Aerodynamic Simulations on Airframe Noise Reduction Technology (FQUROH-2)

JAXA Supercomputer System Annual Report February 2025-January 2026

Report Number: R25EDA101R20

Subject Category: Aeronautical Technology

Responsible Representative: Atsushi Kanda, Program Director of Aviation Technology, Aviation Technology Directorate
Contact Information: Takehisa Takaishi, FQUROH-2 Project Team (Airframe Noise Reduction Technology Project Team), Aviation Technology Directorate(takaishi.takehisa@jaxa.jp)
Members: Takehisa Takaishi, Mitsuhiro Murayama, Yasushi Ito, Takashi Ishida, Yoimi Kojima, Kazuomi Yamamoto, Kentaro Tanaka, Tohru Hirai, Gen Nakano

Abstract

Major airports in Japan are considering increasing the number of takeoffs and landings to meet the projected growth in air travel demand, strengthen their international competitiveness, and enhance passenger convenience. To support this expansion, it is essential to advance technologies that reduce airframe noise—particularly noise generated by high-lift devices and landing gear—so that surrounding communities are not adversely affected. Our approach includes developing a flight-test plan using a commercial aircraft to demonstrate airframe-noise reduction under real operating conditions. In parallel, we have created an 8%-scale semi-span wind tunnel model based on NASA's High-Lift Common Research Model (CRM-HL) to conduct further demonstrations using a generic aircraft configuration. These efforts represent key steps toward the practical development of effective noise-reduction technologies. We also use computational simulations to confirm the feasibility of noise-reduction concepts and associated design methods. This analysis evaluates the aerodynamic impact of proposed noise-reduction strategies on overall aircraft performance, ensuring that noise benefits are achieved without compromising safety or efficiency.

Reference URL

Please refer to https://www.aero.jaxa.jp/eng/research/ecat/fquroh/ .

Reasons and benefits of using JAXA Supercomputer System

The JSS3 enabled rapid execution of many high‑fidelity Reynolds‑Averaged Navier–Stokes (RANS) simulations that capture aerodynamically important details across multiple flight configurations within the expected flight envelope. This capability enables evaluation and quantification of the aerodynamic effects of low‑noise devices—something that is difficult to achieve solely through wind tunnel testing.

Achievements of the Year

In this project, our objective is to apply the airframe noise-reduction technology we developed to a commercial airplane and demonstrate its effectiveness in flight. Additionally, we aim to verify that this technology works effectively across a broader range of airplane configurations. To achieve this, we apply the technology to the High-Lift Common Research Model (CRM-HL), which represents the shape of the latest commercial airplane, to demonstrate its effectiveness in reducing noise. Before this evaluation, we performed steady Reynolds-averaged Navier–Stokes (RANS) analyses to investigate two key points: (1) how the noise-reduction devices installed on the wind-tunnel model affect the aerodynamic characteristics of the airplane, and (2) to conduct preliminary studies for PIV measurements planned in the wind-tunnel tests (Fig. 1). It is widely recognized that steady RANS analysis tends to overpredict boundary-layer separation at high angles of attack. As a step toward addressing this issue, we evaluated the influence of grid resolution on the analysis results, and the outcomes of this study were presented externally.

Fig.1: Cross-sectional total pressure distribution and surface skin‑friction coefficient distribution of the CRM‑HL, viewed from the upstream side (left) and downstream side (right) (Reynolds number of 2.62 million and an angle of attack of 8 degrees).

Publications

- Non peer-reviewed papers

1) Kojima, Y., Murayama, M., Ito, Y., Ishida, T., Tanaka, K., and Hirai, T., "Numerical Sensitivity of the Slat Brackets Wake on Fixed Grid RANS Simulations of the High-Lift Configuration Aircraft," AIAA Paper 2026-1564, AIAA SCITECH 2026 Forum, Orlando, FL, January 2026, DOI: 10.2514/6.2026-1564.

Usage of JSS

Computational Information

Process Parallelization Methods: MPI
Thread Parallelization Methods: OpenMP
Number of Processes: 64 - 432
Elapsed Time per Case: 22.3 Hour(s)

JSS3 Resources Used

Fraction of Usage in Total Resources*1(%): 0.28

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	7520994.47	0.34
TOKI-ST	1378.52	0.00
TOKI-GP	0.00	0.00
TOKI-XM	0.00	0.00
TOKI-LM	0.00	0.00
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	0.00	0.00
/data and /data2	1024.00	0.01
/ssd	0.00	0.00

Archiver Resources
Archiver Name	Storage Used (TiB)	Fraction of Usage*2(%)
J-SPACE	887.20	2.70

*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)	115.15	0.08

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