Michael Mitterlindner

Michael Mitterlindner
Dipl.-Ing. BSc

Phone: +43 316 873 - 30411

About me

I began my studies in chemical engineering at Graz University of Technology in 2018. Upon completing my bachelor's program, I pursued a master's degree in chemical engineering with a focus on systems engineering and process technology. During my master's studies, my fascination for various simulation methods and techniques grew. This interest led me to work at the Institute of Process and Particle Engineering, where I applied CFD simulations to predict potential distributions in the wafer etching process. Additionally, I was involved in a project exploring flow behavior for cleanroom decontamination using H₂O₂. As part of my master's thesis at the Institute of Chemical and Environmental Engineering, I developed and implemented an optimization algorithm aimed at determining optimal parameters of thermodynamic models based on experimental data, employing a machine learning strategy. In 2023, I embarked on my Ph.D. journey at the Institute of Process and Particle Engineering under the guidance of Stefan Radl.

Research interests

I work for the “DigiBioTech” LEAD-project, focusing on Reduced Order Modelling (ROM). My research uses unsupervised clustering to identify smart compartments for efficient multiphase simulations. By automating the conversion of complex CFD data into fast network models, this approach explicitly captures interphase mass transfer to enable real-time process control and design.

This workflow is implemented in CLARA (Clustering Algorithm and Applications), an open-source software package compatible with OpenFOAM®, Fluent®, and SimVantage®. While currently optimized for Eulerian data, I am extending this approach to cluster (CFD-)DEM results. This expansion will enable efficient modelling of Lagrangian systems, such as bubbly flows and battery drying processes.

Download CLARA on GitLab (Link: https://gitlab.tugraz.at/simSci/clara-public.git)

I also specialize in predicting thermal radiation, with a primary application in battery thermal runaway simulations. Accurate modeling of heat propagation in these dense systems is critical for safety assessments but computationally demanding. A key part of my research involves finding efficient geometric approximations to predict view factors for particles, avoiding the high cost of standard ray-tracing.

Additionally, I recently improved the accuracy of (radiative) heat transfer in coarse-grained CFD-DEM simulations. This ensures that temperature spikes are captured correctly even when using simplified particle models to increase simulation speed.

Read the Publication (Link: https://doi.org/10.1016/j.partic.2025.07.003)

Finished Research

The already finished research project "Ni2Steel", aimed at enhancing the recycling process of NiMH batteries. The primary objective of my research was to model extremely compactable and cohesive materials using the Discrete Elemental Method (DEM). In addition to mechanical properties, accurately modeling heat conduction within the bulk material is essential for safety reasons and the design of further equipment. I aimed to utilize experimental data to optimize simulation parameters for DEM, enabling realistic representation of flow and thermal conduction properties of crushed batteries. Furthermore, I plan to leverage AI-supported models and intelligent optimization algorithms to efficiently determine appropriate parameters for simulation.

Figure 2. CT-Scan of the battery recyclate