航空学报 > 2025, Vol. 46 Issue (20): 531923-531923   doi: 10.7527/S1000-6893.2025.31923

超声速层流翼自主标模转捩预测及不确定性分析

史亚云1,2(), 季昕泽3,4, 杨体浩3,4, 吴鹏飞3,4, 谢露5, 白俊强6,7, 冯凯旋1,2   

  1. 1.西安交通大学 航天航空学院,西安 710049
    2.复杂服役环境重大装备结构强度与寿命全国重点实验室,西安 710049
    3.西北工业大学 航空学院,西安 710072
    4.飞行器基础布局全国重点实验室,西安 710072
    5.航空工业第一飞机设计研究院,西安 710089
    6.西北工业大学 无人系统研究院,西安 710072
    7.无人飞行器技术全国重点实验室,西安 710072
  • 收稿日期:2025-03-03 修回日期:2025-03-24 接受日期:2025-04-07 出版日期:2025-05-20 发布日期:2025-05-19
  • 通讯作者: 史亚云 E-mail:yayunshi@xjtu.edu.cn

Transition prediction and uncertainty analysis of self-developed benchmark models for supersonic laminar wings

Yayun SHI1,2(), Xinze JI3,4, Tihao YANG3,4, Pengfei WU3,4, Lu XIE5, Junqiang BAI6,7, Kaixuan FENG1,2   

  1. 1.School of Aerospace Engineering,Xi’an Jiaotong University,Xi’an 710049,China
    2.State Key Laboratory for Strength and Vibration of Mechanical Structures,Xi’an 710049,China
    3.School of Aeronautic,Northwestern Polytechnical University,Xi’an 710072,China
    4.National Key Laboratory of Aircraft Configuration Design,Xi’an 710072,China
    5.AVIC The First Aircraft Design Institute,Xi’an 710089,China
    6.Unmanned System Research Institute,Northwestern Polytechnical University,Xi’an 710072,China
    7.National Key Laboratory of Unmanned Aerial Vehicle Technology,Xi’an 710072,China
  • Received:2025-03-03 Revised:2025-03-24 Accepted:2025-04-07 Online:2025-05-20 Published:2025-05-19
  • Contact: Yayun SHI E-mail:yayunshi@xjtu.edu.cn

摘要:

层流减阻技术是超声速民机实现综合性能提升的关键技术之一。但是超声速层流转捩预测模型标定以及超声速层流翼层流维系能力对外界扰动的敏感性仍需进一步研究。设计了分别具有超声速前缘及亚声速前缘的2副超声速层流翼自主标模。风洞试验结果显示,超声速前缘层流翼可维持30%~60%当地弦长的层流区,亚声速前缘层流翼可维持约20%~70%当地弦长的层流区。试验与数值分析表明,构建的基于线性稳定理论的eN 转捩预测方法能够准确地捕捉超声速层流翼的转捩现象,并标定转捩阈值为5.2。进一步,发展了自适应Kriging方法量化揭示了转捩阈值、运营工况及几何等多源不确定性因素影响层流翼标模层流维系能力的规律。不确定性量化分析表明各不确定性因素对不同超声速层流翼标模性能的影响程度差异显著。超声速前缘层流翼标模的转捩位置受转捩阈值影响最大,而亚声速前缘层流翼标模受马赫数扰动最为明显。设计的超声速层流自主标模、标定的转捩阈值及揭示的不确定性影响规律对实现超声速层流翼的转捩精确预测及气动稳健优化设计具有重要意义。

关键词: 超声速层流翼, 自主标模, 转捩预测, 自适应Kriging, 不确定性分析, 转捩阈值

Abstract:

Laminar flow drag reduction is one of the key technologies for enhancing the comprehensive performance of supersonic civil aircraft. However, further research is required in its engineering application, particularly regarding the calibration of transition prediction models for supersonic laminar wings and their sensitivity to external disturbances. In this paper, two self-developed benchmark models of supersonic laminar wing are designed, one with a supersonic leading edge and the other with a subsonic leading edge. Wind tunnel test results demonstrated that the supersonic leading-edge laminar wing maintained a laminar region of 30%c to 60%c, while the subsonic leading-edge laminar wing exhibited laminar regions spanning approximately 20%c to 70%c. Experimental and numerical analysis confirmed that the eN transition prediction method, based on linear stability theory, accurately captured the transition phenomena of supersonic laminar wings, with a calibrated critical N factor of 5.2. Furthermore, the self-adaptive Kriging method was developed to quantify the impact of high-dimensional, multi-source uncertainties, including critical N factor, operating conditions, and geometry deviation on transition prediction. Uncertainty quantification analysis reveals substantial variation in the influence degree of uncertainty factors on the performance metrics of different supersonic laminar wing benchmark models. The critical N factor has the greatest influence on transition prediction for the supersonic leading-edge laminar model. For the subsonic leading-edge laminar model, Mach number disturbances have the most significant impact on transition prediction. The self-developed benchmark models for supersonic laminar wings designed in this paper, along with the calibrated critical N factor and revealed uncertainty influence mechanisms, are of significant importance for achieving precise transition prediction and aerodynamic robust design optimization for supersonic laminar wings.

Key words: supersonic laminar wings, self-developed benchmark models, transition prediction, adaptive Kriging, uncertainty analysis, transition critical

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