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。

Application of distributed micro plasma actuators

JAXA Supercomputer System Annual Report February 2022-January 2023

Report Number: R22EACA47

Subject Category: JSS Inter-University Research

PDF available here

  • Responsible Representative: Takashi Matsuno, Associate Professor, Tottori University
  • Contact Information: Takashi Matsuno(matsuno@tottori-u.ac.jp)
  • Members: Takeo Hoshino, Rikiya Hata, Masahiro Kanazaki, Daisuke Kikuchi, Yuki Kishi, Seiichiro Morizawa, Takashi Matsuno, Takuya Nankaku, Taisei Nishimura

Abstract

Plasma actuators (PAs) are flow control devices that use discharged plasma to generate wall jets from smooth surfaces. In recent years, a method of generating a distribution of body force on a surface by using a large number of small-scale PAs has been proposed by the applicants. The objective of this study is to obtain knowledge about the application method of plasma actuator and its optimization method for arbitrary control of the velocity profile of a three-dimensional boundary layer, keeping in mind the boundary layer control of high-speed flows.

Reference URL

N/A

Reasons and benefits of using JAXA Supercomputer System

In order to perform large-scale 3D flow analysis and aerodynamic optimization with high computational costs, a supercomputer is required. The JAXA supercomputer, which provides an environment for the use of computational tools, is the most suitable environment for this study.

Achievements of the Year

The focus of this year’s research was on the application and validation of the optimal design method.

In the application of the optimal design method, numerical and aerodynamic analyses were performed by RANS for two-dimensional asymmetric arbitrary shapes, and multi-objective optimization problems were solved by using the optimal design tool. FaSTAR, developed by JAXA, was used for the numerical calculations. For grid generation, HexaGrid, an automatic grid generation software also developed by JAXA, was used. The computational grid is as small as 100,000 points. For the optimal design, a spline function was used to define the shape of the grid from a large number of design points, which was then changed in various ways to automatically generate the grid while analyzing several hundred samples to search for solutions.

Figure 2 presents the sample distribution of the design exploration and shows that most of the additional samples are clustered in the upper left optimal direction compared to the initial sample, indicating that the solution search is being performed appropriately. The design points were reduced using the variable reduction method developed in this study (shown in Fig. 1), and the optimal direction aimed to minimize Cd and maximize Cl. The samples are concentrated near the front edge of the Pareto surface, indicating that the search for a non-inferior solution is proceeding efficiently. The shape and Mach number distribution of the solution samples on the Pareto surface are shown in Figure 3, where Sample B has the largest L/D among the samples. It illustrates that a shape similar to a general cambered airfoil shape can be obtained with a limited number of sample points due to variable contraction and that the required specifications can be met.

Annual Reoprt Figures for 2022

Fig.1: Locations of the design variables selected by the current variable reduction method

 

Annual Reoprt Figures for 2022

Fig.2: Sample distribution of the design exploration

 

Annual Reoprt Figures for 2022

Fig.3: Shape and Mach number distribution of the solution samples on the Pareto surface

 

Publications

N/A

Usage of JSS

Computational Information

  • Process Parallelization Methods: MPI
  • Thread Parallelization Methods: N/A
  • Number of Processes: 12 – 1000
  • Elapsed Time per Case: 12 Hour(s)

JSS3 Resources Used

 

Fraction of Usage in Total Resources*1(%): 0.24

 

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 6148046.82 0.27
TOKI-ST 91629.18 0.09
TOKI-GP 0.00 0.00
TOKI-XM 0.00 0.00
TOKI-LM 4610.88 0.31
TOKI-TST 0.30 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 463.33 0.42
/data and /data2 17853.67 0.14
/ssd 386.67 0.05

 

Archiver Resources
Archiver Name Storage Used
(TiB)
Fraction of Usage*2(%)
J-SPACE 2.02 0.01

*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)
467.55 0.33

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

JAXA Supercomputer System Annual Report February 2022-January 2023