RWE Renewables sponsored research studentship in identification methods for offshore wind turbine structures and foundations
4-year DPhil (PhD) studentship starting October 2021
Supervisor: Professor Ross McAdam
A coordinated programme of research at the University of Oxford aims to develop new data driven methods for estimating offshore wind foundation properties in the field. Complementary work packages focus on realistic structural dynamic numerical modelling, laboratory scale dynamic monitoring, system identification algorithm development and field scale validation and analysis. This specific project is sponsored by RWE Renewables and is concerned with the development and implementation of system identification methodologies to estimate the foundation properties of offshore wind turbine structures, using laboratory and field monitored data. Within this overall scope there will be opportunities to develop the project in areas, such as (i) System identification algorithm selection and development for foundation parameter estimation; (ii) Analysis of the influence of foundation model type and effects, such as scouring, on parameter estimation accuracy and observability; (iii) Implementation of data driven methods for analysis of large datasets and estimation of lesser instrumented structures; (iv) Analysis of sensor network optimisation for best/economic parameter estimation. Candidates demonstrating potential for data driven algorithm application and development, with an appreciation of structural dynamic modelling, are sought. The successful applicant will join a strong team of students, academics and industrial partners, and work collaboratively alongside engineers from RWE Renewables.
RWE Renewables is one of the world's leading renewable energy companies, with onshore and offshore wind farms, photovoltaic plants and battery storage facilities having a combined capacity of approximately 9 GW. RWE is the second biggest player in offshore wind globally with an offshore portfolio of 2.5 GW (pro-rata view) in operations.
The successful applicant will be a member of the EPSRC-funded Centre for Doctoral Training in Wind and Marine Energy Systems and Structures (WAMESS CDT), which is a collaboration between the Universities of Strathclyde, Oxford and Edinburgh. Further details of the WAMESS CDT can be found on the course page on the University’s graduate admissions website. The CDT is committed to equality, diversity and inclusion, and is keen to attract applicants from a wide variety of backgrounds.
Candidate Requirements
Prospective candidates will be judged according to how well they meet the following criteria:
· A first class, or strong upper second class, honours degree in general engineering, civil engineering, mechanical engineering, or other relevant discipline such as physics or applied mathematics
· Excellent mathematical skills
· Strong understanding of mechanics and structures
· Ability to undertake scientific programming (e.g. in Matlab, Python, Fortran or C/C++)
· Excellent written and spoken communication skills in English
The following skills are desirable but not essential:
· Knowledge of system identification or structural health monitoring approaches
· Experience of dynamic structural modelling
· Experience of applying data driven approaches to large datasets
Application Procedure
Informal enquiries are encouraged and should be addressed to Professor Ross McAdam (ross.mcadam@eng.ox.ac.uk). Please quote 21ENGCI_RMid in all correspondence.
Formal applications are to be made using the online Graduate Application Form that is accessible through the WAMESS CDT course page (click the “Apply” button on the “How to Apply” tab). When filling out the fields of the Graduate Application Form, please observe the following guidance.
On the “Course” tab:
· Proposed field and title of research project: Identification methods for offshore wind turbine structures and foundations (RWE Renewables)
On the “Funding” tab:
· Are you applying for an advertised studentship competition(s): Yes
· Advertised reference code: 21ENGCI_RMid
Application deadline: 16 April 2021
Start date: October 2021
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