamics code LAMMPS. The variant used within the thesis is the academic fork LIGGGHTS-PFM which is under current development. Since LAMMPS already has some modules for GPU processing, it is the goal of the thesis to modify LIGGGHTS-PFM to make use of these capabilities. In a first step the best strategy for implementing LIGGGHTS-PFM on GPUs should be evaluated. Based on this a concept and initial steps of the implementation are expected. However, it is not required that all features of LIGGGHTS-PFM are implemented within the scope of the thesis. It is expected that the enhancement will improve the run-time performance and pave the road to particle simulations on GPUs. General programming experience is required. Knowledge in GPUcomputing and particle transport is beneficial but not mandatory.
Precice as already presented at the GöHPCoffee is a multiphysics framework which allows the combination of various simulation codes to perform coupled simulations. These can both include coupled thermal problems or topics related to fluid structure interaction. So far, there exists no possibility to perform a coupled particle simulation using preCICE since the only particle solver is not publicly available. It is the aim of this thesis to mitigate this limitation by implementing a precice-adapter for the particle solver LIGGGHTS-PFM. One possibility could be the modification of an existing OpenFOAM-adapter in preCICE. In addition, the thesis will compare the achievable performance with other coupling libraries using LIGGGHTS and its derivatives. General programming experience is required. Knowledge in simulation technology and particle transport especially in LIGGGHTS is beneficial but not mandatory.
In flow loop experiments, I studied the damping of oscillations in a pipe subject to flow and particle transport. I recorded the movement with two GoPro Hero 9 cameras to have valuable absolute position data in addition to accelerations recorded by the Inertial Measurement Unit (IMUs) placed inside the inner pipe in the picture. However, due to the frame rate of 200 fps, the contrast is changing between frames due to the 50 Hz frequency of the electricity powering lamps in the laboratory. A manual processing of all videos is unfeasible due the large number of frames to be analyzed (approx. 100k). Therefore, an automation either by using image processing libraries, e.g. OpenCV, or an AI based approach on GPUs, e.g. with the Facebook library detectron2, is required to process this larger amount of video data. Preferable knowledge for the project are experience with AI/ML and/or image processing, since he successful applicant will evaluate using different approaches with the goal of segmenting the videos and providing both positions of the inner pipe and particle bed. Further information on the experiment can be found in https://doi.org/10.21268/20241022-0. Upon successful completion of the project, the application will have gain hands-on experience with a real-world problem in the area of AI processing of video data. The results are also planned to be submitted in a scientific publication, so it is your chance to get your first paper published.
All publications as BibTex