Technology demonstration of Airborne wind energy Kite system for Ordinary people

JAXA Supercomputer System Annual Report February 2025-January 2026

Report Number: R25EDA201E77

Subject Category: Aeronautical Technology

Responsible Representative: Naoki Matayoshi, Aeronautical Technology Directorate
Contact Information: Yoimi Kojima(kojima.yoimi@jaxa.jp)
Members: Yoimi Kojima, Sho Yokoyama

Abstract

To reproduce the behavior of an aerial wind turbine kite on a flight simulator, we are analyzing the aerodynamic coefficient of the kite and the surrounding flow field during flight using CFD. The kite's characteristic behavior is a figure-eight flight due to self-excited oscillation, and we aim to reproduce this behavior.

Reference URL

N/A

Reasons and benefits of using JAXA Supercomputer System

Unlike airplane wings, kites have complex shapes, and introducing aerodynamic coefficients into the simulator requires large-scale calculations with various values for the angle of attack and angle of sideslip. Therefore, large computational resources and advanced computers are essential, so we are using the JAXA supercomputer.

Achievements of the Year

Research on power generation using kites has primarily focused on pumping-cycle-based systems; however, JAXA is currently investigating a power generation method that utilizes figure-eight flight induced by self-excited oscillations. Power generation with kites requires control, and in order to verify such control systems at low cost and within a short time, highly accurate aerodynamic predictions of kite configurations are essential. In this fiscal year, we first validated the aerodynamic prediction accuracy of a computational fluid dynamics (CFD) solver by comparing numerical results with wind tunnel experiments, confirming that the flow field around the kite is well reproduced by the simulations. In addition, the aerodynamic characteristics of multiple kite configurations were examined. The study clarified the differences between kites that exhibit stable flight but are less effective at energy extraction and those that exhibit unstable flight but allow for greater energy extraction.

Fig.1: Comparison of surface pressure coefficient distributions at various angles of attack (experiment: smooth and rough surfaces, and CFD). The numerical results show good agreement with the experimental data over a wide range of angles of attack.

Fig.2: Visualization of the flow field (pressure distribution) around representative kite configurations: (a) Synapse 140 and (b) Plate_85mm. Differences in flow structures and pressure distributions are observed depending on the geometry.

Publications

N/A

Usage of JSS

Computational Information

Process Parallelization Methods: MPI
Thread Parallelization Methods: N/A
Number of Processes: 576
Elapsed Time per Case: 8 Hour(s)

JSS3 Resources Used

Fraction of Usage in Total Resources*1(%): 0.29

Details

Please refer to System Configuration of JSS3 for the system configuration and major specifications of JSS3.

Computational Resources
System Name	CPU Resources Used (Core x Hours)	Fraction of Usage*2(%)
TOKI-SORA	7548564.80	0.34
TOKI-ST	10560.39	0.01
TOKI-GP	0.00	0.00
TOKI-XM	0.00	0.00
TOKI-LM	0.00	0.00
TOKI-TST	0.00	0.00
TOKI-TGP	0.00	0.00
TOKI-TLM	0.00	0.00

File System Resources
File System Name	Storage Assigned (GiB)	Fraction of Usage*2(%)
/home	0.00	0.00
/data and /data2	0.00	0.00
/ssd	0.00	0.00

Archiver Resources
Archiver Name	Storage Used (TiB)	Fraction of Usage*2(%)
J-SPACE	16.79	0.05

*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.

ISV Software Licenses Used

ISV Software Licenses Resources
	ISV Software Licenses Used (Hours)	Fraction of Usage*2(%)
ISV Software Licenses (Total)	672.52	0.47

*2: Fraction of Usage：Percentage of usage relative to each resource used in one year.