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Fatigue and corrosion are critical failure causes of offshore wind support structures. The low accessibility and high inspection/repair costs of large structures in corrosive environments is motivating remote monitoring and optimized inspection and maintenance plans, based on continuous assessment of the structure reliability. Due to the time invariant uncertainties associated with the applied loads, stress prediction and modelling of the deterioration mechanisms, a more advanced lifecycle reliability assessment is necessary to assess the structural safety and to support decision making. The project aims to reduce the uncertainties by combining inspection data (about cracks and corrosion), load measurements and estimates (stresses, wind, wave) and improved material property models. This information can be employed to regularly update the failure probabilities of welded joints and thus structural reliability. This also necessitates to have reliable models that can simulate the real behavior of the structure to predict qualitatively and quantitatively the degradations due to corrosion and fatigue. These numerical models will be calibrated with available and new field data (unique longterm load measurements on offshore wind turbines). The global goal is to get a much better estimation of the remaining life of in service wind turbines and to derive optimized inspection and maintenance plans for a group of similar structures.
YOUR RESEARCH GOAL
The aim of the research is to further develop the concepts of virtual sensing and the fleet leader. The concept of virtual sensing is to use a limited number of available sensors on a monitored structure to determine the stress at all fatigue critical locations (hot spots), especially when these are unsuitable for direct sensor placement. Virtual sensing enables to assess the stress histories of all hot spots and determine their exact fatigue damage. At VUB virtual sensing has been developed and validated for application to OWTs on monopile foundations. Virtual sensing would be even more valuable for more complex welded structures such as nodes in off- shore wind jacket foundations, but this has only been tentatively investigated. The presence of local deformation modes which might not be captured by equally distributed sensors poses a big challenge. In addition, virtual sensing for strongly damped systems is still topic of research. For large scale structures it is expensive to have a dedicated integrity monitoring system installed. For obvious economic reasons, only a limited number of offshore wind turbines in a park can be instrumented. The fleet leader concept aims to use data obtained from the limited number of instrumented structures to assess the condition of other similar structures in the fleet. These techniques all assume an identical dynamic behavior, but for large structures there are differences (between sites and configuration or operational conditions), which imply that the dynamic behavior will differ between fleet members. As fatigue comes from an interaction of loads and the structural dynamics this implies that even under identical loads, different members of the fleet will fatigue at different rates. To reach an effective monitoring strategy the uncertainties associated with both virtual sensing and the fleet leader concept need to be well understood and assessed.
Contact: Wout Weijtjens (firstname.lastname@example.org) and Christof Devriendt (email@example.com)
Over 55% off all insurance claims to offshore wind farms are incurred by damaged cables and foundations. Moreover, the severity of these failures and associated repair costs result that these claims represent more than 90% of the total claims. Online monitoring is seen by the offshore wind industry as one of the main solutions to decrease the number of damages in cables and foundations, through their early detection and identification. Fiber optic technology is chosen as the preferred monitoring technique as it is very versatile (i.e. sensitive to temperature, strain, impact, partial discharge activity, …). The wide application of fibre optic sensing, however, lacks maturity as only temperature sensing on cables (DTS technology) and strain sensing on foundations (FBG technology or classical strain gauge technology) is common practice in offshore wind.
The BOPTIC project will address the need of offshore wind developers and operators to reduce the operational expenses of the Balance of Plant (BOP) by the development of innovative monitoring technologies for both the cables and foundations using emerging distributed optical sensing technologies. This research project wants to investigate in depth how to use distributed optical sensing techniques to:
- Monitor strain and bending in offshore power cables during installation and operation
- Monitor strain and bending moments of monopiles with high spatial resolution during their lifetime for hotspot location and seabed evaluation
- Detection and Monitoring of electrical faults of an offshore power cable during operation.
- Detection and Monitoring of cracks on welds of offshore structures during their lifetime
This project is initiated by 9 different stakeholders of the Blue Energy including 3 research institutions: Marlinks, 24SEA, ENGIE Laborelec, IMDC, Com&Sens, Parkwind, OCAS, VUB and UGent.
YOUR PHD :
The aim of the research is to investigate and develop the potential of using distributed optical sensing technologies for structural health monitoring and model updating in a digital twin context. You'll explore the state of the art in distributed optical sensing technology in the context of offshore wind.
