Josef Tausendschön

Josef Franz Viktor Tausendschön
Dipl.-Ing. BSc
+43 316 873 - 30417
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About Me

In summer 2018 I finished my Master’s Program in Chemical and Process Engineering at Graz University of Technology with distinction. During my master's thesis, where I used CFD-DEM simulations to study the sedimentation behavior of wet gas-particle systems, I was a visiting student research collaborator at Princeton University. After the completion of my Master’s degree I started my PhD program at the Institute of Process- and Particle Engineering at Graz University of Technology, where I am currently employed as Research and Teaching Assistant.

The topic of my PhD thesis is "Physics-based Deep Learning for Advanced Multiphase Flow Prediction" and deals with the use of artificial intelligence in the simulation of multiphase flows.

Research Interests: Coarse-Graining

The starting point of my dissertation and research work, in cooperation with the group of Prof. Sundaresan of Princeton University, is the investigation of the so-called coarse-graining approach in simulations of wet gas-particle systems, where a computational particle package is considered representative of a certain number of primary particles to significantly reduce the computational demand without sacrificing accuracy (see publication list).

Research Interest Heat Radiation Modelling

Using pre-trained Deep Neural Networks (DNN), a way to compute view factors for modeling radiative heat transfer between particles and between particles and walls with an accuracy and speed necessary for DEM simulations was created. The data set created is composed of a wide range of system sizes and packing densities and has been validated at very high temperatures with experimental data (see publication list).

The current focus here is on extending the published approach from monodisperse to polydisperse systems and investigating other machine learning models that go beyond DNNs.