本文へ移動

サイトナビゲーションへ移動

検索ボックスへ移動

サイドバーへ移動

ここは、本文エリアの先頭です。

Innovative Green Aircraft Technology (iGreen) : Riblet coating technology

JAXA Supercomputer System Annual Report February 2022-January 2023

Report Number: R22EDA101R01

Subject Category: Aeronautical Technology

PDF available here

  • 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 determined an optimal shape of the riblet. In addition, we have analyzed the effects of deterioration in a riblet sheet and those of a gap between the riblet sheets with the use of DNS.

Annual Reoprt Figures for 2022

Fig.1: Near-wall streaks and vortical structures in the blade-type riblet DNS. The white isosurfaces refer to positive values of the second invariant of the fluctuating velocity tensor, whereas the red and blue contours denote the positve and negative values of the streamwise velocity fluctuation.

 

Annual Reoprt Figures for 2022

Fig.2: Contours of the second invariant of the fluctuating velocity tensor in the blade-type riblet DNS. The red and blue contours denote the positve and negative values of the second invariant of the fluctuating velocity tensor.

 

Publications

N/A

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(%): 3.24

 

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 88216227.95 3.85
TOKI-ST 208.91 0.00
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 8.43 0.01
/data and /data2 34203.87 0.26
/ssd 70.83 0.01

 

Archiver Resources
Archiver Name Storage Used
(TiB)
Fraction of Usage*2(%)
J-SPACE 6.45 0.03

*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)
1173.38 0.82

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

JAXA Supercomputer System Annual Report February 2022-January 2023