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.

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.

Fixed-term contract: 4 years.

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.

Technische Universiteit Delft

Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.

Faculty Aerospace Engineering

The faculty of Aerospace Engineering at Delft University of Technology is one of the world's largest faculties devoted entirely to aerospace engineering. In the Netherlands it is the only research and education institute directly related to the aerospace engineering sector. It covers the whole spectrum of aerospace engineering subjects. In aeronautics, the faculty covers subjects ranging from aerodynamics and flight propulsion to structures and materials and from control and simulation to air transport and operations. In astronautics, topics include astrodynamics, space missions and space systems engineering. The faculty has around 2,500 BSc and MSc students, 214 PhD candidates and 27 professors supported by scientific staff.
The faculty's mission is to be the best Aerospace Engineering faculty in the world, inspiring and educating students through modern education techniques and enabling staff to perform ambitious research of the highest quality for the future of aerospace. The working atmosphere at the faculty is friendly, open-minded and dedicated.

The Department of Aerodynamics, Wind Energy and Flight Performance and Propulsion (AWEP) is one of four departments composing Aerospace Engineering. Fundamental research is performed in the Aerodynamics section. Aircraft design, propulsion systems and their integration are the main topics in the FPP section. Wind energy systems, from small wind turbines to large offshore farms, are the objective of the research of the Wind Energy Section. The department operates comprehensive laboratories, equipped with modern wind tunnels and state-of-the-art measurement systems.

The Wind Energy Section facilitates the development of wind energy technology and the expansion of the use of wind power through research and education.
In its research activities there is a focus on large multi megawatt offshore wind turbines and offshore wind farms, though urban and airborne wind power is also addressed. Both technology development aspects as well as fundamental aspects are present in the research program. With respect to educational courses, the BSc and MSc level are offered for Aerospace students, SET students and for the European Wind Energy Master (EWEM) students.

SGRE - Siemens Gamesa Renewable Energy was created in April, 2017, with the merger of Gamesa Corporación Tecnológica and Siemens Wind Power.
Together, Siemens Wind Power and Gamesa are a leader in the renewable energy industry. SGRE wind power products and technologies are being used in more than 90 countries, across five continents, onshore and offshore.
For more than 25 years, SGRE engineers and technicians pioneered the offshore sector back in 1991, with the world’s first wind power plant in Denmark. Since then, Siemens Gamesa has grown to become the global leader in offshore power generation offering full-scope solutions from R&D, design, prototyping  to industrial manufacturing, project management and services.  The company is notable for its SG 11.0-200 DD offshore wind turbine, the largest variant based on the Siemens DD Platform, as well as being one of the largest wind turbines in the world.
On the fleet and database side, SGRE OF has access to a large offshore wind farm database containing the operational data of >1500 offshore wind turbines, access to dedicated measurement turbines, data on load calculations and support structure designs of >60 offshore wind farms.
Siemens Gamesa Renewable Energy is having a leading role in the commercial development of the most advanced FOWT technologies and two installed pilot parks in European waters. Therefore, SGRE bring valuable experience about all phases of development of full-scale FOWTs and parks. SGRE experience covers several full-scale floating wind turbines, incl. five modern 6MW FOWTs and planning installation of 11 of the SG 8.0-167 DD turbines for the world’s largest floating wind farm.