3.4 Conclusions
87
remain largely chaotic, becoming comparable to that of a conventional fluidised
bed. While in the structured regime, bubbles self-organise in predictable triangular
lattices of determined wavelength and angle, manifesting increased homogeneity in
the populations of size, spatial separation as well as residence time. As a result, one
would expect tight control over both solid mixing and interphase transport rate when
operating with structured flows. The results highlight the potential of these dynamically structured flows to be applied as a new principle looking into operation and
design criteria, as well as process scale-up. Nevertheless, the deficiencies of limited
propagation and the operating regime are subjected to further investigation.
Obviously, more features associated with the structured flows remain unexplored.
The experiments and analysis conducted only focus on bubble properties. Without
knowing details about solid dynamics, many features of the pattern, such as circulation rate of solids, alternative nucleation of bubbles, the relationship between bubble
size and separation are still largely unknown. One approach moving forward is to
undertake particle image velocimetry experimental measurements or computational
simulations to gain access to details of solid dynamics. Besides, by identifying the
operating regime, one can apply oscillatory flows with finer steps in pulse frequency
to draw the transitions of pattern intensity in detail. Instead of employing sinusoidal
flows, a step flow can be applied to decouple the time interval of fluidisation and
defluidisation for each cycle. On the other hand, the unique appearance excels the
structured flow as a convenient validation tool for a computational model of gas-solid
flow. Unlike common validation associated with temporally or spatially averaged
quantities, the multiphase models confront a more rigorous test in this validation, as
it requires to predict the specific spatiotemporal evolution of the gas-solid suspension,
which involves the dynamic interplay between bubbles and emulsion, and alternates
between fluidised and defluidised states.
References
1. Chetverikov D, Hanbury A (2002) Finding defects in texture using regularity and local
orientation. Pattern Recognit 35(10):2165–2180
2. Coppens M-O, Regelink MA, van den Bleek CM (2002) Pulsation induced transition from
chaos to periodically ordered patterns in fluidised beds. In: Proceedings of 4th World Congress
on Particle Technology (WCPT), Sydney, pp 1–8
3. de Martín L, Ottevanger C, van Ommen JR, Coppens M-O (2018) Universal stability curve
for pattern formation in pulsed gas-solid fluidized beds of sandlike particles. Phys Rev Fluids
3(3):034303
4. Hajdu A, Harangi B, Besenczi R, Lázár I, Emri G, Hajdu L, Tijdeman R (2016) Measuring
regularity of network patterns by grid approximations using the LLL algorithm. In: Proceedings
of 23rd International Conference on Pattern Recognition (ICPR), Cancun, pp 1524–1529
87
remain largely chaotic, becoming comparable to that of a conventional fluidised
bed. While in the structured regime, bubbles self-organise in predictable triangular
lattices of determined wavelength and angle, manifesting increased homogeneity in
the populations of size, spatial separation as well as residence time. As a result, one
would expect tight control over both solid mixing and interphase transport rate when
operating with structured flows. The results highlight the potential of these dynamically structured flows to be applied as a new principle looking into operation and
design criteria, as well as process scale-up. Nevertheless, the deficiencies of limited
propagation and the operating regime are subjected to further investigation.
Obviously, more features associated with the structured flows remain unexplored.
The experiments and analysis conducted only focus on bubble properties. Without
knowing details about solid dynamics, many features of the pattern, such as circulation rate of solids, alternative nucleation of bubbles, the relationship between bubble
size and separation are still largely unknown. One approach moving forward is to
undertake particle image velocimetry experimental measurements or computational
simulations to gain access to details of solid dynamics. Besides, by identifying the
operating regime, one can apply oscillatory flows with finer steps in pulse frequency
to draw the transitions of pattern intensity in detail. Instead of employing sinusoidal
flows, a step flow can be applied to decouple the time interval of fluidisation and
defluidisation for each cycle. On the other hand, the unique appearance excels the
structured flow as a convenient validation tool for a computational model of gas-solid
flow. Unlike common validation associated with temporally or spatially averaged
quantities, the multiphase models confront a more rigorous test in this validation, as
it requires to predict the specific spatiotemporal evolution of the gas-solid suspension,
which involves the dynamic interplay between bubbles and emulsion, and alternates
between fluidised and defluidised states.
References
1. Chetverikov D, Hanbury A (2002) Finding defects in texture using regularity and local
orientation. Pattern Recognit 35(10):2165–2180
2. Coppens M-O, Regelink MA, van den Bleek CM (2002) Pulsation induced transition from
chaos to periodically ordered patterns in fluidised beds. In: Proceedings of 4th World Congress
on Particle Technology (WCPT), Sydney, pp 1–8
3. de Martín L, Ottevanger C, van Ommen JR, Coppens M-O (2018) Universal stability curve
for pattern formation in pulsed gas-solid fluidized beds of sandlike particles. Phys Rev Fluids
3(3):034303
4. Hajdu A, Harangi B, Besenczi R, Lázár I, Emri G, Hajdu L, Tijdeman R (2016) Measuring
regularity of network patterns by grid approximations using the LLL algorithm. In: Proceedings
of 23rd International Conference on Pattern Recognition (ICPR), Cancun, pp 1524–1529