PhD scholarship on Fast and accurate machine-learning surrogates of atmospheric flow dynamics w
3 days ago
This PhD project will explore advanced data-driven methods to accelerate the green transition by providing fast and accurate modeling of wind farm wake aerodynamics, which occurs as large wind turbines interact with the complex atmosphere.
Are you eager to explore advanced machine-learning techniques and enthusiastic about accelerating the green energy transition by improving modeling of wind farm wake aerodynamics? If so, this PhD scholarship is for you.
Individual wind turbines as well as wind farm clusters continue to increase in size, which increases the complexity of the turbulent inflow in which they operate. The added complexity can be captured by high-fidelity numerical tools, but the computational costs are too high to utilize for turbine design and operational improvements. However, machine-learning can be utilized to build fast and accurate models based on high-fidelity data-sets.
The aim of this PhD project is to expand an existing framework for fast and accurate modeling of wind farm wake aerodynamics as presented by Andersen and Murcia Leon, 2022. The existing framework consists of dimensional reduction combined with a stochastic engine and a surrogate to predict unseen cases, not included in the training data-set, similar to Solera-Rico et al., 2024. The next step is to investigate modern machine-learning methods for dimension reduction, synthetic turbulence generation and regression across an expanded parameter range of application. A significant learning objective is to understand the trade-offs between model accuracy and computational costs associated with training.
This PhD is part of a strategic research collaboration between DTU and Royal Institute of Technology (KTH) in Stockholm, Sweden, so the project will be co-supervised by Associate Professor Ricardo Vinuesa from KTH and include an external stay at KTH.
Responsibilities and qualifications
Your overall responsibilities will be to:
- Develop efficient dimension reduction methods to approximate wind farm flows across various operational and atmospheric conditions.
- Generate synthetic turbulence based on stochastic model or deep-learning techniques.
- Construct regression models capturing changes in turbulent structures for the various conditions.
- Compare different machine-learning techniques in terms of accuracy and computational efficiency, e.g. linear and non-linear methods.
- Perform detailed validation and error estimation of the models.
- Provide physical interpretation of the constructed models.
- Participate in scientific conferences and publish results in scientific journals.
We expect that you have:
- A background in data science, physics, engineering, or similar.
- Experience developing and using machine-learning techniques, e.g. neural networks.
- Ability to work with large data sets.
- Scientific programming experience, e.g. Python.
- Understanding of fluid mechanics, turbulence, boundary-layer flows and/or time series analysis is beneficial.
- Clear and concise communication skills in English.
- Positive attitude, a strong drive, critical thinking, and an eagerness to learn.
You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.
Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education.
Assessment
The assessment of the applicants will be made by Associate Professor Søren Juhl Andersen, Research Juan Pablo Murcia Leon, Professor Jens Nørkær Søresen from DTU as well as Associate Professor Ricardo Vinuesa from KTH.
We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.
Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.
Starting date will be coordinated in mutual agreement, but preferably 15th January 2024. The position is full-time. The start date will also depend on the enrollment as a PhD student.
Application procedure
Your complete online application must be submitted no later than 30 September 2024 (23:59 Danish time) .
Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file . The file must include:
- A letter motivating the application (cover letter)
- Curriculum vitae
- Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale
You may apply prior to obtaining your master's degree but cannot begin before having received it.
Applications received after the deadline will not be considered.
All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.
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