- Letter
- Open Access
Mutual information as a measure of mixing efficiency in viscous fluids
Phys. Rev. Research 6, L022050 – Published 3 June, 2024
DOI: https://doi.org/10.1103/PhysRevResearch.6.L022050
Abstract
Because of the kinematic reversibility of the Stokes equation, fluid mixing at the microscale requires an interplay between advection and diffusion. Here, we introduce mutual information between particle positions before and after mixing as a measure of mixing efficiency. We demonstrate its application in a Couette flow in an annulus and show that nonuniform rotation sequences can lead to more efficient mixing. We also determine mutual information from Brownian dynamics simulations using data compression algorithms. Our results show that mutual information provides a universal and assumption-free measure of mixing efficiency in microscale flows.
Physics Subject Headings (PhySH)
Article Text
Supplemental Material
References (56)
- I. A. Martínez, A. Petrosyan, D. Guéry-Odelin, E. Trizac, and S. Ciliberto, Engineered swift equilibration of a Brownian particle, Nat. Phys. 12, 843 (2016).
- Z. Lu and O. Raz, Nonequilibrium thermodynamics of the Markovian Mpemba effect and its inverse, Proc. Natl. Acad. Sci. USA 114, 5083 (2017).
- M. V. Berry, Transitionless quantum driving, J. Phys. A: Math. Theor. 42, 365303 (2009).
- A. D. Stroock, S. K. W. Dertinger, A. Ajdari, I. Mezić, H. A. Stone, and G. M. Whitesides, Chaotic mixer for microchannels, Science 295, 647 (2002).
- C. J. Campbell and B. A. Grzybowski, Microfluidic mixers: from microfabricated to self-assembling devices, Philos. Trans. R. Soc. London A 362, 1069 (2004).
- R. O. Grigoriev, M. F. Schatz, and V. Sharma, Chaotic mixing in microdroplets, Lab Chip 6, 1369 (2006).
- D. J. Pine, J. P. Gollub, J. F. Brady, and A. M. Leshansky, Chaos and threshold for irreversibility in sheared suspensions, Nature (London) 438, 997 (2005).
- H. Aref, J. R. Blake, M. Budišić, S. S. S. Cardoso, J. H. E. Cartwright, H. J. H. Clercx, K. El Omari, U. Feudel, R. Golestanian, E. Gouillart, G. J. F. van Heijst, T. S. Krasnopolskaya, Y. Le Guer, R. S. MacKay, V. V. Meleshko, G. Metcalfe, I. Mezić, A. P. S. de Moura, O. Piro, M. F. M. Speetjens et al., Frontiers of chaotic advection, Rev. Mod. Phys. 89, 025007 (2017).
- W. Supatto, S. E. Fraser, and J. Vermot, An all-optical approach for probing microscopic flows in living embryos, Biophys. J. 95, L29 (2008).
- N. Uchida and R. Golestanian, Synchronization and collective dynamics in a carpet of microfluidic rotors, Phys. Rev. Lett. 104, 178103 (2010).
- M. Rahbar, L. Shannon, and B. L. Gray, Microfluidic active mixers employing ultra-high aspect-ratio rare-earth magnetic nano-composite polymer artificial cilia, J. Micromech. Microeng. 24, 025003 (2014).
- Y. Ding, J. C. Nawroth, M. J. McFall-Ngai, and E. Kanso, Mixing and transport by ciliary carpets: a numerical study, J. Fluid Mech. 743, 124 (2014).
- S. Jonas, E. Zhou, E. Deniz, B. Huang, K. Chandrasekera, D. Bhattacharya, Y. Wu, R. Fan, T. M. Deserno, M. K. Khokha, and M. A. Choma, A novel approach to quantifying ciliary physiology: microfluidic mixing driven by a ciliated biological surface, Lab Chip 13, 4160 (2013).
- J. C. Nawroth, H. Guo, E. Koch, E. A. C. Heath-Heckman, J. C. Hermanson, E. G. Ruby, J. O. Dabiri, E. Kanso, and M. McFall-Ngai, Motile cilia create fluid-mechanical microhabitats for the active recruitment of the host microbiome, Proc. Natl. Acad. Sci. USA 114, 9510 (2017).
- R. Golestanian, J. M. Yeomans, and N. Uchida, Hydrodynamic synchronization at low Reynolds number, Soft Matter 7, 3074 (2011).
- J. Arrieta, J. H. E. Cartwright, E. Gouillart, N. Piro, O. Piro, and I. Tuval, Geometric mixing, Philos. Trans. R. Soc. London A 378, 20200168 (2020).
- E. Villermaux, Mixing versus stirring, Annu. Rev. Fluid Mech. 51, 245 (2019).
- E. Tang and R. Golestanian, Quantifying configurational information for a stochastic particle in a flow-field, New J. Phys. 22, 083060 (2020).
- P. V. Danckwerts, The definition and measurement of some characteristics of mixtures, Appl. Sci. Res. 3, 279 (1952).
- J.-L. Thiffeault, Using multiscale norms to quantify mixing and transport, Nonlinearity 25, R1 (2012).
- D. D'Alessandro, M. Dahleh, and I. Mezic, Control of mixing in fluid flow: a maximum entropy approach, IEEE Trans. Autom. Control 44, 1852 (1999).
- M. Camesasca, M. Kaufman, and I. Manas-Zloczower, Quantifying fluid mixing with the Shannon entropy, Macromol. Theory Simul. 15, 595 (2006).
