Cranfield University, in collaboration with Semtronics UK and international academic partners, is offering a PhD studentship focused on enhancing the efficiency and reliability of offshore wind turbines through advanced digital twin and machine learning technologies. This project will investigate existing digital twin and machine learning models and find knowledge gaps, leverage public and industrial datasets to develop scalable machine learning based digital twin models to improve performance, decision-making and reduce costs. A bursary will be provided of up to £20,000 tax-free plus fees for three years.

While digital twins are emerging in various sectors, their application in wind turbines remains underexplored, marked by critical knowledge gaps in integration with advanced machine learning. Research in comprehensive digital twins for wind farms, integrating diverse data sources, is limited. Current use of machine learning in predictive maintenance lacks depth in advanced algorithms like deep learning, essential for complex data. There’s also a gap in real-time data processing methods for immediate operational adjustments. Furthermore, the use of synthetic data for digital twin 4alidationn, especially against real-world conditions, is not well-developed. Finally, the scalability and adaptability of these models across different wind farm conditions is a significant challenge, with most models being turbine specific and not universally applicable. Addressing these gaps is crucial for enhancing wind turbine performance and reducing costs.

This PhD project aims to bridge key gaps in wind energy optimization by integrating digital twin technology with advanced machine learning. It involves developing a holistic digital twin model that incorporates various data from wind farm operations, serving as a platform for applying and honing sophisticated machine learning methods like deep learning for enhanced predictive maintenance. The project prioritizes real-time data processing for dynamic operational adjustments and employs both synthetic and real sensor data for thorough model validation, ensuring robust performance in diverse conditions. Additionally, the project will explore the potential benefits of using digital twins to optimize the performance of wind farms, while identifying any associated limitations or challenges.

At a glance

  • Application deadline28 Aug 2024
  • Award type(s)PhD
  • Start date30 Sep 2024
  • Duration of award3 years
  • EligibilityUK
  • Reference numberSATM465

Entry requirements

Applicants should have a 1st or 2.1 UK degree or an equivalent in a discipline related to electrical engineering, energy, or computer science. The ideal candidate should have background of electrical and computer and have strong programming experiences for wind turbines. The candidate should be self-motivated, possess good communication skills for regular interaction with other stakeholders, with an aptitude for industrial research.

Funding

A bursary will be provided of up to £20,000 tax-free plus fees for three years.

To be eligible for this funding, applicants must be classified as a home student. We require that applicants are under no restrictions regarding how long they can stay in the UK.

About the sponsor

Sponsored by EPSRC and Cranfield University.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

For further information please contact: 
 

If you are eligible to apply for this studentship, please complete the online application form.

Diversity and Inclusion at Cranfield

At Cranfield, we value our diverse staff and student community and maintain a culture where everyone can work and study together harmoniously with dignity and respect. This is reflected in our University values of ambition, impact, respect and community. We welcome students and staff from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing.

We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum.