| Citation: | MA C，SHU B W，HUANG J T，et al. Knowledge mining of aircraft configuration design for sonic boom/aerodynamics[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（3）：975-984 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0310
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Sonic boom suppression is a key technology in the development of a new generation of supersonic civil aircraft. The configuration parameters of aircraft have an important influence on its sonic boom characteristics. Data mining (DM) can search hidden information from a large amount of data through algorithms, thus becoming a powerful tool to extract aircraft design knowledge. Five configuration parameters, including the sweep angle, aspect ratio, taper ratio, dihedral angle and fuselage slenderness ratio, are selected as design variables. The objective function is defined as the perceived noise level, and the lift and drag coefficients are calculated as aerodynamic measurement indexes. The knowledge base of low sonic boom design is extracted based on the data mining system composed of analysis of variance (ANOVA), decision tree and self-organizing feature map (SOM). The method obtains the correlation between the selected design variables and sonic boom/aerodynamic force, realizing the stratification and dimensionality reduction of design variables. The sweep angle has the first design priority, the slenderness ratio and the dihedral angle the second, and the aspect ratio and taper ratio are low sensitive variables. For the aircraft of the example in this paper, choosing a larger sweep angle and dihedral angle in a reasonable range can minimize the sonic boom and design a small aspect ratio configuration suitable for supersonic cruises.
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