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  3. Marie Skłodowska-Curie Early Stage Researcher PhD position: Reduced-order models and machine learning for FOWT analysis and design

Marie Skłodowska-Curie Early Stage Researcher PhD position: Reduced-order models and machine learning for FOWT analysis and design

STEP4WIND is a European Industrial Doctorate programme, granted under the H2020 Marie Skłodowska-Curie Actions Innovative Training Network initiative. The …

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Arbeidsvoorwaarden

Standplaats:
Kluyverweg, Delft, Zuid-Holland
Dienstverband:
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
38 - 38 uur
Salarisindicatie:
€ 2325 - € 2972 per maand
Opleidingsniveau:
WO

Functieomschrijving

STEP4WIND is a European Industrial Doctorate programme, granted under the H2020 Marie Skłodowska-Curie Actions Innovative Training Network initiative. The main objective is to address both technological and economical challenges related to the development of large floating offshore wind farms. Floating offshore wind turbines (FOWTs) could be a game changer to further decrease the cost of offshore wind energy and unlock new markets. Wind turbines placed on a floating support and moored to the seabed can harness energy in areas with much higher wind speeds, at a reduced installation cost. It also gives the opportunity to countries with deep water to enter the offshore wind industry. The project runs over 4 years and will deliver 10 PhD degrees, in joint supervision and training between the public and private sectors. The early-stage researchers (ESRs) will be part of a network supervised by 3 universities with a track-record in wind energy research and 5 companies leading the deployment of floating wind farms and heavily involved in policy-making bodies. A training programme and a mentoring scheme will equip the ESRs with key skills for their future career. Scientifically, STEP4WIND will develop floating-specific tools, methods and infrastructures to tackle the technological and economical challenges of FOWTs, from design to deployment, operation and scaling up. It will also deliver guidelines for large farm deployments, with a clear roadmap to commercialization.
Further information on the Innovation Training Network Marie Skłodowska-Curie Actions Novel design, production and operation approaches for floating wind farms (STEP4WIND) at www.step4wind.eu

Abstract of research project
Existing reduced-order modelling techniques rely on solving the same equations with reduced degrees of freedom or deriving models directly from data. This project will investigate a novel alternative: enhancing low-fidelity physical models with high-fidelity data, hence retaining physical correctness while matching reference data. The resulting models are expected to extrapolate better and require less training data. New reduced models will be derived for a wind turbine undergoing wave motions. As such, this PhD project will build a bridge between the high-fidelity studies and the design methods investigated in STEP4WIND. Preparatory to this work, high-fidelity (forced motion) simulations of turbines will be run to obtain training and validation data. Established model-reduction methods will be applied to this data-set to obtain a baseline for the expected performance of the new models. The development of new, data-driven, physics-constrained models will start from coarse-grid URANS or LES. By being based on PDE solvers, the derived models will respect physical conservation laws. Machine-learning will be used on the training-data to build improved closure models. In Year 1, an overview of the literature in this field will be made with specific emphasis on LES training data generation and system-identification ROMs. The most suitable low-fidelity models will also be assessed in view of enriching the ROM model. In Year 2, the machine-learning techniques will be developed for turbulence modelling based on inference by Bayesian inversion and non-linear eddy viscosity modelling. In Year 3, the results from Year 2 will be exploited to build practical and cheap reduced models. The latter will be applied to Siemens Gamesa’s internal design processes. In particular, the developed reduced-order model will improve the hydrodynamic loading computation coupled to Siemens Gamesa’s in-house aeroelastic code BONUS Horizontal Axis Wind Turbine. This will provide an enhanced design tool, including coupled hydrodynamics, aerodynamics and structural dynamics. Throughout the project, the newly-implemented algorithms will be validated against a measurement campaign from an existing turbine. Finally, Year 4 will be dedicated to finishing the PhD thesis.

Expected results
The principal outputs of this doctoral activity are described as follows:
(1) A LES database designed for training of ROMs, (2) a new capability for effective model reduction of LES, (3) a cheap, high-accuracy, pre-trained ROM for FOWT modelling.

