  Classical Computational Fluid Dynamics (CFD) of long-time processes with
strongly separated time scales is computationally extremely demanding if not
impossible. Consequently, the state-of-the-art description of such systems is
not capable of real-time simulations or online process monitoring. In order to
bridge this gap, we propose a new method suitable to decouple slow from fast
degrees of freedom in many cases. Based on the recurrence statistics of
unsteady flow fields, we deduce a recurrence process which enables the generic
representation of pseudo-periodic motion at high spatial and temporal
resolution. Based on these fields, passive scalars can be traced by recurrence
CFD. While a first, Eulerian Model A solves a passive transport equation in a
classical implicit finite-volume environment, a second, Lagrangian Model B
propagates fluid particles obeying a stochastic differential equation
explicitly. Finally, this new concept is tested by two multiphase processes - a
lab scale oscillating bubble column and an industrial scale steelmaking
converter. Results of tracer distribution obtained by recurrence CFD are in
very good agreement with full CFD simulations, while computational times are
dramatically reduced. Actually, recurrence CFD is a promising candidate for
online simulations of passive transport processes at full CFD resolution, which
opens the door towards improved process monitoring.
