Wind energy is becoming a main source of energy globally. The conversion of kinetic energy from incoming wind to electricity by wind turbines involves flows of different scales, from the boundary layer over blade and wind turbine wakes to the atmospheric boundary layer. Properly accounting for the effects of these flows is crucial for designing wind turbines and wind farms. However, the broad range of scales and complexity of these flows make it extremely difficult. Nowadays, more data are generated from computer simulations, laboratory and field measurements. Machine learning provides a new way to extract information from these data to learn models for inferring the flow state and predicting the performance of wind turbines and wind farms.
This special issue aims to provide a forum for communicating recent advances in applying machine learning methods in fluid mechanics research for wind energy. The topics of interest include, but are not limited to:

1. Application of machine learning techniques for wind resource assessments.
2. Machine learning-based wind farm layout optimizations.
3. Data-driven control of wind turbines and wind farms.
4. Data-driven models for power predictions of wind turbines and wind farms.
5. Data-driven wind turbine wake models.
6. Machine learning-based optimization of wind turbine designs.

We invite researchers in this field to submit their original research papers to this special issue. We look forward to receiving and sharing your innovative and exciting findings.

Guest editors:

Prof. Xiaolei Yang, PhD

Institute of Mechanics, Chinese Academy of Science, Beijing, China

(Turbulent flows, Wind energy, Computational fluid dynamics);

Prof. Mahdi Abkar, PhD

Aarhus University, Aarhus, Denmark

(Fluid Mechanics, Turbulence, Wind Energy, Data-driven Modeling, Machine Learning)

Manuscript submission information:

Authors are encouraged to submit manuscripts online to the journal Theoretical and Applied Mechanics Letters through the Editorial Manager System link: 

https://www.editorialmanager.com/taml/default2.aspx 

When submitting your manuscript please choose the special issue “VSI: ML in Wind Energy” from the choice of submission types. The manuscript will go through the regular peer review process before being accepted. 

The submission is open from now to 1-October-2023;
The deadline for acceptance is 31-December-2023.  

Please visit the journal website for additional notes for the authors: https://www.elsevier.com/journals/theoretical-and-applied-mechanics-letters/2095-0349/guide-for-authors 

Keywords:

(Wind energy) AND (Wind farm) AND (Power prediction), (Wake model) AND (Layout optimization) AND (Coordinated control)

Why publish in this Special Issue?

• Special Issue articles are published together on ScienceDirect, making it incredibly easy for other researchers to discover your work.

• Special content articles are downloaded on ScienceDirect twice as often within the first 24 months than articles published in regular issues.

• Special content articles attract 20% more citations in the first 24 months than articles published in regular issues.

• All articles in this special issue will be reviewed by no fewer than two independent experts to ensure the quality, originality and novelty of the work published.

Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues

Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors