Optimization design and data mining for supersonic civil aircraft based on sonic boom efficient prediction

Zuotai LI; Shusheng CHEN; Shiyi JIN; Zhenghong GAO; Weiguo ZHOU

doi:10.7527/S1000-6893.2025.31920

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2025 , Vol. 46 >Issue 20: 531920 - 531920

DOI: https://doi.org/10.7527/S1000-6893.2025.31920

Special Issue: Key Technologies for Supersonic Civil Aircraft

Optimization design and data mining for supersonic civil aircraft based on sonic boom efficient prediction

Zuotai LI ,
Shusheng CHEN ,
Shiyi JIN ,
Zhenghong GAO ,
Weiguo ZHOU

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^1.School of Aeronautics，Northwestern Polytechnical University，Xi’an 710072，China
^2.National Key Laboratory of Aircraft Configuration Design，Xi’an 710072，China
^3.AVIC The First Aircraft Design Institute，Xi’an 710089，China

E-mail： sshengchen@nwpu.edu.cn

Received date: 2025-03-03

Revised date: 2025-04-01

Accepted date: 2025-05-18

Online published: 2025-05-27

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Abstract

Green SuperSonic Transport （SST） is a key future direction in civil aircraft， with sonic boom remaining an important factor affecting its development. Traditional CFD method is unsuitable for massive and iterative designs， due to large computation quantity and time-consumption. Sonic boom efficient prediction method based on area rule can significantly reduce computation time with a certain level of accuracy， making it suitable for the requirement of a large number of rapid calculations in the initial design phase of SST. Therefore， based on the efficient prediction method， we first establishe a framework for low sonic boom optimization design of SST. Then LM1021 no-nacelle configuration is optimized to verify the optimization in multiple propagation angles. The optimization results are validated by high-accuracy CFD method： the maximum overpressure in the far field is reduced by 32.03% considering the propagation angle of 0°， while the maximum overpressures are decreased by 30.45% and 32.66% respectively when it takes the angles of 0° and 30° into account together. In addition，sonic boom dataset of SST is generated with the help of efficient prediction method. Random forest and AdaBoost algorithms are applied for intelligent data mining to extract the key design variables of low sonic boom. Finally， the results of data mining are consistent with CFD flow fields and area rule， highlighting that the designs of the Quiet Spike， the airfoil and dihedral angle of wing root， and the tail shape are crucial for sonic boom mitigation. The combination of sonic boom optimization and data mining based on efficient prediction method demonstrates advantage of high efficiency， offering technical support for future SST design.

Key words： supersonic civil aircraft; sonic boom prediction; area rule; optimization design; data mining

Cite this article

Zuotai LI , Shusheng CHEN , Shiyi JIN , Zhenghong GAO , Weiguo ZHOU . Optimization design and data mining for supersonic civil aircraft based on sonic boom efficient prediction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(20) : 531920 -531920 . DOI: 10.7527/S1000-6893.2025.31920

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