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A generalized optimization principle for asymmetric branching in fluidic networks

When applied to a branching network, Murray’s law states that the optimal branching of vascular networks is achieved when the cube of the parent channel radius is equal to the sum of the cubes of the daughter channel radii. It is considered integral to understanding biological networks and for the b...

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Detalles Bibliográficos
Autores principales: Stephenson, David, Lockerby, Duncan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4971259/
https://www.ncbi.nlm.nih.gov/pubmed/27493583
http://dx.doi.org/10.1098/rspa.2016.0451
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author Stephenson, David
Lockerby, Duncan A.
author_facet Stephenson, David
Lockerby, Duncan A.
author_sort Stephenson, David
collection PubMed
description When applied to a branching network, Murray’s law states that the optimal branching of vascular networks is achieved when the cube of the parent channel radius is equal to the sum of the cubes of the daughter channel radii. It is considered integral to understanding biological networks and for the biomimetic design of artificial fluidic systems. However, despite its ubiquity, we demonstrate that Murray’s law is only optimal (i.e. maximizes flow conductance per unit volume) for symmetric branching, where the local optimization of each individual channel corresponds to the global optimum of the network as a whole. In this paper, we present a generalized law that is valid for asymmetric branching, for any cross-sectional shape, and for a range of fluidic models. We verify our analytical solutions with the numerical optimization of a bifurcating fluidic network for the examples of laminar, turbulent and non-Newtonian fluid flows.
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spelling pubmed-49712592016-08-04 A generalized optimization principle for asymmetric branching in fluidic networks Stephenson, David Lockerby, Duncan A. Proc Math Phys Eng Sci Research Articles When applied to a branching network, Murray’s law states that the optimal branching of vascular networks is achieved when the cube of the parent channel radius is equal to the sum of the cubes of the daughter channel radii. It is considered integral to understanding biological networks and for the biomimetic design of artificial fluidic systems. However, despite its ubiquity, we demonstrate that Murray’s law is only optimal (i.e. maximizes flow conductance per unit volume) for symmetric branching, where the local optimization of each individual channel corresponds to the global optimum of the network as a whole. In this paper, we present a generalized law that is valid for asymmetric branching, for any cross-sectional shape, and for a range of fluidic models. We verify our analytical solutions with the numerical optimization of a bifurcating fluidic network for the examples of laminar, turbulent and non-Newtonian fluid flows. The Royal Society Publishing 2016-07 /pmc/articles/PMC4971259/ /pubmed/27493583 http://dx.doi.org/10.1098/rspa.2016.0451 Text en © 2016 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Research Articles
Stephenson, David
Lockerby, Duncan A.
A generalized optimization principle for asymmetric branching in fluidic networks
title A generalized optimization principle for asymmetric branching in fluidic networks
title_full A generalized optimization principle for asymmetric branching in fluidic networks
title_fullStr A generalized optimization principle for asymmetric branching in fluidic networks
title_full_unstemmed A generalized optimization principle for asymmetric branching in fluidic networks
title_short A generalized optimization principle for asymmetric branching in fluidic networks
title_sort generalized optimization principle for asymmetric branching in fluidic networks
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4971259/
https://www.ncbi.nlm.nih.gov/pubmed/27493583
http://dx.doi.org/10.1098/rspa.2016.0451
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