Recurrence CFD (rCFD) – Efficient Time-extrapolation of Classical CFD Simulations

Description

Particulate processes commonly involve extremely differing time scales, ranging from milliseconds (e.g. for resolving particle collisions) to minutes and hours (e.g. for determining residence times). Therefore, an efficient time-extrapolation of numerical simulations is essential. Recurrence CFD (rCFD) aims at an efficient representation of long-term processes which slowly evolve on highly-dynamic pseudo-periodic flow fields. In the framework of rCFD, short-term full CFD simulation deliver recurrence databases of the governing flow at different operating conditions. Based on statistical reasoning, rCFD then exploits these databases in order to either develop (i) generic flow fields or (ii) generic transport fields, which subsequently serve as basis for the long-term process under consideration. In case of unsteadily varying operating conditions, we interpolate between existing recurrence databases. In applying rCFD to a set of single-phase and multiphase flows, we experienced a computational speed-up of two (flow based rCFD) to four (transport based rCFD) orders of magnitude. In many cases this dramatic speed-up allows for faster-than-real-time simulations, which run on the same high-resolution grid as the original full CFD simulation. Finally, we explore the possibility of incorporating such real-time rCFD simulations into process monitoring and control, by using real-time rCFD simulations as plant predictor.

Period	11 Sept 2018
Event title	12th European Fluid Mechanics Conference
Event type	Conference
Location	Wien, AustriaShow on map