Mechanical Engineering Student | Energy Engineering, CFD & Machine Learning
I am a Mechanical Engineering student at TU Dresden with a focus on Energy Engineering, Computational Fluid Dynamics (CFD), and data-driven methods.
I currently work as a research assistant at the Institute of Automation and Measurement Technology (ITM), where I focus on sensor technology, measurement systems, and data-driven analysis. I am particularly interested in combining physical models with numerical simulation and machine learning to solve engineering problems.
Machine Learning in Computational Fluid Dynamics
Topics include surrogate modeling, supervised learning, regression models,
analysis of coherent structures (POD/DMD), reduced-order modeling,
and data-driven flow control.
Development of a two-axis IMU calibration test stand with a differential gear mechanism. Features 3D-printed components, NEMA 17 stepper motors, and precise motion control for sensor calibration.
Skills used: Sensor Systems · Mechanical Design · Embedded Systems · Python · Git
Development of a machine learning model for predicting membrane force and position based on data from eight strain sensors. The system runs on a Raspberry Pi 5 and visualizes the predictions in real time on an LED matrix.
Skills used: Data-Driven Modeling · Machine Learning · Sensor Systems · Embedded Systems · Python · Git · Simulation
Computational Fluid Dynamics
Finite Volume Method, Fluid mechanics, Numerical Simulation
OpenFOAM
Solver Setup, Case Configuration, Post-processing
Machine Learning
Supervised Learning, Regression, Neural Networks, Pytorch
Python
NumPy, Pandas, Matplotlib
Engineering Tools
MATLAB, Git, SolidWorks, CoolProp