JAXA Supercomputer System Annual Report (April 2018-March 2019)

CFD simulations of a wind turbine wake for rapid recovery of velocity deficits
Report Number: R18ETET09
Subject Category: Skills Acquisition System
URL: https://www.jss.jaxa.jp/en/ar/e2018/9167/
Responsible Representative
Yasutada Tanabe, Leader, High Speed Rotorcraft Research Section Aviation Systems Research Unit
Aeronautical Technology Directorate
Contact Information
The University of Tokyo, Keita Kimura (k.kimura@ilab.eco.rcast.u-tokyo.ac.jp)
Members
Keita Kimura, Yasutada Tanabe
Abstract
In a large wind farm (arrays of wind turbines), wind turbine wake can cause power deficits or/and additional
fatigue in turbine stands in wake region. In this study, several wake simulations were performed to obtain thecharacteristics of wind profiles in wind turbine wakes. These information can be used for design of wind farm.
In this year, we focused on the effect of rotational speeds and yaw angles on recovery processes of velocity
deficits. In addition, simulations of multiple rotor are performed to investigate wake interferences in actual windfarms.
Reasons for using JSS2
In wind turbine wake simulations, comtutational domain should cover about 10 rotor diameters downwind
because of its large impact range. On the other hand, to resolve the fluctuations from rotational blades, grid scales
need to be the order of chord length of blade tips. As a result, it is necessary to put large computational grid which
has high grid resolution. Thanks to JSS2, such simulations of high computational cost can be performed.
Achievements of the Year
Our numerical study focuses on the effects of tip speed ratio (TSR) and yaw angle on the recovery of velocity
deficits. All simulations are performed by using rFlow3D. Fig. 1 shows vorticity contours in the near-wake region.
When increasing TSR, distances between vortices becomes narrows and vortex pairing occurs more easily.
Consequently, it accelerates momentum exchanges between wake region and outer flow.
Fig. 2 depicts a wake strucutures in active yawed condition, in which yaw angles change periodically. Dynamic
meandering caused by an active yaw condition can be confirmed in the picture and it has strong mixing effects ofwake as shown in Fig. 3. Deficits of averaged wind speed in wake region quickly recover when the wake meanders
dynamically.

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Fig. 4 shows isosurfaces of vorticity in a case of multiple rotors assuming large wind power plants. A problemof much computational time was solved by applying MPI techniques to multiple rotors. This simulations canevaluate rotor torques and velocity fields in various conditions of wake interferences at the same time.

Fig. 1: contours of vorticity around the blade tip in the near-wake region

Fig. 2: iso-surfaces of vorticity in a case of active yawed condition

Fig. 3: Transition of rotor averaged wind speeds in wake region－292－This document is provided by JAXA.

JAXA Supercomputer System Annual Report (April 2018-March 2019)

Fig. 4: iso-surfaces of vorticity in a case of multiple rotors

Publications- Peer-reviewed papers
1) Keita Kimura, Yasutada Tanabe, Yuichi Matsuo, and Makoto Iida. “Forced wake meandering for rapid
recovery of velocity deficits in a wind turbine wake”, AIAA Scitech 2019 Forum, AIAA SciTech Forum, (AIAA2019-2083)- Oral Presentations
1) K. Kimura, Y. Tanabe, T. Aoyama, Y. Matsuo and M. Iida, “A NUMERICAL STUDY OF THE RELATIONBETWEEN WAKE STRUCTURE AND WIND FLUCTUATION,” GRE 2018, Yokohama, Japan, June, 2018.
2) K. Kimura, Y. Tanabe, T. Aoyama, Y. Matsuo and M. Iida, “The relationship between vortex pairings andvelocity deficit recovery in a wind turbine wake,” iTi conference 2018, Bertinoro, Italy, September, 2018.
3) K. Kimura, Y, Tanabe, Y, Matsuo and M. Iida. “A numerical study of the wake interference and wind
distributions for offshore WPP,” 40th Wind Energy Symposium, Tokyo, December, 2018.
4) K. Kimura, Y. Tanabe, Y. Matsuo and M. Iida, “Forced wake meandering for rapid recovery of velocity deficits
in a wind turbine wake,” 2019 Wind Energy Symposium, AIAA Scitech Forum, California, USA, January, 2019

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JAXA Special Publication JAXA-SP-19-003E

Usage of JSS2


Computational Information
Process Parallelization Methods

MPIThread Parallelization Methods

OpenMP

Number of Processes

2-8Elapsed Time per Case

300 Hour (s)Resources Used
Fraction of Usage in Total Resources*1 (%): 0.68
Details
Computational Resources
System Name

Amount of Core Time(core x hours)

Fraction of Usage*2 (%)

SORA-MA

9,046.58

0.00

SORA-PP

1,142,860.12

9.13

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage Assigned (GiB)

Fraction of Usage*2 (%)

/home

96.43

0.10/data

10,850.70

0.19/ltmp

2,170.14

0.19

Archiver Resources
Archiver Name

Storage Used (TiB)

J-SPACE

Fraction of Usage*2 (%)
0.00

0.00

*1: Fraction of Usage in Total Resources: Weighted average of three resource types (Computing, File System,and Archiver).
*2: Fraction of Usage: Percentage of usage relative to each resource used in one year.

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