- PhD Project Title
- Efficient ensemble simulation methods for in silico trials of endovascular medical devices
Efficient ensemble simulation methods for in silico clinical trials of endovascular medical devices
I graduated from the University of Manchester in July 2018 with a Masters degree in Mathematics and Physics. During this degree I enjoyed studying applied mathematics in particular, with modules on continuum mechanics and fluids being highlights. In my final year I undertook two projects on the fluid mechanics of blood flow and on the dynamics of thermals. These projects made me want to pursue further research in the field.
Fluid dynamics in biomedical applications, haemodynamics, reduced order modelling, machine learning, intracranial aneurysms, in silico clinical trials.
In silico trials refer to those performed, entirely or in part, using individualised computer modelling and simulation to virtually test some aspect of a medical device, drug or clinical procedure across a large range of anatomical morphologies and physiologies. This allows for better judgement of performance and safety across the global population. However, exploring the design parameter space for a wide range of variation in the population requires a vast number of computational simulations, which is prohibitively expensive, both in cost and time. This means that the success of in silico trials depends upon the development of inexpensive computational models that can accelerate the simulations without making unacceptable sacrifices in accuracy.
The first aim of my project is to develop an accelerated in silico clinical trial framework in the context of endovascular medical devices, which means medical devices positioned within a blood vessel, such as stents or flow diverters. The techniques I shall investigate for accelerating the simulations shall primarily be reduced order modelling and machine learning. Once the in silico clinical trial framework is in place, the second aim of my project is to carry out a mock in silico trial for the performance assessment of flow diverters in the treatment of intracranial aneurysms.
Why I chose the CDT in Fluid Dynamics
I chose the CDT because I wanted the chance to learn about more of the research areas in the field before choosing my project. I get the chance to do this through weekly seminars which are often on applications of fluid dynamics that I hadn’t previously considered.
I also chose the CDT because working as part of a cohort appeals to me. I think being part of a cohort will keep me well informed of research in areas of fluid dynamics that aren’t directly related to my own project.