DTU: PhD scholarship in Uncertainty Quantification of Wind Farm Flow ModelsDTU: PhD scholarship in Uncertainty Quantification of Wind Farm Flow Models

The Department of Wind Energy at the Technical University of Denmark, DTU Wind Energy, is seeking a PhD Student for the Aeroelastic Design Section (AED). Our competences are aero-servo-elasticity (coupling between structural dynamics, aerodynamics and control), aerodynamics, CFD and aero acoustics. We focus on the development of models for the design, analysis and improvement of wind farms and wind turbines and the validation of these models in full scale tests or wind tunnel experiments.

Responsibililties and tasks

Wind farm flow models are subjects to different uncertainty sources which for design of cost effective wind farms are important to model and quantify.

These uncertainties include both physical and epistemic (model, statistical and measurement) uncertainties.

The following strategies to verify and validate and quantify uncertainty of Annual Energy Production and Damage Equivalent Fatigue Loads of wind farms will be investigated during the PhD time span:

* Inputs uncertainty elicitation and propagation through the model chain
* Model parameters uncertainty quantification and propagation (e.g. for Annual Energy Production and Fatigue Loads Evaluation)
* Combined statistical uncertainties of long term fatigue evaluations and Annual Energy Production
* Model inadequacy estimation (Estimating the error of models with respects to the inputs)
* Models selection (which model according to the input region)
* Models averaging (How to combine results of different models in order to obtain a more robust estimate)
* Multi-fidelity wake modeling (When to switch to models of higher degrees of fidelity, Recalibrating and estimating the model inadequacy with respect to chain of model fidelity)

The work done during the PhD study will support different national and international research projects targeted at wind farm design such as IEA-WakeBench, EU-FP7 EERA-DTOC, DSF COMWIND, TOPFARM2 and future similar research projects.

Qualifications

Candidates should have:
* A master’s degree in mathematics, computer science, engineering or a similar degree with an academic level equivalent to the master’s degree in engineering
* A strong background in wind energy and/or in probabilistic methods
* A strong experience in programming language (e.g. Python, Matlab, Fortran, C/C++) and in linux environment
* Excellent verbal and written communication skills in English

Optional:
* Experience running models on large computer clusters
* Experience running wind farm flow/wake models
* Experience running Computational Fluid Dynamics (CFD) models
http://www.vindenergi.dtu.dk/english/About/Vacant-positions-at-DTU-Wind-Energy/7349e000-c487-4f31-bd53-bf9f2ac64048.aspx