绿色超声速民机是未来民用飞行器的重要发展方向，而声爆仍是制约其发展的关键因素之一。传统CFD方法计算量大、耗时长，难以直接用于初期迭代设计。但基于面积律的声爆高效预测方法能够在保证一定精度的前提下大幅缩短计算时间，契合了超声速民机初期设计阶段大量、快速计算的需求。因此，本文基于声爆面积律高效预测方法，建立了一套超声速民机低声爆优化设计框架。以LM1021无短舱构型为初始构型验证了多声爆传播角下优化设计的有效性，优化结果均经过高精度CFD验证：0°传播角优化下的远场最大过压值降低32.03%；0°和30°传播角协同优化下的远场最大过压值分别降低30.45%、32.66%。借助声爆高效预测方法快速生成超声速民机声爆数据集，采用随机森林和自适应增强算法进行智能数据挖掘，提取超声速民机低声爆关键设计变量。经对比验证，数据挖掘结果与CFD流场特征、面积律理论相吻合，结果表明合理设计机鼻静音锥、翼根翼型及上反角、机尾外形是超声速民机低声爆设计的关键。基于高效预测方法开展的声爆优化设计和数据挖掘，表现出显著的效率优势，能够为后续的超声速民机设计工作提供技术支撑。
Green supersonic transport (SST) is one trend of civil aircraft in the future. Sonic boom is an important factor affecting on its development. Traditional CFD method isn’t suitable for massive and iterative designs, due to large computation quantity and time-consumption. However, sonic boom efficient prediction method based on area rule can significantly reduce computation time with a certain level of accuracy. It satisfies the requirement of a large number of rapid calculations in the initial design phase of SST. Therefore, the paper establishes a framework for low sonic boom optimization design of SST, based on the efficient prediction method. 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. 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. The results of data mining are consistent with CFD flow fields and area rule. The designs of the Quiet Spike, the airfoil and dihedral angle of wing root and the tail shape are of importance for sonic boom mitigation. Sonic boom optimization design and data mining based on efficient prediction method can provide support for SST design, with the advantage of high efficiency.
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