PhD Positions in Digital Methods for Life Cycle Analysis, O&M and Prognostics

Do you want to become part of the solutions that deliver the green energy transition? Do you wish to contribute to the development and further digitalization of wind energy, and are you looking for a career in R&D or academia? Then we have an opportunity for you.

Responsibilities and qualifications

We seek PhD students who can contribute to the advancement of our wind turbine and component digital twin frameworks through developing the research-based technology enablers in several potential work areas. Depending on the candidates’ qualifications, we target one or more of the following individual PhD project topics:

• Decision tools for autonomous wind turbine operation. This project focuses on improving models for fault detection based on operational data such as SCADA or inspections, fault identification and prognostics, developing indicators for turbine health status, decision modeling, uncertainty quantification and risk analysis.
• Uncertainty quantification of the wind turbine modeling chain. All digital models and the data that feeds them come with uncertainty that can affect decisions and lead to non-optimal design and operation. The scope of this project is to carry out a systematic uncertainty quantification on the various elements of the wind turbine design and prognostics model chains. The project activities include parametric studies, data processing, surrogate model training, uncertainty analysis, meta-studies. Potentially field validation if data is available.
• Development of conceptual physics based wind turbine drive train models capable of coupling the operation state with the electromagnetic – mechanical – thermal response in order to evaluate the lifetime of specific components. Secondly estimating the sustainability impact of the material used in the drive trains (steel, copper, permanent magnets and superconductors) should be targeted.
• Life cycle analysis of next generation offshore and floating wind farms. This project aims to develop high fidelity probabilistic cost models, focusing in all stages of the project development and with particular focus in optimizing operation and maintenance expenditure. The project will consider reliability distributions across a wind farm as well as quantify reliability of novel components through concepts of quantitative reliability assessment.
• Data-driven asset management for large scale wind farms. This project aims to perform a mapping of data relevant to the design and operation of offshore wind farms, with a view to optimize through life operation through maximizing value of data. The developed methods should consider future trends including floating wind farms and autonomous systems.

You will be part of the SIL team with experts in the field of structural dynamics, wind turbine drive train design, applied statistics and Machine Learning. We work on problems spanning from component to wind farm level. The Structural Integrity and Loads Assessment Section within DTU, is currently running a number of EU projects which may provide input to this projects, including Flagship, Hiperwind and Realcoe, with participation of a number of key players active in the wind energy market.

Application
Please submit your online application no later than 31 May 2022 (Danish time).