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

Tekaslan H. E., Yıldız Ş., Demiroğlu Y., Nikbay M.

Journal of Aircraft, vol.60, no.2, pp.410-422, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 60 Issue: 2
  • Publication Date: 2023
  • Doi Number: 10.2514/1.c036962
  • Journal Name: Journal of Aircraft
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.410-422
  • Istanbul Technical University Affiliated: Yes


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.