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。

Physics understanding and modeling based on high-fidelity numerical analysis

JAXA Supercomputer System Annual Report February 2022-January 2023

Report Number: R22EDA201J01

Subject Category: Aeronautical Technology

PDF available here

  • Responsible Representative: FUJII Kenji, Director, Aviation Technology Directrate, Fundamental Aeronautics Research Unit
  • Contact Information: Abe Hiroyuki(abe.hiroyuki@jaxa.jp)
  • Members: Hiroyuki Abe, Manabu Hisida, Ryohei Kirihara, Takuhito Kuwabara, Yuichi Matsuo, Shingo Matsuyama, Yasuhiro Mizobuchi, Taisuke Nambu, Takeshi Okabe, Kei Shimura, Yukari Sakano, Hiroki Yao

Abstract

The aim is to model key phenomena of turbulence, fuel atomization, and combustion based on physics understanding by detailed and high-fidelity simulations and to lead the research.

Reference URL

N/A

Reasons and benefits of using JAXA Supercomputer System

World-level research in this field requires massively parallel huge computational resource and only so-called supercomputer system can provide it.

Achievements of the Year

・Detailed analyses of aeroengine fuel atomization were performed. The gas-liquid interface analysis method was modified to enable stable analysis under atmospheric pressure conditions (high gas-liquid density ratio condition). The analysis succeeded in reproducing the qualitative trend of the primary atomization experiment under atmospheric pressure conditions (see Fig. 1).

・A skewed turbulent boundary layer is one of the key phenomena in aeronautical applications such as combustors and airfoils. In the present study, we have performed a series of direct numerical simulations (DNSs) of a shear-driven three-dimensional turbulent boundary layer up to the momentum thickness Reynolds number Reθ=1500. The latter Reθ is the largest Reynolds number ever performed in this configuration. Number of grid points used for Reθ=1500 are 6.4 billion to resolve the essential motions. Figure 2 shows visualization of tubulence structures for Reθ = 1500, which highlights the presence of hierarchical turbulence in the region where the cross flow is involved.

Annual Reoprt Figures for 2022

Fig.1: Detailed analyses of aeroengine fuel atomization under an atmospheric pressure condition.

 

Annual Reoprt Figures for 2022

Fig.2: Turbulence structures observed in the DNS for Reθ=1500 (blue: negative streamwise velocity fluctuation; red: positive streamwise velocity fluctuation).

 

Publications

– Peer-reviewed papers

1) T.Nambu, Y. Mizobuchi “A study on droplet group evaporation modeling based on interface resolved numerical simulations of two-phase flow” Combustion and Flame

2) S. Matsuyama, “Implicit Large-Eddy Simulation of Turbulent Plane Jet at Re = 10^4,” Computers & Fluids.

3) K. Kawano, H. Gotoda, Y. Ohmichi, and S. Matsuyama, “Complex-network analysis of high-frequency combustion oscillations in a model single-element rocket engine combustor,” Journal of Fluid Mechanics.

– Invited Presentations

H. Abe, T. Nambu and Y. Mizobuchi,DNS and modeling of Re effects in wall-bounded turbulent flows, Research meeting in Research Institute for Mathematical Sciences, Kyoto University “Predictability and controllability of turbulence” (July 20-22, 2022, Hybrid meeting).

– Oral Presentations

1) H. Abe, T. Nambu, Y. Mizobuchi, Prediction of corner separation in NASA Juncture Flow using the AMM-QCRcorner model, 54th FDC/40th ANSS, June, 2022.

2) H. Abe, T. Nambu and Y. Mizobuchi, ” Quadratic constitutive relation for a corner flow and its application to a wing-body juncture flow,” Twelfth International Symposium on Turbulence and Shear Flow Phenomena (July 19-22, 2022, Online).

3) H. Abe, T. Nambu, Y. Mizobuchi and P. R. Spalart, ” Improvement on the AMM model for predicting wing-body juncture flows,” 2022 Symposium on Turbulence Modeling: Roadblocks, and the Potential for Machine Learning ( July 27-29, 2022, Lockheed Martin Center of Innovation).

4) S. Matsuyama, Let’s see what the SGS Model can do, 54th FDC/40th ANSS, June, 2022.

5) S. Matsuyama, 3rd Workshop on Cartesian Grid-based CFD, 54th FDC/40th ANSS, June, 2022.

6) S. Matsuyama, Consideration on cell Reynolds number and wall heat flux, 54th FDC/40th ANSS, June, 2022.

7) S. Matsuyama, LES of a Turbulent Plane Jet by an anisotropic SGS Stress model, Annual Meeting of JSFM 2022, September, 2022.

Usage of JSS

Computational Information

  • Process Parallelization Methods: MPI
  • Thread Parallelization Methods: OpenMP
  • Number of Processes: 120 – 13422
  • Elapsed Time per Case: 1000 Hour(s)

JSS3 Resources Used

 

Fraction of Usage in Total Resources*1(%): 1.34

 

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 36369544.65 1.59
TOKI-ST 31184.64 0.03
TOKI-GP 0.00 0.00
TOKI-XM 0.00 0.00
TOKI-LM 3568.19 0.24
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 344.48 0.31
/data and /data2 30215.99 0.23
/ssd 2879.60 0.40

 

Archiver Resources
Archiver Name Storage Used
(TiB)
Fraction of Usage*2(%)
J-SPACE 35.09 0.16

*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)
363.28 0.25

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

JAXA Supercomputer System Annual Report February 2022-January 2023