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Innovation for Design, Data-acquisition, Trouble-shoot and Certification in Aircraft Development: Aerodynamic Optimization

JAXA Supercomputer System Annual Report April 2016-March 2017

Report Number: R16E0033

  • Responsible Representative: Takeshi Ito(Aeronautical Technology Directorate, Next Generation Aeronautical Innovation Hub Center)
  • Contact Information: Shigeru Kuchiishi(kuchi-ishi.shigeru@jaxa.jp)
  • Members: Shigeru Kuchiishi, Takashi Ishida, Atsushi Hashimoto, Masahiro Kanazaki, Kohji Suzuki, Takatoshi Nakayama, Minoru Yoshimoto, Shinsuke Nishimura, Kei Nakanishi, Yukinori Morita, Takuya Ogura, Kyohei Sawada
  • Subject Category: Aviation(Aircraft)

Abstract

A Multi-Objective Evolutionary Algorithm (MOEA) is employed as an aerodynamic optimization method and the optimization tool is aimed to enable the direct evolutionary computing to perform within a practical computational time by FaSTAR. In the present project, basic programs are developed and validated using JSS2.

Goal

To develop the fastest aerodynamic optimization tool in the word and serve to Japanese aircraft industries or research institutes.

Objective

To develop an aerodynamic optimization tool by using the unstructured CFD code FaSTAR and to examine its validity and efficiency.

References and Links

N/A

Use of the Supercomputer

Each of aerodynamic optimization processes; CFD grid generation, grid deformation, CFD analysis (sample calculation), and evolutionary computation, are performed using JSS2.

Necessity of the Supercomputer

Aerodynamic optimization using an evolutionary algorithm requires a number of high-fidelity and large-scaled computations (3D RANS analysis) and needs to use the supercomputer.

Achievements of the Year

A new code of multi-objective evolutionary algorithm was developed and applied to a simple single wing optimization problem. Based on the ONERA M6 wing configuration, wing shape was arranged by changing the camber line and twist angle of wing tip and optimal value was computed to maximize the Lift to Drag ratio (L/D). The results agreed well with optimal values obtained from the Kriging regression model and the validity of the present code was cleared.

Annual Reoprt Figures for 2016

Fig.1:ONERA M6 grid deformation

 

Annual Reoprt Figures for 2016

Fig.2:Comparison of Optimal Solutions

 

Publications

N/A

Computational Information

  • Parallelization Methods: Process Parallelization
  • Process Parallelization Methods: MPI
  • Thread Parallelization Methods: n/a
  • Number of Processes: 256
  • Number of Threads per Process: 1
  • Number of Nodes Used: 8
  • Elapsed Time per Case (Hours): 3.5
  • Number of Cases: 7

Resources Used

 

Total Amount of Virtual Cost(Yen): 1,129,858

 

Breakdown List by Resources

Computational Resources
System Name Amount of Core Time(core x hours) Virtual Cost(Yen)
SORA-MA 490,890.67 807,335
SORA-PP 13,427.71 114,645
SORA-LM 5.00 112
SORA-TPP 0.00 0

 

SORA-FS File System Resources
File System Name Storage assigned(GiB) Virtual Cost(Yen)
/home 127.02 1,188
/data 15,238.09 142,543
/ltmp 6,845.24 64,033

 

J-SPACE Archiving System Resources
Archiving System Name Storage used(TiB) Virtual Cost(Yen)
J-SPACE 0.00 0

Note: Virtual Cost=amount of cost, using the unit price list of JAXA Facility Utilization program(2016)

JAXA Supercomputer System Annual Report April 2016-March 2017