EngD Project Title: Application and use cases for artificial intelligence/machine learning in the delivery of wind resource/energy yield assessment (pre-construction and operational) for wind farms.

Industrial Partner: Wood Group

Educational and professional background:

I am a graduate of an integrated Master’s (MEng) degree in Aerospace Engineering from the University of Bristol. Here, my research and interests primarily focused on exploring the application of bio-inspired principles to modern aircraft design – both during my thesis, and also during a summer internship at the University of Bristol and the University of West England.

What attracted you to studying with IDCORE?

As I knew I wanted to join the renewable energy sector, I chose to study IDCORE as it provided a clear pathway to achieving my aspirations and advancing my career. The opportunity to work closely with both industry and academia was particularly appealing, offering me a deeper understanding of the sector’s challenges, while also enabling me to serve as a bridge between the two.

What attracted you to the Renewable Energy Sector? What aspects of the industry do you find most inspiring, interesting, or important for the future?

Moving into offshore renewables was especially compelling, as it allowed me to leverage my expertise in aerodynamics and modelling, while also aligning with my strong commitment to tackling climate change. Upon enrolling in this programme, I then found myself drawn to the passion shared by researchers and developers in the field. What I found most admirable was the strong sense of camaraderie among competing developers, where the success of one company is cherished and viewed as a success for offshore renewable energy as a whole.

What ambition would you like to fulfil as a Research Engineer?

My project, in collaboration with the Wood Group, explores the application of machine learning to wind energy yield assessments. My immediate goal is therefore to enhance and streamline current wind resource characterisation processes, either by improving accuracy or by reducing the overall time required. By the end of the programme, I aim to become a specialist in the field of machine learning, and continue applying my expertise to the exciting and impactful sector of renewable energy.

Personal motivation and beliefs

I believe that true fulfilment and happiness comes from helping others and making a meaningful contribution to society. This is why I am committed to furthering the current state of renewable energy technologies,  and doing my part in combating climate change.