Internship | Physics-informed learning digital twin for offshore wind turbine virtual sensors
4 maanden geleden
What will be your role?
Introduction
To combat the imminent threats of climate change, it is imperative to swiftly transition away from traditional fossil fuels. Wind energy has emerged as a pivotal player in the move towards achieving a net-zero emission target. Offshore wind, in particular, has becomes a prominent and effective contributor to the renewable energy portfolio. Floating wind turbines are gaining traction as a key growth driver, harnessing deep-water wind resources and driving further momentum in the field of renewable energy. However, in the offshore environment wind turbine often suffers from reliability and safety issues due to the harsh condition. The Condition monitoring system (CMS) has been developed to monitor the wind turbine health condition and detect incipient faults or failure at an early stage.
Such a CMS often requires additional instrumentation to capture adequate quantities of interest, which may lead to increasingly high expense. Recent studies have shed light on application of digital twin techniques to offshore wind energy. The digital twin, utilizing a numerical model to replicate the real-world wind turbine dynamics and thus predict unmeasured key signals, seems to be an economically viable solution.
Although effective, digital twin techniques leveraging an internal model may not fully represent the real wind turbine dynamics due to the model mismatch, performance degradation and measurement noise. Thinking about the turbine performance, it may deviate from as-designed states over time due to design and manufacturing tolerance, maintenance activities, physical degradation including material aging, material wearing, blade erosion, or the accumulation of ice and dirt.
Without persistent updating of digital twin parameters, the model mismatch may cause sub-optimal digital twin prediction of the wind turbine signals. In this project, we are looking into the fundamentals of digital twin technique and wind turbine physics for visual sensor application. By leveraging artificial intelligence, data-driven or machine learning approaches, we aim at designing an effective self-calibrated learning digital twin for offshore wind turbines. Based on prior physical knowledge and measurement data available, it is desired to understand the offshore wind turbine aero-hydro dynamics and thus predict some unmeasured key signals.
At the Wind Energy (WE) expertise group of TNO we aim to make wind energy a reliable, integrated, and cost-effective source of renewable energy that is widely accepted by the public. Our team has years of experience in the modelling and optimization of offshore wind and hybrid power plant. This knowledge and experience drives the further development and extension of our cutting-edge modelling tools. We are looking for a master student to work on this topic, and provide a study on learning-based digital twin for offshore wind turbine visual sensors. Do you want to join us in building a more sustainable future?
What you will be doing
In this project, you will:
Ultimately, your work will facilitate the design and business case evaluation of digital twin application to offshore wind turbines.
Your work will be carried out in the Wind Energy group of TNO, in close cooperation with TU Delft Mechanical Engineering. It is required to have physical meetings at the Delft/Rijswijk/Den Haag office at least once every week. In other cases, you will be remotely co-supervised by TNO experts and an academic supervisor.
The Wind Energy group of TNO houses approximately 60 enthusiastic, academic professionals who have in-depth knowledge of wind energy technology. Your activities will be connected to a multi-partner project in which TNO is participating, and the results of this work will be a permanent addition to our simulation toolset. The expected time to complete the project is around 9 months.
What we expect from you
We are looking for students with a relevant STEM degree (e.g. mathematics/physics, mechanical/aerospace engineering, or sustainable energy technology), currently enrolled in an MSc program and with the following prerequisites:
Enthusiasm for research and technology development. Highly motivated to work within the renewable energy industry or academia. Interest in digital system design. Strong programming skills. Knowledge of Python, Matlab and/or Simulink is an advantage. Interest in renewable energy system and machine learning algorithm design. Independent and self-motivated working attitude. Excellent communication skills in English both verbally and in writing. Assertive students who are in good academic standing are encouraged to apply. The selection process will start immediately. The intended start date is November , with some flexibility to start later during the spring of . If you wish to apply and/or further discuss the project, please do not hesitate to contact us.
If you feel that you are the one we are looking for, please submit your CV and motivation letter in English.
For more information about this vacancy, please contact Dr. Yichao Liu,
What you'll get in return
You want an internship opportunity on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide:
A highly professional, innovative internship environment, within a team of top experts. A suitable internship allowance ( euro for wo-, hbo- and mbo-students, for a full-time internship). Possibility of eight hours of free leave per internship month (for a full-time internship). A free membership of , where you can meet other TNO professionals and join , such as sports activities, (work-related) courses or the yearly ski-trip. Use of a laptop. An allowance for travel expenses in case you don’t receive an OV-card.-
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