
Fluid mixing usually involves the interplay between advection and diffusion, which together cause any initial distribution of passive scalar to homogenize and ultimately reach a uniform state. However, this scenario only holds when the velocity field is nondivergent and has no normal component to the boundary. If either condition is unmet, such as for active particles in a bounded region, floating particles, or for filters, then the ultimate state after a long time is not uniform, and may be time dependent. We show that in those cases of nonuniform mixing it is preferable to characterize the degree of mixing in terms of an f-divergence, which is a generalization of relative entropy, or to use the $L^1$ norm. Unlike concentration variance ($L^2$ norm), the f-divergence and $L^1$ norm always decay monotonically, even for nonuniform mixing, which facilitates measuring the rate of mixing. We show by an example that flows that mix well for the nonuniform case can be drastically different from efficient uniformly mixing flows.
24 pages, 7 figures. RevTeX 4-2 format. To appear in Physical Review Fluids
Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Physics - Fluid Dynamics
Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Physics - Fluid Dynamics
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 6 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
