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

TL;DR

This paper introduces a comprehensive optimization method for branched fluidic networks that accounts for laminar, turbulent, non-Newtonian flows, and wall roughness, unifying previous approaches and enabling realistic network design.

Contribution

A unifying optimization framework based on the Darcy friction factor that covers all flow regimes and fluid models, including complex wall roughness and mixed flow types.

Findings

Analytical and graphical optimal channel radii are provided.

The relationship between flow rate and radius varies with Reynolds number.

Previously used exponents in flow optimization are not valid for turbulent flows.

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, no single optimisation approach captures these limit cases. Furthermore, realistic fluidic networks exhibit intermediate points in the parameter space that can hardly be optimised. 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. We optimise fluidic networks for the entire parameter space: Laminar and turbulent flows, including networks that exhibit both flow types; Non-Newtonian fluid models (power-law, Bingham, and Herschel-Bulkley); and Networks with arbitrary wall roughness, including non-uniform relative roughness. The optimal channel radii are presented analytically and graphically. Finally, the parameter $x$ in the optimisation relationship $Q\propto R^x$ was approximated as a function of the Reynolds number, revealing that previously-determined values of $x$ hardly apply to realistic turbulent flows. Our approach can be extended to other configurations for which the friction factor is known, such as different channel curvatures or wall slip conditions, enabling optimisation of a wide range of fluidic networks.