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职位信息
PhD Position Predictive Prognostics and Adaptive Load Control: An Integrated Approach to Wind Turbine Blade Longevity and Efficiency Optimisation.
10月31日截止
IntelliWind is a Marie Sklodowska-Curie Doctoral Network funded by the Horizon Europe program. The project aims to train 16 highly motivated and exceptional PhD candidates. Its primary research objective is to reduce human involvement in decision-making and minimize the need for direct human interventions in operations and maintenance processes.
This approach allows human resources to focus on more complex, better-planned, and efficient operations, leading to significant improvements in cost efficiency and reduced labor intensity in wind farm operations. The project will catalyze a shift in the skills and tasks required for wind power plant operations, moving from traditional engineering roles to the design, analysis, and interaction with automated machine algorithms.
Doctoral candidate position DC6 will be undertaken within the Intelligent Sustainable Prognostics (iSP) Group at the Faculty of Aerospace Engineering, Delft University of Technology, with collaborative placements at the Fraunhofer Institute for Wind Energy Systems IWES and the Technical University of Denmark.
The candidate's research will focus on developing a self-learning, prognostics-based blade load control methodology. This methodology will adapt blade loads to maintain blade lifetime consumption at a target level, given a predefined reliability threshold. The objectives of this thesis include:
Developing an interpretable prognostic model along with an uncertainty management strategy to estimate the conditional reliability and Remaining Useful Life (RUL) of wind turbine blades, considering historical data, expert knowledge, environmental conditions, and operational stress.
Designing a self-learning control algorithm that adjusts blade loading in real-time based on the predicted RUL, conditional reliability, and current health state to optimize energy output and reduce maintenance requirements.
Experimentally validating the developed control algorithm, measuring real-time energy output adaptations to optimize cost functions.
Applicant Requirements:
Educational Background: An MSc degree (or equivalent) in engineering, mathematics, or a related field, with a strong background in machine learning, stochastic modeling, and Bayesian statistics. (Note: You may apply before obtaining your master’s degree, but you must have it before starting the position.)
Programming Skills: Proficiency in programming languages such as Python, C, or R.
Teamwork and Responsibility: Ability to work effectively within a project team and take responsibility for your own research objectives.
Communication Skills: Excellent communication skills in English, both written and oral.
Eligibility Criteria:
You must not have been awarded a PhD. Applicants who have successfully defended their doctoral thesis but have not yet formally received the doctoral degree are not eligible.
You must not have resided or carried out your main activity in the Netherlands for more than 12 months within the last 3 years.
https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?jobId=18696
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@留德华叫兽：系美国Clemson大学数学硕士(运筹学方向)、Ph.D. candidate，欧盟玛丽居里学者，德国海德堡大学数学博士(离散优化、图像处理)，读博期间前往意大利博洛尼亚大学、IBM实习半年，巴黎综合理工访问一季。现任德国无人驾驶资深研发工程师。
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