Sanjiv Gunasekera    LinkedIn

PhD Candidate, University of New South Wales, Australia. 

 

PhD Topic:

"Experimental and numerical analysis of a stented arteriovenous fistula"

Research interests: Particle Image Velocimetry, Computational Fluid Dynamics, Haemodynamics, Biofluids, Clinical Diagnostics and Imaging

Email: s.gunasekera@unsw.edu.au

Current Project: Evaluate the unsteadiness of the flow within a stented arteriovenous fistula

 

 

Cleansing our blood is an automatic bodily process that happens to a majority of us. However, 10% of Australians suffer from chronic kidney disease, where their kidneys progressively lose the ability to filter blood. One way of overcoming this is by creating an arteriovenous fistula referred to as an AVF to enable filtration via an external machine. Although the AVF helps solve one problem, it faces challenges of its own. AVF failure rates are very high, requiring further intervention or treatment like an implantation of metallic scaffolding that hold open a diseased vessel, which we also refer to as a stent. The high AVF failure rates are caused by vascular disease that constrict the vessels. Disturbances in the blood flow is a factor in the onset of disease in the AVF.

My research involves analysing the flow within the AVF to understand its features and its impact on disease initiation. To delve into the blood flow, we obtain ultrasound scans of the patients’ AVFs and measure the general blood flow rates. I use this data to create a virtual geometry of the AVF and run (Computational Fluid Dynamics – CFD) simulations using the initial flow rates. The simulations are run with modelling assumptions to replicate the physical properties of the blood (using non-Newtonian functions) and turbulent flow behaviour (using turbulence models – k-ω SST). To complement the simulations with a perspective free from modelling, I also fabricated a transparent benchtop geometry of the AVF. A transparent fluid with blood-like properties is pumped through this. High speed cameras are used to capture the flow by tracking small reflective particles in the fluid (Particle Image Velocimetry – PIV). The two flow measuring methods, computational simulations and benchtop experiments, give high resolution velocity information. The velocities are analysed in space and across time to find flow features that have an impact on the vessel wall. The computational simulations are extended by also assessing the effect of stent implantation.

 

Understanding the specific flow features that impact the vessel wall, would detail the relationship between blood flow behaviour and tendency for disease onset in an AVF. This understanding will firstly, help surgeons and clinicians when creating or treating AVFs. Secondly it will help medical device (or stent) manufacturers in designing products that diminish flow features that cause AVF failure. The ultimate goal would be to minimise the amount of maintenance the AVF requires thereby improving the quality of life of patients with AVFs.

Education/Employment

2018-present PhD Mechanical Engineering, UNSW, Sydney

2016-2017 Undergraduate Mechanical Engineer, AECOM, Sydney

2013-2017 BEng Honours Class 1 (Mechanical Engineering), UNSW, Sydney

Publications

Journal articles

 

Gunasekera, S.; Ng, O.; Thomas, S.; Varcoe, R.; de Silva, Charitha.; & Barber, T. (2020). Tomographic PIV analysis of physiological flow conditions in a patient-specific arteriovenous fistula. Experiments in Fluids. (under review)

Conference papers

 

Gunasekera, S.; Ng, O.; Thomas, S.; Varcoe, R.; de Silva, Charitha.; & Barber, T. (2019). Towards 3D PIV measurements of a patient-specific arteriovenous fistula. 9th Australian Conference on Laser Diagnostics, 87-90. 

Gunasekera, S.; Ng, O.; Thomas, S.; Varcoe, R.; de Silva, Charitha.; & Barber, T. (2019). Flow analysis in a patient-specific arteriovenous fistula. Australian Biomedical Engineering Conference 2019.

Ng, O. XW; Gunasekera, Sanjiv D; Thomas, Shannon D; Varcoe, Ramon L & Barber, Tracie J. (2019) Arteriovenous Fistula (AVF) failure prediction through patient-specific (PS) modelling. Australian Biomedical Engineering Conference 2019.

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