Optimising branched fluidic networks: a unifying approach including laminar and turbulent flows, rough walls and non-Newtonian fluids

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Abstract

Power minimisation in branched fluidic networks has gained significant attention in biology and engineering. The optimal network is defined by channel radii that minimise the sum of viscous dissipation and the volumetric energetic cost of the fluid. For limit cases including laminar flows, high-Reynolds-number turbulence or smooth-channel approximations, optimal solutions are known. However, current methods do not allow optimisation for a large intermediate part of the parameter space which is typically encountered in realistic fluidic networks that exhibit turbulent flow. Here, we present a unifying optimisation approach based on the Darcy friction factor, which has been determined for a wide range of flow regimes and fluid models and is applicable to the entire parameter space: (i) laminar and turbulent flows, including networks that exhibit both flow types, (ii) non-Newtonian fluids (powerlaw, Bingham and Herschel-Bulkley) and (iii) networks with arbitrary wall roughness, including non-uniform relative roughness. The optimal channel radii are presented analytically and graphically. All existing limit cases are recovered, and a concise framework is presented for systematic optimisation of fluidic networks. Finally, the parameter in the optimisation relationship, with the flow rate and the channel radius, was approximated as a function of the Reynolds number, revealing in which case the entire network can be optimised based on one optimal channel radius, and in which case all radii must be optimised individually. Our approach can be extended to a wide range of fluidic networks for which the friction factor is known, such as different channel curvatures, bubbly flows or specific wall slip conditions.

Original languageEnglish
Article numberA42
JournalJournal of fluid mechanics
Volume1011
Early online date15 May 2025
DOIs
Publication statusPublished - 25 May 2025

Keywords

  • UT-Hybrid-D

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