JAXA Supercomputer System Annual Report (April 2018-March 2019)

An attempt to develop a method for modeling holistic phenomena
without a prerequisite knowledge
Report Number: R18EDA201N09
Subject Category: Aeronautical Technology
URL: https://www.jss.jaxa.jp/en/ar/e2018/9130/
Responsible Representative
Takashi Aoyama, Aeronautical Technology Directorate, Numerical Simulation Research Unit
Contact Information
Masashi Kanamori, Aeronautical Technology Directorate, Numerical Simulation Research Unit
(kanamori.masashi@jaxa.jp)
Members
Masashi Kanamori, Shogo Goto
Abstract
It is important to gain a fast and accurate feedback from computational results in a design process. A traditional
way of Computational Fluid Dynamics (CFD) gives physically acceptable results at the costs of tremendous
amount of computational resources and time. Our research group is therefore working on a machine-learningbased approach aimed at an innovative design tool.
Reasons for using JSS2High acurracy prediction by machine learning requires a wide variety of data the amount of which should be aslarge as possible. This conversely indicates poorly predicted results as a consequence of a shortage of data. The
combination of the processing capability of JSS2 and a fast algorithm implemented in our CFD code, FaSTAR
can serve us a plenty of data within a short period of time and its data productivity is necessary for the success ofour research.
Achievements of the Year
We prepared a large number of computational results mainly for two-dimensional NACA airfoils using JSS2
(Fig. 1).
We trained a neural network by the results and predicted the flow field around the two-dimensional airfoil by
the network.
The prediction of the trained data succeeded to reproduce the original distribution correctly (Fig. 2). And areproducibility was confirmed in the prediction of untrained data (Fig. 3).
In the future, we aim to improve the reproducibility for machine learning prediction by training the wider varietyof data.

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JAXA Special Publication JAXA-SP-19-003E

Fig. 1: List of training data made by JSS2

Fig. 2: Prediction result for trained data

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JAXA Supercomputer System Annual Report (April 2018-March 2019)

Fig. 3: Prediction result for untrained dataPublications
N/AUsage of JSS2
Computational InformationProcess Parallelization Methods

MPI

Thread Parallelization Methods

OpenMP

Number of Processes

2 - 64

Elapsed Time per Case

6 Hour (s)Resources Used
Fraction of Usage in Total Resources*1 (%): 0.02－15－

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JAXA Special Publication JAXA-SP-19-003E

Details
Computational Resources
System Name

Amount of Core Time(core x hours)

Fraction of Usage*2 (%)

SORA-MA

48,374.72

0.01

SORA-PP

16,502.38

0.13

SORA-LM

0.00

0.00

SORA-TPP

0.00

0.00

File System Resources
File System Name

Storage Assigned (GiB)

Fraction of Usage*2 (%)/home

5.960.01/data

6,103.52

0.11/ltmp

1,220.70

0.10Archiver Resources
Archiver Name

Storage Used (TiB)

J-SPACE

Fraction of Usage*2 (%)
0.00

0.00

*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.

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