ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Transition prediction and uncertainty analysis of self-developed benchmark models for supersonic laminar wings
Received date: 2025-03-03
Revised date: 2025-03-24
Accepted date: 2025-04-07
Online published: 2025-05-19
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.
Yayun SHI , Xinze JI , Tihao YANG , Pengfei WU , Lu XIE , Junqiang BAI , Kaixuan FENG . Transition prediction and uncertainty analysis of self-developed benchmark models for supersonic laminar wings[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(20) : 531923 -531923 . DOI: 10.7527/S1000-6893.2025.31923
| [1] | LYNDE M N， CAMPBELL R L. Expanding the natural laminar flow boundary for supersonic transports［C］∥34th AIAA Applied Aerodynamics Conference. Reston：AIAA， 2016. |
| [2] | 徐悦， 韩忠华， 尤延铖， 等. 新一代绿色超声速民机的发展现状与挑战［J］. 科学通报， 2020， 65（2）：127-133. |
| XU Y， HAN Z H， YOU Y C， et al. Progress and challenges of next generation green supersonic civil aircraft［J］. Chinese Science Bulletin， 2020， 65（2）：127-133 （in Chinese）. | |
| [3] | 丁玉临， 韩忠华， 乔建领， 等. 超声速民机总体气动布局设计关键技术研究进展［J］. 航空学报， 2023， 44（2）： 626310. |
| DING Y L， HAN Z H， QIAO J L， et al. Research progress in key technologies for conceptual-aerodynamic configuration design of supersonic transport aircraft［J］. Acta Aeronautica et Astronautica sinica， 2023， 44（2）： 626310 （in Chinese）. | |
| [4] | DING Y L， HAN Z H， QIAO J L， et al. Inverse design method for low-boom supersonic transport with lift constraint［J］. AIAA Journal， 2023， 61（7）： 2840-2853. |
| [5] | MUNGUíA B C， ECONOMON T D， ALONSO J J. A Discrete adjoint framework for low-boom supersonic aircraft shape optimization［C］∥18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston： AIAA， 2017. |
| [6] | ISHIKAWA H， UEDA Y， TOKUGAWA N. Natural laminar flow Wing design for a low-boom supersonic aircraft［C］∥55th AIAA Aerospace Sciences Meeting. Reston： AIAA， 2017. |
| [7] | 李军府， 陈晴， 王伟， 等. 一种先进超声速民机低声爆高效气动布局设计［J］. 航空学报， 2024， 45（6）： 629613. |
| LI J F， CHEN Q， WANG W， et al. Design of low sonic boom high efficiency layout for advanced supersonic civil aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（6）： 629613 （in Chinese）. | |
| [8] | 单程军， 贡天宇， 易理哲， 等. 超声速民机高效高可信度声爆/气动多学科优化方法［J］. 航空学报， 2024， 45（24）： 630573. |
| SHAN C J， GONG T Y， YI L Z， et al. High-efficiency and high-reliability sonic boom/aerodynamic multidisciplinary optimization method for supersonic civil aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（24）： 630573 （in Chinese）. | |
| [9] | 袁吉森， 孙爵， 李玲玉， 等. 超声速飞机层流布局设计与评估技术进展［J］. 航空学报， 2022， 43（11）： 526316. |
| YUAN J S， SUN J， LI L Y， et al. Progress of supersonic aircraft laminar flow layout design and evaluation technologies［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（11）： 526316 （in Chinese）. | |
| [10] | VERMEERSCH O， YOSHIDA K， UEDA Y， et al. Natural laminar flow wing for supersonic conditions： Wind tunnel experiments， flight test and stability computations［J］. Progress in Aerospace Sciences， 2015， 79： 64-91. |
| [11] | UEDA Y， YOSHIDA K， MATSUSHIMA K， et al. Supersonic natural-laminar-flow wing-design concept at high-Reynolds-number conditions［J］. AIAA Journal， 2014， 52（6）： 1294-1306. |
