基于声爆高效预测的超声速民机优化设计与数据挖掘

doi:10.7527/S1000-6893.2025.31920

Abstract

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

CLC Number:

V211.3

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 Aeronautica et Astronautica Sinica, 2025, 46(20): 531920.

Figures/Tables 30

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计算环节	计算耗时/s
	高效预测方法	CFD+增广Burgers方程
	高效预测方法	Euler	N-S
近场计算	241	24 915	53 665
远场计算	220	228	214
求和	461	25 143	53 879

最大过压/Pa		变化量/%
初始构型	Opt1构型	变化量/%
42.86	29.13	-32.03

传播角/（°）	最大过压/Pa		变化量/%
传播角/（°）	初始构型	Opt2构型	变化量/%
0	42.86	29.81	-30.45
30	40.81	27.48	-32.66

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

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