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dc.contributor.authorAguilar Garza, Dulce
dc.date.accessioned2021-10-19T19:58:33Z
dc.date.available2021-10-19T19:58:33Z
dc.date.submitted2020-10-08
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/329629
dc.description.abstractFabrication of functional vascularised three-dimensional tissue constructs has been a long-standing objective in the field of tissue engineering. Currently, the main limitation in this field is the inability to produce fully vascularised tissue with an internal mass transport system (vascular network) that can provide cells with nutrients and oxygen while removing waste, to imitate the functions of human living tissue. Achieving such a system would allow the development of large-scale tissue constructs and increase the potential for in vivo integration. There are different approaches to attempt vascularisation, which use a diversity of techniques. Among these, one of the most promising is additive manufacturing due to its versatility, reproducibility, and compatibility with suitable materials. With the aim of contributing towards the efforts in this field, the present work presents a method for the automatic generation of physiologically-based vascular network structures as solid 3D models suitable for additive manufacturing technologies. Considering the natural hierarchical branching vasculature as an ideal solution, an algorithm was developed to generate branching tree structures connected at the ends to form vascular networks. The implementation is based on previous work in the field of computational bio-simulation of arterial tree growth. It consists of a space-filling algorithm that connects all given points to a growing tree within a defined three-dimensional volume, while fulfilling constraints associated with the physiological laws of circulation. The networks are generated using a CAD environment and thus can be used in additive manufacturing processes. An investigation was carried out on the effect of three input parameters (namely volumetric flow rate, pressure difference across the tree, and number of terminal points) in order to find a suitable combination of parameters that would produce networks with diameters above the fabrication threshold. In order to demonstrate feasibility and functionality of the networks fabricated using this proposed method, two network models were produced by 3D printing and subsequently used as a sacrificial structure to produce PDMS blocks with the hollow vascular networks embedded in it. Particle tracking was used to measure the flow velocity in the channels at two different inlet flow rates. Comparisons were made with theoretical values obtained from computational fluid dynamics simulations and show a good agreement between experiment and theory. From the measurements of maximum velocity, it was observed that at a lower flow rate, the experimental values were closer to the theoretical values than at a higher flow rate. This might be due to the challenges that higher flow rates represent, such as less accurate particle tracking. Given the overall agreement, it is concluded that computational fluid dynamics simulations are a fast and effective way to analyse flow in vascular network models produced by the method here proposed.
dc.description.sponsorshipThe Cambridge Trust, CONACyT (Consejo Nacional de Ciencia y Tecnologia), EPSRC Cambridge & Cranfield Doctoral Training Centre in Ultra Precision
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectVascular networks
dc.subjectAdditive manufacturing
dc.subject3D printing
dc.subjectAlgorithmic
dc.subjectCFD
dc.subjectParticle Tracking Velocimetry
dc.subjectTissue Engineering
dc.titleAlgorithmic generation of vascular network models for additive manufacturing
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.identifier.doi10.17863/CAM.77078
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
rioxxterms.typeThesis
dc.publisher.collegeLucy Cavendish
dc.type.qualificationtitlePhD in Engineering (Ultra Precision)
pubs.funder-project-idEPSRC (1567042)
cam.supervisorMarkaki, Athina
cam.supervisor.orcidMarkaki, Athina [0000-0002-2265-1256]


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