- G. B. Brandani, M. Schor, C. E. MacPhee, H. Grubmüller, U. Zachariae, D. Marenduzzo, and C. M. Aegerter, Quantifying disorder through conditional entropy: An application to fluid mixing, PLoS One 8, e65617 (2013).
- R. Kree and A. Zippelius, Chaos and mixing in self-propelled droplets, Phys. Rev. Fluids 4, 113102 (2019).
- J.-L. Thiffeault, Nonuniform mixing, Phys. Rev. Fluids 6, 090501 (2021).
- Z. B. Stone and H. A. Stone, Imaging and quantifying mixing in a model droplet micromixer, Phys. Fluids 17, 063103 (2005).
- G. Mathew, I. Mezić, and L. Petzold, A multiscale measure for mixing, Physica D 211, 23 (2005).
- Y.-K. Tsang, T. M. Antonsen, and E. Ott, Exponential decay of chaotically advected passive scalars in the zero diffusivity limit, Phys. Rev. E 71, 066301 (2005).
- P. Meunier and E. Villermaux, The diffuselet concept for scalar mixing, J. Fluid Mech. 951, A33 (2022).
- T. Schürmann and P. Grassberger, Entropy estimation of symbol sequences, Chaos 6, 414 (1996).
- R. Avinery, M. Kornreich, and R. Beck, Universal and accessible entropy estimation using a compression algorithm, Phys. Rev. Lett. 123, 178102 (2019).
- S. Martiniani, P. M. Chaikin, and D. Levine, Quantifying hidden order out of equilibrium, Phys. Rev. X 9, 011031 (2019).
- M. Zu, A. Bupathy, D. Frenkel, and S. Sastry, Information density, structure and entropy in equilibrium and non-equilibrium systems, J. Stat. Mech. (2020) 023204.
- A. Ziepke, I. Maryshev, I. S. Aranson, and E. Frey, Multi-scale organization in communicating active matter, Nat. Commun. 13, 6727 (2022).
- G. Ariel and H. Diamant, Inferring entropy from structure, Phys. Rev. E 102, 022110 (2020).
- E. Brigatti and F. N. M. de Sousa Filho, Comment on “Universal and accessible entropy estimation using a compression algorithm”, Phys. Rev. Lett. 129, 029801 (2022).
- F. N. M. de Sousa Filho, V. G. Pereira de Sá, and E. Brigatti, Entropy estimation in bidimensional sequences, Phys. Rev. E 105, 054116 (2022).
- https://github.com/hxim/paq8px (2023).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.6.L022050 for movies showing the time dependence of probability density, comparison with a conventional mixing norm (variance of concentration), and a 1D test case for compression algorithms.
- T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd ed. (Wiley, Hoboken, NJ, 2005).
- C. Dieball and A. Godec, Coarse graining empirical densities and currents in continuous-space steady states, Phys. Rev. Res. 4, 033243 (2022).
- J. Happel and H. Brenner, Low Reynolds Number Hydrodynamics (Springer, Dordrecht, 1983).
- M. V. Mahoney, Adaptive weighing of context models for lossless data compression, Tech. Rep. CS-2005-16 (Florida Institute of Technology, 2005), https://cs.fit.edu/media/TechnicalReports/cs-2005-16.pdf.
- B. Knoll and N. de Freitas, A machine learning perspective on predictive coding with PAQ8, in Data Compression Conference (DCC) (IEEE, New York, 2012), pp. 377–386.
- G. I. Taylor, Dispersion of soluble matter in solvent flowing slowly through a tube, Proc. R. Soc. London, Ser. A 219, 186 (1953).
- M. F. Eggl and P. J. Schmid, Mixing enhancement in binary fluids using optimised stirring strategies, J. Fluid Mech. 899, A24 (2020).
- M. F. Eggl and P. J. Schmid, Mixing by stirring: Optimizing shapes and strategies, Phys. Rev. Fluids 7, 073904 (2022).
- O. Gubanov and L. Cortelezzi, Towards the design of an optimal mixer, J. Fluid Mech. 651, 27 (2010).
- Z. Lin, J.-L. Thiffeault, and C. R. Doering, Optimal stirring strategies for passive scalar mixing, J. Fluid Mech. 675, 465 (2011).
- D. P. G. Foures, C. P. Caulfield, and P. J. Schmid, Optimal mixing in two-dimensional plane Poiseuille flow at finite Péclet number, J. Fluid Mech. 748, 241 (2014).
- L. Vermach and C. P. Caulfield, Optimal mixing in three-dimensional plane Poiseuille flow at high Péclet number, J. Fluid Mech. 850, 875 (2018).
- D. Saintillan and M. J. Shelley, Instabilities, pattern formation, and mixing in active suspensions, Phys. Fluids 20, 123304 (2008).
- P. Mueller and J.-L. Thiffeault, Fluid transport and mixing by an unsteady microswimmer, Phys. Rev. Fluids 2, 013103 (2017).
- H. Reinken, S. H. L. Klapp, and M. Wilczek, Optimal turbulent transport in microswimmer suspensions, Phys. Rev. Fluids 7, 084501 (2022).
- J. den Toonder, F. Bos, D. Broer, L. Filippini, M. Gillies, J. de Goede, T. Mol, M. Reijme, W. Talen, H. Wilderbeek, V. Khatavkar, and P. Anderson, Artificial cilia for active micro-fluidic mixing, Lab Chip 8, 533 (2008).
- A. R. Shields, B. L. Fiser, B. A. Evans, M. R. Falvo, S. Washburn, and R. Superfine, Biomimetic cilia arrays generate simultaneous pumping and mixing regimes, Proc. Natl. Acad. Sci. USA 107, 15670 (2010).