| patrick.hoehn@gwdg.de | |
| Telephone | +49 551/3930265 |
Experience
- Research Associate GWDG October 2025 - present
- Research Associate University of Göttingen May 2023 - September 2025
- 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
- Technische Universität Clausthal - Drilling Simulator Celle - Dr.-Ing., Petroleum Engineering 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
- Coburg University of Applied Sciences - Dipl. Ing. (FH), Mechanical Engineering 2003 - 2008
Honors and Awards
- Scholarship for Research Exchange at NORCE Norwegian Research Center - E.ON Stipendienfonds Dec 2018
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. The numerical analysis in my PhD thesis used OpenFOAM fork foam-extend as a framework for fluid and solid using solids4foam, and CFDEM®coupling-PUBLIC for coupling to the particle solver LIGGGHTS®-PUBLIC. Since then, most code development happened in the academic fork from the Department of Particulate Flow Modelling at Johannes Kepler University in Linz, Austria. Upon successful completion of the project, the applicant will gain hands-on experience with a real-world problem in the area of numerical modeling using different frameworks in C++. The developed code is planned to be upstreamed, enabling simulations currently not possible even with many commercial simulation programs.
Despite drilling technology traditonally originates from the field of oil and gas, it still plays a crucial role in emerging fields of Carbon Capture and Storage, geothermal energy or hydrogen storage. In order to reach a wide adoption of the new fields it is crucial to optimize the wellbore construction costs. In my research I was using mathematical models, i.e. both statistical and empircal, to replicate scenarios generated from previous drilling projects. In my previous paper "Framework for automated generation of real-time rate of penetration models" (doi:10.1016/j.petrol.2022.110369), I created a framework for the automatic parametrization of models for a single variable based on preprocessed measurement data. These models include both empirical models from the literature and trained using machine learning algorithms from sklearn. In a recent Master Thesis, a new simulation framework was developed in Python which could use the parametrized models for research and education in the drilling industry. Compared to the implementation in the paper, the new version will integrate several models from the literature to enable a more comprehensive simulation experience both for researchers and students. Upon successful completion of the project, the applicant will gain hands-on experience with a real-world problem in the area of mathematical and ML modeling. 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