Functie-eisen

Eligibility criteria
Candidates should have a MSc Degree (120 ECTS points) or a similar degree with an academic-level equivalent to a Master of Science (5 years minimum duration Bachelor + Master).

The successful candidate is required to have:
• Documented background in CFD modelling or reduced-order modelling
• Documented background in aerodynamics analysis, preferably for wind turbines
• Documented background in data analysis and programming (e.g. Matlab, Python, Fortran, C)
• Ability to work in a project team and take responsibility for own research goals
• Practical skills are strongly preferred
• Fluency in communicating and reporting in English

In addition, the successful candidate should satisfy at the time of the recruitment the following mandatory characteristics:
• having not more than 4 years of equivalent research experience (i.e. working as researcher after obtaining your master’s degree);
• having not been awarded a title of PhD;
• having not resided or carried out her/his main activity in the Netherlands for more than 12 months in the last 3 years.

The enrolment is subject to academic approval and fulfilment of the requirements for admission to a doctoral program at TU Delft. The present PhD project will take advantage of collaboration with researchers at TU Delft and Siemens Gamesa Renewable Energy.

Conditions

TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff, and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related, and research skills. Please visit www.tudelft.nl/phd for more information.

Salary and appointment terms
The selected candidate will be appointed on a temporary contract for 48 months, to be renewed annually. A contract of 30 months will be provided by TU Delft (The Netherlands) and a 18-month contract will be provided by Siemens Gamesa Renewable Energy (Denmark). The salary will be in line with the funding schemes of MSCA action, in accordance respectively with Danish and Dutch rules and regulations within this regard, and Country specific requirements, as stated in the Grant Agreement and Guide for Applicants.

Monthly salary in Denmark (living allowance + mobility allowance) will be of 4.414,50 € + 600 € (gross amount), allocated following Danish specific contract conditions for MSCA candidates.
The monthly salary in The Netherlands will be in line with the CLA of the Dutch Universities. Additional payments will be made in accordance with the specific contract conditions for MSCA candidates.
Family allowance accounts for 500 €/month (gross amount), regardless of the Country issuing the working contract, and will be granted only upon specific conditions.

Additional information

For more information about this position, please contact Dr. Axelle Viré, e-mail: A.C.Vire@tudelft.nl.

A Skype interview will be setup with the selected candidates to:
• Discuss about their CV, skills and experiences;
• Assess their communication skills in English and motivation;
• Discuss about the research/organizational topics.

Candidates may apply prior to obtaining their master's degree but cannot begin before having received it. Applications and enclosures received after the deadline will not be considered.
All interested candidates irrespective of age, gender, disability, religion or ethnic background are encouraged to apply.

Assessment
The assessment of the applicants will be made jointly by Axelle Viré, Assistant Professor in Wind Energy at TU Delft, Richard Dwight, Associate Professor at TU Delft, and Alex Loeven, Head of Rotor Performance – Offshore at Siemens Gamesa Renewable Energy. More people from both TU Delft and Siemens Gamesa might join the evaluation committee in due course.

During the 4 years of employment, the workplace will be TU Delft (The Netherlands) for an initial period of 18 months, then Siemens Gamesa Renewable Energy (Denmark) for a period of 18 months, and then the final period of 12 months again at TU Delft to finalise the PhD thesis.

Applications must be submitted via email no later than 31 May 2020, 23:59 (Dutch time) as one PDF file containing all materials to be given consideration. The file name of the PDF document will contain your first and last name.
To apply, send an e-mail to Ms. Sylvia Willems (S.M.Willems@tudelft.nl) and attach all your materials in English in one PDF file. When applying for this position, please refer to vacancy number LR20.20. The file must include:
• A letter motivating the application (cover letter);
• Curriculum vitae with publications (if any);
• Grade transcripts and BSc/MSc diploma;
• Research statement: through the research statement, the applicants have the chance to present a research problem that interests them and propose how it might be investigated. The research problem should be preferably related to the topic of the advertised PhD position.

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