Sonic Boom Analysis under Conditions of Atmospheric Uncertainty Using Polynomial Chaos
スポンサーリンク
概要
- 論文の詳細を見る
This study establishes an efficient approach for sonic boom analysis under conditions of atmospheric uncertainty, such as temperature, density and wind velocity. This approach combines a non-intrusive polynomial chaos (NIPC) method, which approximates the statistical behavior of output function under conditions of uncertainty, with the sonic boom analysis method using an augmented Burgers' equation, which is able to account for the rise time of the sonic boom signature. The present simulations demonstrate the superior capability of NIPC, which can estimate the statistical signature of sonic boom under conditions of atmospheric uncertainty, while ensuring the same accuracy and much less computational time compared to the Monte Carlo (MC) method. The present simulation results indicate that temperature uncertainty has an impact on the local rise in sonic boom pressure, and atmospheric humidity uncertainty has an impact on the entire shape of the sonic boom signature, while wind uncertainty has almost no impact.
著者
-
Shimoyama Koji
Institute Of Fluid Science Tohoku University
-
Hashimoto Atsushi
Japan Aerospace Exploration Agency
-
JEONG Shinkyu
Department of Mechanical Engineering, Kyunghee University
-
ONO Daichi
Institute of Fluid Science, Tohoku University
関連論文
- Multi-Objective Design Optimization of an Air Cleaner Fan Using Kriging Models
- Numerical Analysis of Flow through a Hole for Modeling of Wind Tunnel Porous Wall
- Numerical Analysis of Flow through a Hole for Modeling of Wind Tunnel Porous Wall
- Kriging/RBF-Hybrid Response Surface Methodology for Highly Nonlinear Functions
- Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm
- Aerodynamic Characteristics and Effects of Winglets of the Boomless Tapered Supersonic Biplane during the Starting Process
- Airport Terrain-Induced Turbulence Simulations Integrated with Weather Prediction Data
- Sonic Boom Analysis under Conditions of Atmospheric Uncertainty Using Polynomial Chaos