Development of improved numerical tools for Certification by Analysis(CbA)

JAXA Supercomputer System Annual Report February 2022-January 2023

Report Number: R22EDA201G22

Subject Category: Aeronautical Technology

Responsible Representative: Kazuyuki Nakakita, Aviation Technology Directorate, Aircraft Lifecycle Innovation Hub
Contact Information: Andrea Sansica(sansica.andrea@jaxa.jp)
Members: Hirokazu Higashida, Manabu Hisida, Atsushi Hashimoto, Kenji Hayashi, Yuki Ide, Takashi Ishida, Masashi Kanamori, David Lusher, Tomoaki Matsuzaki, Yoimi Kojima, Hideji Saiki, Yosuke Sugioka, Andrea Sansica, Kosuke Uchida, Takahiro Yamamoto, Paul Zehner, Markus Zauner

Abstract

High-accuracy simulations for stall and buffet are needed during the aircraft design. However, since the simulation cost is very high, we want to develop computation methodologies that aim at high-accuracy results with low computational cost. Hybrid RANS/LES methods (Embedded LES, ELES) and Adaptive Mesh Refinement (AMR) techniques have been implemented in FaSTAR and validated for different flow configurations.

Reference URL

Please refer to https://www.aero.jaxa.jp/research/basic/numerical/ .

Reasons and benefits of using JAXA Supercomputer System

For stall and buffet analysis, it is necessary to perform calculations on 3D complex geometries. Achieving high accuracy requires a large amount of computing power, so it is necessary to use JAXA supercomputer.

Achievements of the Year

Adaptive Mesh Refinement (AMR) was impemented into FaSTAR and test cases were conducted on the ONERA-M6 wing. The same computational accuracy could be achieved with a 47% reduction in grid resolution (figure 1).

We also implemented Embedded-LES method to FaSTAR and attempted to simulate transonic buffet flow over an OAT15A airfoil (video 1). The simulation was conducted with about half number of grid points compared to previous research, and provided consistent results.

Fig.1: Current status and prospects of FaSTAR’s Adaptive Mesh Refinement (AMR) framework.

Fig.2(video1): ELES computation of the transonic buffet flow over the OAT15A airfoil

Publications

– Oral Presentations

1) Yoimi Kojima, Atsushi Hashimoto, “An Application of Embedded Large Eddy Simulation for Transonic Buffet Prediction,” AIAA SciTech Forum 2023.

Usage of JSS

Computational Information

Process Parallelization Methods: MPI
Thread Parallelization Methods: N/A
Number of Processes: 480 – 24576
Elapsed Time per Case: 240 Hour(s)

JSS3 Resources Used

Fraction of Usage in Total Resources*1(%): 3.42

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	90391507.59	3.94
TOKI-ST	545796.79	0.55
TOKI-GP	6388.03	0.27
TOKI-XM	1759.98	1.10
TOKI-LM	51573.62	3.46
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	1833.96	1.66
/data and /data2	202229.70	1.56
/ssd	37428.68	5.18

Archiver Resources
Archiver Name	Storage Used (TiB)	Fraction of Usage*2(%)
J-SPACE	38.71	0.17

*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)	2126.67	1.48

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