UCL Academia Focus

Vascular Network Prototype 3D printing Part 2

University College London (UCL) has numerous long-established relations with the commercial world which enables its students to gain practical, hands-on experience beyond the class room environment. One such relationship exists between UCL Mechanical Engineering and leading engineering software solutions provider Desktop Engineering (DTE).

UCL Mechanical Engineering uses 200 student and 35 academic licences of CATIA V5 from Dassault Systèmes. Components commonly used by the students include surface design, assembly design, mechanical design, FE analysis, and modelling. CATIA was chosen by Dr Baker as he sees it as the “industry standard”. The emphasis of the course is geared towards teaching practical engineering, with design and CATIA at the heart of that focus.

In September 2014, UCL’s Faculty of Engineering Sciences introduced a new integrated engineering programme with engineering at its core, with more emphasis on introducing more real-world skills such as sustainability, ethics, and finance with collaborations between students from different departments.

View 3D Vascular Prototype Case Study I >>

All third year students are tasked with designing a product in industry leading software, such as CATIA, which will have commercial value. For this particular case study, we will examine the work of Samuel Meikle-Small and how he is in the process of designing a flow rig for vascular networks which will either validate Magnetic Resonance Imaging (MRI) results or highlight errors in the MRI model – “3D Printing of Vascular Network Prototypes."

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The ability of organs to function efficiently within the body is directly dependent on the delivery of blood. Blood supply is achieved by a complex network of blood vessels, varying in size from capillaries through to arteries. This network brings oxygenated blood within finite distances of every tissue in the body to meet the required metabolic demands. Blood fulfils these demands by carrying dissolved nutrients (e.g. oxygen) and solutes (e.g. drugs), which subsequently diffuse into the intended tissue through the vessel walls. Hence, if the network has an abnormal structure, this will result in limitations in the ability of the network to deliver blood to the organs, resulting in reduced functionality and efficiency.

The performance can, therefore, be analysed as it is tightly correlated to the three-dimensional (3D) positioning of the blood vessels. In contrast to healthy tissue with a coherent 3D structure, tumours are characterized by irregular, inconsistent structures and subsequently compromised nutrient and solute delivery ability.

The project involved processing detailed information on tumours and various liver cancers vascular networks and printing representative flow model networks using 3D printers. The target within the printing section of the project was to design an automated assembly package, which allows for rapid modelling.

MRI machine

Once this step of the project was completed, the next step was to design a flow rig to analyse various blood flow conditions within the printed model networks. The rig needed to be capable of measuring inflow and outflow pressures and velocities. Once tested and proven, the results of the flows are used to parameterise mathematical models of blood flow and oxygen delivery already developed in C++, allowing for the computational exploration of blood flow and oxygen concentration within the networks. The final objective of the project was to set-up the rig within a functioning MRI machine and to analyse the subsequent results that it produced, bridging the gap between the purely mathematical models and the MRI scans.

First Steps

After undertaking initial reading on subjects ranging from vascular modelling, to multi-scale imaging to computational modelling of blood flow, the next step was to choose a fluid in which to test the rig with in order to check that it was possible, given the constraints of a 3D printer. Treacle was chosen for the first round of testing due to its extremely high viscosity.

Once the fluid was chosen, the drafting of the algorithm behind the scaling program commenced. This involved firstly checking the physics behind scaling the model, in order to provide a representative viscous-driven flow, and then to incorporate this into the program. To allow for ease of use and quick changes, a Microsoft Excel spreadsheet was used to make scaling calculations. Whilst working on the scaling spreadsheet, work was undertaken to model a scaled version of a high-density rat tumour network in CATIA, this was done in order to test the printers and modelling process and ultimately ensure more complex networks were possible.

Anatomy of the body in vascular systems

Geometric Network Scaling

Once the initial network was developed, the next step was to model a more complex brain network. The complexity of the networks modelled was increased from 28 segments to 50 segments. In addition, the network contained more 3D elements. When modelling the brain network, time was taken to simplify the process. It was therefore decided that each individual segment should be drawn as a separate part. The final network would start as an assembly file with every segment added in one step while specifying to ‘add with position’. This allowed the entire network to be created in one clean step.

Drawing of First Scaled Networks and Model Fluid Analysis

UCL 3D CATIA vascular finalOnce the network scale program was written, it then became possible to scale the first network for characteristic flow properties. The first network chosen was the high-density rat tumour as this exhibited a suitable complexity. The network was drawn in CATIA. After undertaking the entire scaling process from start to finish with the high-density rat tumour network, the same process was repeated for the brain network. This was of significant importance as the data set for the brain network also contained details of pressures at the inlets to the model.

As scaling a network was possible, a quick set of analysis was completed to validate that treacle or a fluid with a similar dynamic viscosity was suitable. The brain network was scaled to a 50mm X 50mm X 50mm volume, but the dynamic viscosity of the model fluid was varied with each iteration. The corresponding average diameter of the scaled segments was then compared to the dynamic viscosity.

MRI Flow Rig Design

The final design aspect was the conception of the MRI Flow Rig. It was agreed that 3D printing was the ideal way to manufacture the rig due to its consistency with the rest of the project and its ability to quickly manufacture the rig with minimal lead times. An initial design for the rig in a modular format was created. A modular format was chosen to reduce wastage and allow for simple modifications to be made if needed. The rig accommodated weighing the outflow fluid from individual segments as well as ‘settling’ lengths designed to ensure that the fluid was in a laminar regime upon entry to the scanning volume. The fluid was weighed to allow for the calculation of flow rates from each exit segment. This also allows for total loss pressure calculations to be undertaken.

As of today, Samuel has successfully printed the parts and conducted several flow tests.

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Design engineering was traditionally taught using drawing boards which is now antiquated. At UCL Mechanical Engineering we endeavour to push our students to design something that results in an end product and to do this we need the latest technology. This means we have to invest, but in terms of the quality of student we produce it is worth it. We encourage hands-on, problem solving design engineering and solutions like CATIA help us deliver graduates that are more capable of handling the day-to-day challenges of the work environment.
Dr Tim Baker, Lecturer - UCL Mechanical Engineering


Tim Baker, a lecturer at UCL Mechanical Engineering, explains how the relationship with DTE evolved:

During the working week I wear both an academic and a commercial motorsport hat and know DTE from previous work that I have done in the industry. Back in 2002, I was looking for a CAD solution and came across DTE due to a colleague’s recommendation. Since that day I have worked with DTE on a regular basis and have always received a great service. I always find they’re willing to go the extra mile in terms of helping our students with their design projects.

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