| [12] | NORRIS G. Picture： Boeing plans plain grey natural laminar flow nacelles for 787s in bid to reduce fuel burn［EB/OL］. （2006-6-12）［2025-1-13］. . |
| [13] | Union European. Hybrid laminar flow control on tails & sing［EB/OL］.（2025-1-13）［2025-03-01］. . |
| [14] | SCHRAUF G， VON GEYR H. Hybrid laminar flow control on A320 fin： Retrofit design and sample results［J］. Journal of Aircraft， 2021， 58（6）： 1272-1280. |
| [15] | KRISHNAN K S G， BERTRAM O， SEIBEL O. Review of hybrid laminar flow control systems［J］. Progress in Aerospace Sciences， 2017， 93： 24-52. |
| [16] | SCHRAUF G. Large-scale laminar flow tests evaluated with linear stability theory［J］. Journal of Aircraft， 2004， 41（2）： 224-230. |
| [17] | SHI Y Y， CAO T S， YANG T H， et al. Estimation and analysis of hybrid laminar flow control on a transonic experiment［J］. AIAA Journal， 2020， 58（1）： 118-132. |
| [18] | SHI Y Y， YANG T H， BAI J Q， et al. Research of transition criterion for semi-empirical prediction method at specified transonic regime［J］. Aerospace Science and Technology， 2019， 88： 95-109. |
| [19] | YANG T H， CHEN Y F， SHI Y Y， et al. Stochastic investigation on the robustness of laminar-flow wings for flight tests［J］. AIAA Journal， 2022， 60（4）： 2266-2286. |
| [20] | YANG T H， ZHONG H， CHEN Y F， et al. Transition prediction and sensitivity analysis for a natural laminar flow wing glove flight experiment［J］. Chinese Journal of Aeronautics， 2021， 34（8）： 34-47. |
| [21] | STREIT T， WEDLER S， KRUSE M. DLR natural and hybrid transonic laminar wing design incorporating new methodologies［J］. The Aeronautical Journal， 2015， 119（1221）： 1303-1326. |
| [22] | SHI Y Y， MADER C A， HE S C， et al. Natural laminar-flow airfoil optimization design using a discrete adjoint approach［J］. AIAA Journal， 2020， 58（11）： 4702-4722. |
| [23] | 杨体浩， 王一雯， 王雨桐， 等. 基于离散伴随的层流翼优化设计方法［J］. 航空学报， 2022， 43（12）： 126132. |
| YANG T H， WANG Y W， WANG Y T， et al. Discrete adjoint-based optimization approach for laminar flow wings［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（12）： 126132 （in Chinese）. | |
| [24] | CHEN Y F， RAO H Y， XIONG N， et al. Adjoint-based robust optimization design of laminar flow airfoil under flight condition uncertainties［J］. Aerospace Science and Technology， 2023， 140： 108465. |
| [25] | CHEN Y F， RAO H Y， DENG Y J， et al. Adjoint-based robust optimization design of laminar flow wing under flight condition uncertainties［J］. Chinese Journal of Aeronautics， 2023， 36（6）： 19-34. |
| [26] | BARKLAGE A， R?MER U， BERTRAM A， et al. Analysis and uncertainty quantification of a hybrid laminar flow control system［J］. AIAA Journal， 2022， 60（10）： 5735-5749. |
| [27] | SABATER C， BEKEMEYER P， G?RTZ S. Robust design of transonic natural laminar flow wings under environmental and operational uncertainties［C］∥ AIAA Scitech 2021 Forum. Reston： AIAA， 2021. |
| [28] | OWENS L R， BEELER G， KING R， et al. Supersonic traveling crossflow wave characteristics in ground and flight tests［C］∥AIAA Scitech 2020 Forum. Reston： AIAA， 2020. |
| [29] | BARKLAGE A， R?MER U， SEITZ A， et al. Validation of suction velocity analysis for active laminar flow control with uncertainties［J］. AIAA Journal， 2023， 61（9）： 3910-3922. |
