Thomas Lichtenegger

Thomas Lichtenegger

Senior Scientist

Johannes Kepler University Linz

About me

I completed my PhD in Theoretical Physics at the Johannes Kepler University (JKU) Linz, Austria, followed by a postdoctoral stay at the State University of New York at Buffalo, United States, on the topic of quantum liquids such as superfluid helium at absolute zero. Since 2014, I have been working on the multiscale problem of complex multiphase and multiphysics flows as a Senior Scientist at the Department of Particulate Flow Modelling at JKU, where I earned my Habilitation (venia docendi) in 2021. In our group, I lead the team for particle-based simulations with a focus on data-driven methods and enjoy supervising PhD students in this area.

During my PhD studies, I learned the importance of first identifying the relevant physics of a problem, then modeling it rigorously, and finally solving it numerically in an efficient fashion. This principle also guides my current research activities, where I integrate physics-based models with data-driven approximations. Compared to classical simulation methods, this kind of approach enables calculations that are several orders of magnitude faster so that real-time simulations of complex flows are within reach. Together with the fantastic team of Emmi AI, I am working on deep learning surrogates for digital process twins.

Interests

Modeling complex flows
Multiscale and multiphysics systems
Data-assisted simulations

Education

Habilitation in Fluid Mechanics
JKU
PhD in Theoretical Physics
JKU

My research topics

Particulate flow modeling

Particulate flow modeling

Basics about modeling and simulating granular flows

Feb 22, 2025

Recurrence-based time extrapolation

Recurrence-based time extrapolation

Fast simulations of passive and weakly coupled transport processes in recurrent flows

Feb 21, 2025

Propagator formulation for transport processes

Propagator formulation for transport processes

Linear transport equations can be solved with large steps if the velocity field is known

Feb 20, 2025

Deep learning complex particle dynamics

Deep learning complex particle dynamics

Universal physics transformers can be trained to predict the evolution of various particulate flows.

Feb 19, 2025

Selected publications

Propagator-moments approximation for recurrence CFD: application to species transport in turbulent flows

Time Extrapolation, Recurrence CFD, Propagator Formulation, Turbulent Species Transport

Propagator-moments approximation for recurrence CFD: application to species transport in turbulent flows

In his first paper, my PhD student Hannes employed the propagator formalism to establish a theoretical framework for transport-based recurrence CFD. Not only can we simulate transport processes, e.g., in turbulent flows faster than real time, now we also understand why. In the course of these theoretical developments, Hannes managed to determine a correction term for numerical diffusion in rCFD which makes simulation results also more accurate than those in our previous studies.

Apr 4, 2025

NeuralDEM - Real-time Simulation of Industrial Particulate Flows

Universal Physics Transformers, Real-Time Simulations, Granular Flows in Various Regimes

NeuralDEM - Real-time Simulation of Industrial Particulate Flows

In this work, colleagues from NXAI (now Emmi AI) and I let universal physics transformers learn the dynamics of granular matter both in the dense, pseudo-steady regime of hopper flow and the dilute, dynamic behavior encountered in fluidized bed reactors. The neural networks were trained with a large number of DEM and CFD-DEM trajectories for varying particle properties, boundary conditions and geometries. Long-term predictions for different values of these properties showed (i) simulation speed ups of several orders of magnitude, (ii) very good generalization capabilities to unseen conditions, and (iii) the feasibility to directly use macroscopic material properties (e.g. angle of repose) instead of microscopic DEM parameters.

Nov 14, 2024

Fast long-term simulations of hot, reacting, moving particle beds with a melting zone

Moving Bed Reactors, Multiphysics Flows, Time-Extrapolation

Fast long-term simulations of hot, reacting, moving particle beds with a melting zone

Blast furnaces, still the dominant type of steel-making reactor, are difficult to simulate because of the vastly different time scales involved. While stiff grains and fast gas flow dictate small time steps, large-scale material transport and chemical conversion take place over the course of hours. To bridge this massive gap, we employed a simple time-extrapolation technique built upon detailed CFD-DEM data from which we obtained the granular velocity field. To cover long durations, we replaced solid, interacting particles with non-interacting counterparts that followed the prescribed streamlines while exchanging heat and mass with the surrounding gas flow. This allowed us to use much larger time steps and describe the evolution of a full-scale blast furnace towards its thermo-chemical equilibrium at comparatively little numerical costs.

Aug 18, 2023

Control of segregation by non-uniform aeration in a fluidized bed spray granulator

Fluidized Spray Granulation, CFD-DEM, Particle Segregation

Control of segregation by non-uniform aeration in a fluidized bed spray granulator

Together with our colleagues from the Hamburg University of Technology, we investigated how segregation in polydisperse fluidized beds can be controlled to avoid undesired behavior such as defluidization. Experiments were carried out by the great team in Hamburg while simulations were mainly done by my former Postdoc Marco.

Jul 7, 2023

Block-movement-based calibration of a discrete element model for fine, cohesive powders

Cohesive Powder, Discrete Element Method, Parameter Optimization, Rotating Drum

Block-movement-based calibration of a discrete element model for fine, cohesive powders

In this work, my former PhD student Tobias developed an optimization framework to obtain DEM parameters for highly cohesive powders from rotating drum experiments and subsequent block analysis. These parameters allowed for massively coarse-grained simulations in very good agreement with experiments.

Jan 17, 2023

Transport in turbulent, recurrent flows: Time-extrapolation and statistical symmetrization

Submerged Double-Jet, Bubble Transport, Multi-Scale Problems, Data-Assisted Simulations, Time-Extrapolation, Recurrence CFD

Transport in turbulent, recurrent flows: Time-extrapolation and statistical symmetrization

Together with my former PhD student Sanaz, I studied bubble transport in a strongly turbulent double-jet configuration and a data-assisted strategy to predict the long-term bubble distribution in the domain. Despite of massive fluctuations of the velocity field that hid any larger-scale recurrences of the flow topology, our nearest-neighbor-based approach recurrence CFD produced results in surprisingly good agreement with detailed LES data.

Feb 23, 2022

A block-movement-based analysis for cohesive powders in a rotating drum experiment

Rotating Drum, Cohesive Powder, Characterization, Flowability

A block-movement-based analysis for cohesive powders in a rotating drum experiment

Very fine metal powders are very challenging to characterize because many standard measurements fail for highly cohesive materials. To overcome this limiation, my former PhD student Tobias built a rotating drum experiment with automated video analysis and material block detection. The resulting statistics on the size, velocity, etc. of the blocks can be used to characterize such powders and detect even slight changes in their behavior, e.g., because of humidity uptake.

Nov 12, 2021