To support the development of electricity production from wind power, IFP Energies nouvelles is involved in the energy transition as a research and training player, especially in emerging technologies such as floating offshore wind turbines (FOWT).
The fatigue design of an offshore wind turbine is an expensive task because it requires a large volume of multi-physics simulations in order to cover a large number of environmental conditions (wind and sea), especially if the wind turbine is placed within a farm (wind wake effect). In practice during an industrial study, the limitations due to the computation time force to limit the input parameters to a few scalars. This reduces the robustness of the final design and increases its uncertainty.
The need for an accurate non-intrusive surrogate model to accelerate computations is becoming apparent. A surrogate model designates a function built as an approximation of a numerical simulator. This approximation is constructed from the simulator output values at different points in the input space. A classical technique (in the field of uncertainty quantification and in machine learning) for building a metamodel consists in modeling a simulator using a Gaussian process and obtaining an approximation by calculating the a posteriori mean of the process.
An efficient surrogate model could especially open the gate to more comprehensive uncertainty quantification (UQ) studies and design optimization. This will also allow real-time control, live tracking of wind turbines fatigue, and thus a more efficient maintenance planning.
The state of the art regarding surrogate modelling for WT computations mainly address low dimensional input spaces (e.g., wind or wave statistical parameters) and low dimensional output spaces (e.g., DEL at a particular point of the structure).
The objective of this thesis is to develop strategies for building Gaussian process metamodels in large dimensions (typically 104-106) for inputs and outputs, whose prediction error can be quantified. The usual techniques to approach this type of problem consist in carrying out projections on reduced dimension spaces. In this thesis work, the attention will mainly focus on the problem of the selection of the hyper-parameters of Gaussian processes for high-dimensional problems, by focusing on the statistical properties of the estimators and on the techniques of sequential design of experiments allowing to reduce the uncertainty on the hyper-parameters.
These theoretical developments will be evaluated on the problem of numerical simulation of offshore wind turbines with wind and wave transient type inputs and load field or spatially variable kinematic fields outputs.
Keywords: Data science, offshore wind energy, gaussian processes, mechanical design, machine learning
- Academic supervisor Emmanuel Vazquez, professeur, The Laboratory of Signals and System (L2S), CentraleSupélec, Paris-Saclay Univ.
- Doctoral School Doctoral school 422 STITS, http://ed-stits.fr/fr/
- IFPEN supervisor Nicolas Bonfils, research engineer, IFP Énergies Nouvelles (IFPEN)
- PhD location IFPEN, Rueil-Malmaison, France
- Duration and start date 3 years, beginning of the fourth trimester
- Employer IFPEN, Rueil-Malmaison, France
- Academic requirements Engineering degree with a specialization in data science or statistics
- Language requirements Very good knowledge of French is required, knowledge of English is desirable
- Other requirements Knowledge of python, R and main machine learning libraries is required. Knowledge of GitHub repository service is desirable
To apply, please send your cover letter and CV to the IFPEN supervisor indicated here above.
IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site.
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions. For more information, please see our dedicated WEB pages.