Promoter: J. Meyers
Contact: Prof. J. Meyers, Department of Mechanical Engineering, Celestijnenlaan 300A, B3001 Leuven, Belgium. T: +32(0)16 322502.
Apply using the KU Leuven online application platform. (Applications by email are not considered!)
This PhD position is part of the FREEWIND project (Development of a Fast REsourcE planning and forecasting platform for the Belgian offshore WIND zones), financed by the Flemish Energy Transition Fund, which aims to encourage and support energy research and development supporting the transition to a carbon-neutral society. The project team consists of nine researchers and supporting staff. Three PhD students will be recruited at the start of the project and work full time for four years (the current position is one of them). A data scientist and ICT engineer, will work part time on the project. The project is closely aligned with another funded project on two-way meso–micro coupling for wind farm optimization and design, carried out by two PhD students at KU Leuven. The project is led by Prof. Johan Meyers (Turbulent Flow Simulation and Optimization (TFSO) research group; department of Mechanical Engineering) and Prof. Nicole van Lipzig (Regional Climate Studies (RCS) research group; department of Earth and Environmental Sciences). Within the TFSO and RCS group there is ample of expertise on the modelling tools needed for the FREEWIND project. The current PhD position will be supervised by Prof. J. Meyers and co-supervised by Prof. N. van Lipzig.
Offshore wind energy plays a central role in Europe’s transition to a carbon-free energy system. In Europe, numerous offshore wind zones surpass 1GW in capacity, several of which are under construction. At these sizes, wind farms interact with the atmospheric boundary layer and the local meso-scale weather system. Only very recently, the importance of these effects for wind-farm operation have been recognized. For instance for the combined Belgian–Dutch offshore cluster, the effect of wind-farm induced gravity-wave systems on the overall Annual Energy Production can be up to 6% (less production), and up to 30% on hourly production. Two-way interaction with other meso-scale systems, such as land–sea breeze or convection cells may also be important, but this has not yet been investigated to date. These effects are not included in current windfarm planning and forecasting tools. The FREEWIND project aims at developing a planning and forecasting platform that includes mesoscale feedback. A central case study will be centered around Belgian’s offshore wind zones. The platform is made available open-source through a dedicated web interface that allows for online scenario analysis.
PHD PROJECT DESCRIPTION
Research: To date, the main engineering paradigm with respect to the wind resource is a one-way approach, in which wind turbines are considered too small to affect the local wind climate. Current engineering tools for wind-farm planning are based on this approach. The development and open availability of fast models that include two-way coupling will be paramount for the efficient development and future exploitation of Europe’s large offshore wind farms. For this reason, KU Leuven developed an atmospheric perturbation model (Allaerts & Meyers, JFM 2019). The PhD will work on extending this model to take into account nonhomogeneous conditions, and baroclinic conditions. Moreover, a dynamical version of the model will be developed. The micro-scale model SP-Wind, a Large-Eddy Simulation code developed at KU Leuven, will be used to obtain highly detailed datasets for the development and validation of the atmospheric perturbation model. To this end, the current version of SP-Wind, will be slightly extended to include shallow boundary layers and effects of baroclinicity in the free atmosphere. The ultimate goal of this PhD is to develop and validate an engineering model for the planning (5 years to 20 years), forecasting (1 day to 7 days) and nowcasting (30 min to 1 day) ranges thereby including two-way coupling on all these timescales.
Timeline and remuneration: Ideal start time is March 1st 2020, but earlier and later starting dates can be negotiated. The PhD position lasts for the duration of four years, and is carried out at the University of Leuven. During this time, the candidate also takes up a limited amount (approx. 10% of the time) of teaching activities. The remuneration is generous and is in line with the standard KU Leuven rates. It consists of a net monthly salary of about 2000 Euro (in case of dependent children or spouse, the amount can be somewhat higher).
Candidates have a master degree in one of the following or related fields: fluid mechanics, aerospace or mathematical engineering, numerical mathematics, or computational physics. They should have a good background or interest in fluid mechanics, simulation, optimization, and programming (Fortran, C/C++, MATLAB, Python, …). Proficiency in English is a requirement. The position adheres to the European policy of balanced ethnicity, age and gender. Both men and women are encouraged to apply.