| [30] | YANG T H， WANG Y W， SHI Y Y， et al. Transition prediction for hybrid laminar flow control flight test considering geometric uncertainties［J］. Journal of Aerospace Engineering， 2022， 35（6）： 04022100. |
| [31] | ZHENG X H， YAO W， GONG Z Q， et al. Learnable quantile polynomial chaos expansion： An uncertainty quantification method for interval reliability analysis［J］. Reliability Engineering & System Safety， 2024， 245： 110036. |
| [32] | 赵超帆， 袁修开， 陈敬强. 结构全局失效概率函数估计的自适应增强线抽样方法［J］. 西北工业大学学报， 2023， 41（1）： 105-114. |
| ZHAO C F， YUAN X K， CHEN J Q. Structural global failure probability function estimation based on adaptive augmented line sampling method［J］. Journal of Northwestern Polytechnical University， 2023， 41（1）： 105-114 （in Chinese）. | |
| [33] | CHEN Y G， ZHONG R， WANG Q S， et al. Uncertain stochastic vibration characteristic analysis of composite laminated rectangular plate based on improved Kriging model［J］. Composite Structures， 2024， 340： 118180. |
| [34] | ZHANG W， ZHAO Z Y， XU H W， et al. AK-Gibbs： An active learning Kriging model based on Gibbs importance sampling algorithm for small failure probabilities［J］. Computer Methods in Applied Mechanics and Engineering， 2024， 426： 116992. |
| [35] | 韦新鹏， 姚中洋， 宝文礼， 等. 一种基于主动学习克里金模型的证据理论可靠性分析方法［J］. 机械工程学报， 2024， 60（2）： 356-368. |
| WEI X P， YAO Z Y， BAO W L， et al. Evidence-theory-based reliability analysis method using active-learning Kriging model［J］. Journal of Mechanical Engineering， 2024， 60（2）： 356-368 （in Chinese）. | |
| [36] | CEBECI T. Stability and transition： Theory and application： Efficient numerical methods with computer programs［M］. Cham：Springer， 2004. |
| [37] | ARNAL D. Boundary layer transition： Predictions based on linear theory［J］. AGARD， 1994. |
| [38] | UEDA Y， YOSHIDA K， MATSUSHIMA K， et al. Supersonic natural-laminar-flow wing-design concept at high-Reynolds-number conditions［J］. AIAA Journal， 2014， 52（6）： 1294-1306. |
| [39] | OWENS L R， BEELER G， KING R， et al. Supersonic crossflow transition control in ground and flight tests［C］∥2019 AIAA SciTech Forum and Exposition. Reston： AIAA， 2019. |
| [40] | ECHARD B， GAYTON N， LEMAIRE M. AK-MCS： An active learning reliability method combining Kriging and Monte Carlo simulation［J］. Structural Safety， 2011， 33（2）： 145-154. |
| [41] | FENG K X， LU Z Z， WANG L. A novel dual-stage adaptive Kriging method for profust reliability analysis［J］. Journal of Computational Physics， 2020， 419： 109701. |
| [42] | GARZON V E， DARMOFAL D L. Impact of geometric variability on axial compressor performance［J］. Journal of Turbomachinery， 2003， 125（4）： 692-703. |
| [43] | KENWAY G， KENNEDY G， MARTINS J R R A. A CAD-free approach to high-fidelity aerostructural optimization［C］∥13th AIAA/ISSMO Multidisciplinary Analysis Optimization conference. Reston： AIAA， 2010. |
| [44] | LIU D S， LITVINENKO A， SCHILLINGS C， et al. Quantification of airfoil geometry-induced aerodynamic uncertainties： Comparison of approaches［J］. ASA Journal on Uncertainty Quantification， 2017， 5（1）： 334-352. |
| [45] | ADLER R J， TAYLOR J E. Random fields and geometry［M］. Cham： Springer， 2009. |
/
| 〈 |
|
〉 |
CJA