CFD–DEM simulations of industrial fluidized beds suffer from a major computational bottleneck: simulating one second of physical time often requires hours of wall-clock time on modern computer hardware, with hundreds of processors, which makes long-term process analysis impractical. Recurrence-based CFD (rCFD) addresses this challenge by exploiting pseudo-periodic flow patterns to extrapolate short CFD–DEM simulations over extended time periods, but experimental validation of such time-extrapolated predictions has remained absent.
In this collaborative study, the researchers from the Institute of Solids Process Engineering and Particle Technology, and the Institute of Process Imaging at TUHH present the first rigorous experimental validation of recurrence-based CFD (rCFD) using full-field MRI measurements of a bubbling fluidized bed.
The work includes:
• Systematic comparison of four drag models
• MRI-based validation of internal bubble dynamics
• Identification of a characteristic recurrence period (τ ≈ 0.27 s)
• Computational speed-up of 4,050 times while maintaining predictive accuracy
Asif Shaik, Swantje Pietsch-Braune, Stefan Pirker, Melis Özdemir, Muhammad Adrian, Alexander Penn, Stefan Heinrich (2026). MRI-validated CFD–DEM simulation and recurrence-based time extrapolation (rCFD) of a bubbling fluidized bed: Drag model selection and computational speed-up. Chemical Engineering Research and Design 227, 879-893.
