JAXA Supercomputer System Annual Report April 2020-March 2021

Report Number: R20ETET51

Subject Category: Skills Acquisition System

PDF available here

• Responsible Representative: AOYAMA Takashi, Director, Aeronautical Technology Directrate, Numerical Simulation Research Unit
• Contact Information: Yukari Sakano(y.sakano@akane.waseda.jp)
• Members: Yukari Sakano

Abstract

The purpose of the research is to construct the numerical model of cross flow atomization which can predict the particle size distribution precisely and efficiently. As the previous models recognized all droplets as spheres and caused irregularities, this study aims to consider droplets' shapes. Finally, we are going to improve the calculation efficiency by applying the deep learning technologies to the analysis.

The development of this model contributes to efficient designing of the aerospace engines, as this cross flow atomization is introduced to the main burners of them.

Reference URL

N/A

Reasons and benefits of using JAXA Supercomputer System

We used the supercomputer to get datasets for the model construction.

As it is difficult to capture the atomization phenomenon by experimental way in cross flow,

we have to run detailed numerical calculations which require large calculation costs.

At first, we took spatial and chronological sampling of the droplets in the detailed calculation for grasping shape distributions and droplets' motions. Using this information, we are constructing the model based on geometrical and statistical analysis.

Achievements of the Year

By conducting the spatial sampling for the result of detailed numerical analysis, we gathered the data of droplet shapes in cross flow atomization. Fig.1 shows the example of these droplets. In addition to that, the facts that the geometric parameter can evaluate droplet shapes and deep learning technologies can be applied for improve the efficiency of the calculation costs in the model are indicated.

Analyzing these results and combining more detailed analysis or chronological droplet information, the construction of the model considering droplet shapes, which is the goal of the study, is in progress.

Fig.1: The example of droplets extracted from detailed numerical calculation of the cross flow

Publications

- Oral Presentations

Sakano, Y., Sato, T., Evaluation of 3D Shape Representation Method of Droplets in Crossflow Type Atomization, The Fifty-Eighth Symposium (Japanese) on Combustion, Online, Dec. 2020.

Sakano, Y., Sato, T., 3D Shape Recognition of Droplets in Atomization Modeling Based on Deep Learning Technique, 29th ILASS-Japan Symposium, Online, Dec. 2020.

Usage of JSS

Computational Information

• Process Parallelization Methods: MPI
• Thread Parallelization Methods: N/A
• Number of Processes: 1452
• Elapsed Time per Case: 24 Hour(s)

Resources Used(JSS2)

Fraction of Usage in Total Resources*1(%): 0.00

Details

Please refer to System Configuration of JSS2 for the system configuration and major specifications of JSS2.

*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.

Resources Used(JSS3)

Fraction of Usage in Total Resources*1(%): 0.00

Details

Please refer to System Configuration of JSS3 for the system configuration and major specifications of JSS3.

*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.

JAXA Supercomputer System Annual Report April 2020-March 2021