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 Project Assistant.

Research Interests

My current research focusses on improving the accuracy and efficiency of advanced multiphase flow prediction. Exemplarily for that research topic is the investigation of the coarse-graining approach in wet gas-particle systems, where a computational parcel that represents a certain number of primary particles is simulated without decrease in prediction accuracy. In these CFD-DEM simulations OpenFOAM(R) is used for the Computational Fluid Dynamics (CFD) part, while the Discrete Element Method (DEM) part is solved within the framework of LIGGGHTS(R). The necessary coupling between these two solvers is performed with CFDEM(R).

The title of my PhD Thesis is “Physics-based Deep Learning for Advanced Multiphase Flow Prediction”. The most current task is to develop and train Deep Neural Networks (DNN) to improve the modelling of radiative heat transfer in multiphase flow systems.