Implementation of Multidisciplinary and Multifidelity Uncertainty Quantification Methods for Sonic Boom Prediction

被引:1
|
作者
Tekaslan, Huseyin Emre [1 ]
Yildiz, Sihmehmet [1 ]
Demiroglu, Yusuf [1 ]
Nikbay, Melike [1 ]
机构
[1] Istanbul Tech Univ, Fac Aeronaut & Astronaut, AeroMDO Lab, TR-34469 Istanbul, Turkey
来源
JOURNAL OF AIRCRAFT | 2023年 / 60卷 / 02期
关键词
OPTIMIZATION;
D O I
10.2514/1.C036962
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
To advance a supersonic aircraft design process, an uncertainty quantification study is conducted for sonic boom prediction while considering uncertainties associated with flight and atmospheric conditions. The uncertainty quantification process is implemented within a multidisciplinary analyses framework and assisted with a multifidelity surrogate model based approach. The sonic boom prediction framework requires input from the flowfield pressure distribution solution to generate the near-field pressure signature of the aircraft, which is then propagated throughout the atmosphere to the ground by using aeroacoustic methods. The open-source SU2 suite is employed as a high-fidelity flow solver tool to obtain the aerodynamic solution, while in-house postprocessing scripts are developed to generate the required near-field pressure signature. For low-fidelity flow analysis, A502 PAN AIR, a higher-order panel code which solves flows around slender bodies in low angles of attack for subsonic and supersonic regimes, is used. For nonlinear aeroacoustic propagation, NASA Langley Research Center's code sBOOM exploits the near-field pressure signature for both high-fidelity and low-fidelity sonic boom calculations. Efficient uncertainty quantification tools are developed in house by implementing multifidelity polynomial chaos expansion and multifidelity Monte Carlo methods. Several flight and atmospheric parameters are selected to include randomness where these uncertainties are propagated into the sonic boom loudness prediction of the JAXA wing-body model, which is a low boom aircraft. Finally, an overall assessment of the multifidelity uncertainty quantification methods is presented in terms of efficiency and numerical accuracy.
引用
收藏
页码:410 / 422
页数:13
相关论文
共 50 条
  • [21] A CFD Based Sonic Boom Prediction Method and Investigation on the Parameters Affecting the Sonic Boom Signature
    Lengyan
    Qian Zhansen
    2014 ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY, APISAT2014, 2015, 99 : 433 - 451
  • [22] Uncertainty Quantification and Certification Prediction of Low-Boom Supersonic Aircraft Configurations
    West, Thomas K.
    Reuter, Bryan W.
    Walker, Eric L.
    Kleb, Bil
    Park, Michael A.
    JOURNAL OF AIRCRAFT, 2017, 54 (01): : 40 - 53
  • [23] Uncertainty quantification and certification prediction of low-boom supersonic aircraft configurations
    1600, AIAA International, 12700 Sunrise Valley Drive, Suite 200Reston, VA, Virginia, Virginia 20191-5807, United States (54):
  • [24] Spectrally accurate prediction of sonic boom signals
    Pilon, Anthony R.
    AIAA JOURNAL, 2007, 45 (09) : 2149 - 2156
  • [25] Spectrally accurate prediction of sonic boom signals
    Pilon, Anthony R.
    AIAA Journal, 2007, 45 (09): : 2149 - 2156
  • [26] SONIC-BOOM STRUCTURAL DAMAGE PREDICTION
    HERSHEY, R
    HIGGINS, T
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 1974, 55 (02): : 439 - 439
  • [27] MULTIFIDELITY UNCERTAINTY QUANTIFICATION FOR ONLINE SIMULATIONS OF AUTOMOTIVE PROPULSION SYSTEMS
    Yang, Hang
    Gorodetsky, Alex
    Fujii, Yuji
    Wang, Kon-Well
    PROCEEDINGS OF ASME 2021 INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, IDETC-CIE2021, VOL 9, 2021,
  • [28] Uncertainty quantification of spent nuclear fuel with multifidelity Monte Carlo
    Alba, Arnau
    Adelmann, Andreas
    Rochman, Dimitri
    ANNALS OF NUCLEAR ENERGY, 2024, 211
  • [29] CALCULATION OF SONIC BOOM SIGNATURES BY BICHARACTERISTIC METHODS
    DAVIS, SS
    AIAA JOURNAL, 1972, 10 (09) : 1211 - &
  • [30] Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems
    Chaudhuri, Anirban
    Lam, Remi
    Willcox, Karen
    AIAA JOURNAL, 2018, 56 (01) : 235 - 249