This is a potential research project for new research students. It is intended to be a starting point for discussion/further investigation rather than a complete research plan. If you are interested in working with me, see opportunities for new researchers.
Keywords: surrogate modelling, machine learning, reservoir computing, power electronics
Designing new devices, particularly in electrical power conversion for renewable energy, is often challenging because of constraints on mass, volume, and cost. Designers must innovate within these boundaries, making trade-offs to meet specifications without compromising performance. This PhD project will employ surrogate modelling and machine learning to improve the efficiency of design processes.
Power electronic device design involves choosing from a limited library of existing parts as well as dealing with behaviors across timescales, from nano-second switching to bulk behaviour over several seconds. Commercial simulation tools, such as Plex, offer accuracy but lack the computational speed needed for quick iterative design. Collaborating with Dr. Ian Laird, who brings extensive experience in power system design optimisation, this project aims to provide the fundamental research developments needed to create rapid design tools.
The project will focus on developing surrogate models using reservoir computing techniques to accelerate simulations for components like modular multi-level converters (MMCs), crucial in renewable energy systems. These models will enable faster design iteration, optimising systems to meet specific application constraints, such as those for offshore wind turbines. Key challenges include modelling the discrete switching of electrical components and the wide range of dynamic timescales. Addressing these challenges is essential for capturing sudden changes and complex interactions, with potential applications extending beyond power systems to fields like synthetic biology and electromagnetic sensing.
The anticipated result is a tool for rapidly designing optimized electrical power systems, streamlining resource use and cost in system deployment. The research has the potential to yield significant publications with broad impacts across multiple disciplines.
If you are interested in working on this or a related project, please email david.barton@bristol.ac.uk with a copy of your CV and a short description of your research interests; we can co-develop a more complete research statement tailored to you. Applications from all backgrounds are encouraged, especially under-represented groups.