To apply, use the KU Leuven online application platform (applications by email are not considered) Please include:
a) an academic CV and a PDF of your diplomas and transcript of course work and grades
b) a statement of research interests and career goals, indicating why you are interested in this position
c) a sample of technical writing, e.g. a paper with you as main author, or your bachelor or master thesis
d) two recommendation letters
d) a list of possible additional references (different from recommendation letters): names, phone numbers, and email addresses
e) some proof of proficiency in English (e.g. language test results from TOEFL, IELTS, CAE, or CPE)
Please send your application as soon as possible and before May 31st, 2020 at the latest.
Decision: when a suitable candidate applies.
Starting date: candidates can start immediately. Start preferable Spring 2020.
Primary supervisor: Prof. Jan Helsen
The VUB Acoustics and Vibrations Research group and VUB AI-group work closely together in the field of machine monitoring. Novel signal processing and AI methods are developed specifically targeted at the prediction of failures and accurate assessment of their progression. In this context we work closely together with leading companies: Atlas Copco, BASF, DEME, …
The team has a core focus on wind energy in the context of OWI-lab. We have ongoing research projects with MHIVOW, ZF Wind Power, Parkwind, … Our multi-disciplinary approach allows us to bring methodological advancements all the way to application in industry.
Full Project Detail
The process of tracking the health of machinery is commonly known as condition monitoring. Typically, it involves recording data, analyzing this data, and then inspecting the resulting indicators for potential significant changes that could be symptomatic of a defect. Incorporating condition monitoring in the Operations and Maintenance of a company opens the door for predictive maintenance. At VUB we can offer help to companies in this condition monitoring process by performing specialized data analysis of their machines. This can be through the use of vibrations, rotation speed, acoustics, or other sources of measurable information. All these measurements typically produce a lot of complex data, therefore we investigate new ways how we effectively and efficiently analyze this data to provide an as accurate as possible health summary of the machine. Next to data analysis, there is thus also a strong focus on big data processing, automation of the result interpretation using machine learning, and keeping up with the Internet of Things trend of increased sensorization and data acquisition.
Postdoc job description
The research focuses on developing new data analysis tools for condition monitoring of wind turbines and rotating machinery in general. The work will include implementing existing concepts in code, but also developing novel ideas for signal processing. There is a strong emphasis on drivetrain monitoring (bearings, gears, generator,… through vibrations, currents, …) . In addition to the development of novel methodologies for signal analysis, we also strive to deliver actionable information, relevant to the industry. Thanks to our strong connections with several industrial partners, we have the opportunity to work on interesting issues, but this means we also need to disseminate our results. Therefore, your work will go beyond the development of new methods and will also include expanding our data analysis platform with your new tools and combining your new tools with state-of-the-art machine learning approaches. The latter is accomplished by our collaboration with the Artificial Intelligence group of VUB.
We offer the opportunity to work in a very inspired, motivated and enjoyable research group that is looking to expand. The focus is also not purely on academic aspects thanks to our industrial collaborations. On top of the meaningful academic and industrial experience , we encourage going abroad and presenting your work at international conference. Since this is a postdoc position, one of your responsibilities will also be to mentor and supervise PhD and master thesis students. You also need to be willing to help out in writing parts of or giving input for project proposals. It is also possible you will be given minor teaching tasks.
Applicants should preferably have:
- PhD degree in Mechanical, Electrical, Computer Science, or Mathematical engineering
- A relevant Master’s degree and/or experience in one or more of the following would also be an advantage: wind turbine dynamics, signal processing, machine learning techniques, Bayesian statistics, ...
- Background or interest in programming (Matlab, python, java, C/C++, …)
- Proficiency in English is a plus
Interested candidates are recommended to apply as soon as possible.
We offer an international open working environment stimulating personal development through international courses, many opportunities to attend and present at conferences abroad. Possibility to spend part of the research abroad. A generous competitive salary, public transport coverage and health insurance. The Postdoc position normally lasts at least 2 years, with potential extension.
Mail to firstname.lastname@example.org
How to apply
All applications should be made through e-mail (email@example.com)