Innovative Green Aircraft Technology (iGreen) : Riblet coating technology

JAXA Supercomputer System Annual Report February 2023-January 2024

Report Number: R23EDA101R01

Subject Category: Aeronautical Technology

Responsible Representative: Mitsuru Kurita, Aeronautical Technology Directorate, Aviation Environmental Sustainability Innovation Hub
Contact Information: Mitsuru Kurita, Aeronautical Technology Directorate, Aviation Environmental Sustainability Innovation Hub(kurita.mitsuru@jaxa.jp)
Members: Fumitake Kuroda, Mitsuru Kurita, Monami Sasamori

Abstract

By developing a particular riblet pattern that is effective at reducing the turbulence frictional resistance, and by producing and applying an easy-to-coat method that can create an optimum riblet surface on the airframe, reduce friction drag in the turbulence boundary layer.

Reference URL

Please refer to https://www.aero.jaxa.jp/eng/research/ecat/igreen/ .

Reasons and benefits of using JAXA Supercomputer System

CFD analysis is used for developing a particular riblet pattern that is effective at reducing the turbulence frictional resistance. Huge calculation resources and costs are required for the high fidelity and quick response CFD analysis for obtaining the optimum riblet pattern. Use of JSS is indispensable for these requirements; the cost and time on the CFD analysis are drastically saved .

Achievements of the Year

We have performed a series of direct numerical simulations (DNS) of a turbulent channel flow over a blade-type riblet and analyzed the effects of deterioration in a riblet sheet and those of a gap between the riblet sheets.

Fig.1: Contours of the streamwise velocity fluctuation together with the velocity vectors in the blade-type riblet DNS with a flattened tip. The red and blue contours denote the positive and negative values of the streamwise velocity fluctuation, respectively.

Publications

– Poster Presentations

Monami Sasamori, Hiroyuki Abe, Mitsuru Kurita, Fumitake Kuroda, “Single Bevel Straight Riblet for Turbulent Skin-Friction Drag Reduction,” Proceedings of the 61st Aircraft Symposium (Kitakyushu International Conference Center, 15-17 November 2023), 2A08.

Usage of JSS

Computational Information

Process Parallelization Methods: MPI
Thread Parallelization Methods: Automatic Parallelization
Number of Processes: 64 – 256
Elapsed Time per Case: 300 Hour(s)

JSS3 Resources Used

Fraction of Usage in Total Resources*1(%): 3.72

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	100464133.99	4.54
TOKI-ST	18751.86	0.02
TOKI-GP	144.64	0.00
TOKI-XM	0.00	0.00
TOKI-LM	42460.80	3.23
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	1.19	0.00
/data and /data2	64849.37	0.40
/ssd	0.00	0.00

Archiver Resources
Archiver Name	Storage Used (TiB)	Fraction of Usage*2(%)
J-SPACE	6.45	0.02

*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)	2692.62	1.22

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