Wind Farms and Atmospheric Physics

Optional (mandatory 1st or 3rd semester) courses:

* Turbulent Flows (5 ECTS) - Both AP and WF
* Introduction to Dynamical Systems (5 ECTS) - Only WF
* Diffusions and Stochastic Differential Equations (5 ECTS) - Only AP

### Semester 2 - University of Oldenburg

Note: the teaching period is from April to July. But the Research Project can start already in February.

#### Mandatory Courses

* phy692: Research Project EWEM (9 ECTS)
* phy670: Fluid Dynamics II / Advanced Wind Energy Meteorology (6 ECTS)
* phy616: Computational Fluid Dynamics I+II (6 ECTS)
* phy987: Control of Wind Turbines and Wind Farms (6 ECTS)
* phy645: Wind Physics Measurements Project (3 ECTS)

### Semester 3 - DTU

#### Mandatory Courses

* Wind Turbine Measurement Technique (10 ECTS)

Optional (mandatory 1st or 3rd semester) courses:

* Turbulent Flows (5 ECTS) - Both AP and WF
* Introduction to Dynamical Systems (5 ECTS) - Only WF
* Diffusions and Stochastic Differential Equations (5 ECTS) - Only AP

#### Approved Elective Courses

* WF: Deep learning, Intelligent Systems, Optimization in Modern Power Systems, Advanced CFD, Life Cycle Assessment of Products and Systems, HardTech Entrepreneurship, Energy Economics, Integration of Wind Power in the Power System, ...
* AP: Time series Analysis, Advanced Time Series Analysis, Remote Sensing, Introduction to Digital Mapping and GIS, Computational tools for Data Science, Physics of Sustainable Energy, ...
* Both: Introduction to Machine Learning and Data Mining, Optimizations and Data Fitting, Offshore Wind Energy, Probabilistic Methods in Wind Energy

* Contact study coordinators for more information

### Course Reviews

#### Wind Turbine Technology and Aerodynamics

Nice introduction to wind energy and the industry. One previous student described it as a crash-course to MATLAB, as there are a lot of programming exercises, and a functional BEM code is a must for the final exam. Level of effort: Medium (High if not familiar to basic programming with MATLAB or python).

#### Hydrodynamics

Nice review on linear theory of waves, Navier-Stokes and approximations (e.g. shallow water) and integrated momentum equations for boundary layers in laminar and turbulent flows. The course material is very well organized and the teacher (David) is very good. Lecture slides are loaded with fairly heavy (interesting) math and calculus. Programming exercises using Mathematica. Evaluation based on a final exam in Mathematica or MATLAB and a report (in my case on storm surges which was very cool). Level of effort: Easy to Medium

#### Micrometeorology

A thorough introduction to the field of micrometeorology in the context of Wind Energy. Wind profiles, turbulence, spectral analysis, atmospheric boundary layer, wind resource assessment, extreme statistics, bulk statistics and probability distributions. Interactive and demanding classes. Make sure to read the text book before classes. Four (programming) assignments focusing on the fundamental scopes of the course material. Chilled oral exam at the end, discussing the assignment feedback and major concepts. Grade of assignments weighs heavily on the final grade, the oral exam is more to bump the grade up or down. Level of effort: Medium

#### Planning and Design of Wind Farms

Well organized and compact course (3 weeks in January). Group work on a case study of a wind farm project involving: Wind recourse assessment (using the famous DTU WAsP model), wind farm layout and energy yield, project economics, environmental impact assessment, social context and grid connection. Field trip to a wind farm in non-covid years. Grade based on the case study report, a presentation and a short multiple choice exam. Level of effort: Easy to Medium

#### Introduction to Machine Learning and Data Mining

A good introduction course (on a Bachelor level) to machine learning. Two reports with focus on visualizations and supervised learning (regression and classification) on large data sets. Unsupervised learning also included in the course material. A large course, with more than 600 students. The course material is quite extensive. You will gain hands on experience with ML code by running pre-made python/R/MATLAB scripts, which can be used in the assignments. Multiple choice final exam, with all aids except internet. Make sure to solve previous exams and the problems in the book, prior to the exam. Level of effort: Medium

#### Time Series Analysis

Introduction to time series analysis with a strong emphasis on applications relevant to engineering science and for modelling of physical systems. Not heavy theory. Linear stochastic processes, correlation functions, predictions in time series, state space models, Kalman filter. Four assignments that address key concepts of the course, e.g. linear trend models, estimation of ARMA models, ARIMA model identification and Kalman filter. Working with and predicting time series data, e.g. CO2 concentration, covid19 cases and electricity usage. Recommend using R, as the teacher uses that. Also possible to use python or MATLAB. Peergrading format, final grade based on assignment and feedback scores. NO EXAM. Short podcast videos cover the material, ending with a short quiz to emphasize on the important stuff. Lectures used for discussion on the more difficult material and solving textbook exercises. Highly recommended elective on the first semester for AP students, if not familiar to stochastics and Markov processes, to prepare for the stochastic differential equations course (which is allegedly much harder). Level of effort: Medium

#### Research project

Research project is conducted in the ForWind research center in Oldenburg university. The student would be given the possibility to join one of the availeable ongoin projects in this center. A tutor would be assigned to the student and basically this one, will provide help as an assistant, conducting relevant work. Ideally the project could be started in Febraury/March (in between the academic semesters = examp period), to start releasing work and then ease the work load at the final months of your stay in oldenburg.

ForWind research center: https://www.forwind.de/en/

#### Fluid Dynamics II / Advanced Wind Energy Meteorology

These two courses are combined to one module i.e. one exam and grade. But with respect to the content or the structure they are not connected. Each student can choose in which course the exam should be held.

Fluid Dynamics II is all about turbulence, phenomenological and statistical description and derivations from Navier-Stokes. In the corresponding exercise you either derive advanced relations or program what you have learnded in the lecture. In the oral exam you talk about one of the programming exercises. Level of effort: medium to high

The first lectures of Advanced Wind Energy Meteorology should be familiar from Micrometeorology. After that numerical atmospheric flow modelling, applications, wind power forecasting and wind resource assessment are taught. The exam is a 15 page assignment about a follow up topic you can choose from a list. Level of effort: low to medium.

## Track Coordinator (Faculty)

UOL: Andreas H. Schmidt (andreas.hermann.schmidt@uni-oldenburg.de)

DTU: Taeseong Kim (tkim@dtu.dk)

## Track Representative (Student)

Enrique Brizuela enriquebrizuelaao@gmail.com