On the experimental investigation of gas-liquid flow in bubble columns using ultrafast X-ray tomography and radioactive particle tracking

Comparative analysis of liquid hydrodynamics in a co-current flow-through bubble column with densely packed internals via radiotracing and Radioactive Particle Tracking (RPT)

CFD-based deep neural networks (DNN) model for predicting the hydrodynamics of fluidized beds

Machine learning based position‐rendering algorithms for radioactive particle tracking experimentation

Hydrodynamic correlations for bubble columns from complementary UXCT and RPT measurements in identical geometries and conditions

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors