ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Airfoil CFD pressure coefficient modification method based on layered interpolation
Received date: 2025-09-16
Revised date: 2025-10-10
Accepted date: 2025-11-05
Online published: 2025-11-10
Supported by
National Numerical Wind Tunnel Engineering(NNW-FSI-2025);National Key Laboratory for Aerodynamics Science and Technology of Aerospace Flight(SKLA-JSSX-2024-JJXM-06)
Due to actors such as calculation grid resolution， numerical discretization error， and turbulence model adaptability， obvious deviation often exist between CFD predicted pressure data and experimental pressure data. It is thus risky to directly use CFD predicted pressure data to carry out structural strength or aeroelastic analysis. To solve the problem of insufficient precision and large error of traditional single interpolation method when dealing with the pressure coefficient modified of discontinuous changes of suction peak and shock wave position， this paper develops a method for correcting airfoil CFD pressure data based on hierarchical interpolation， enabling high accuracy and high resolution pressure distribution reconstruction. Firstly， the difference between the pressure distribution data at the test point and the pressure distribution data predicted by CFD is calculated by linear interpolation， and then the difference is decomposed into continuous smooth part and discontinuous part by Laplace fairing method. The continuous smooth part of pressure difference is processed by high-order interpolation， and the discontinuous smooth part is processed by linear interpolation. If there is still warpage in the pressure data near the shock wave position after interpolation， the warped part will be smoothed by shock position detection and local position linear interpolation. The method is tested using the RAE2822 airfoil test and CFD computed pressure data， and applied to the DLR-F6 wing-body assembly and HIRENASD wing computed pressure coefficient modified.Results from both test and application show that the delamination pressure data modified method achieves high accuracy， and can obtain more accurate modified results even under the condition of less pressure test measurement points.
Xu ZHANG , Zhe WENG , Zhuolin ZHAO , Jianing CHEN , Zibin ZHAO , Yan SUN . Airfoil CFD pressure coefficient modification method based on layered interpolation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(8) : 132788 -132788 . DOI: 10.7527/S1000-6893.2025.32788
| [1] | LI R Z， ZHANG Y F， CHEN H X. Knowledge discovery with computational fluid dynamics： Supercritical airfoil database and drag divergence prediction［J］. Physics of Fluids， 2023， 35： 016113. |
| [2] | ZHOU H J， XIE F F， JI T W， et al. Fast transonic flow prediction enables efficient aerodynamic design［J］. Physics of Fluids， 2023， 35（2）： 026109. |
| [3] | YANG H， CHEN S S， GAO Z H， et al. Reynolds number effect correction of multi-fidelity aerodynamic distributions from wind tunnel and simulation data［J］. Physics of Fluids， 2023， 35（10）： 103113. |
| [4] | WANG Z， ZHANG W W， WANG X， et al. High precision aerodynamic heat prediction method based on data augmentation and transfer learning［J］. Aerospace Science and Technology， 2024， 155： 109663. |
| [5] | 樊云翔， 艾化楠， 王明振， 等. 基于深度学习的水上飞机非定常水载荷重构［J］. 航空学报， 2024， 45（20）： 129882. |
| FAN Y X， AI H N， WANG M Z， et al. Unsteady hydrodynamic load reconstruction of seaplane based on deep learning［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（20）： 129882 （in Chinese）. | |
| [6] | ZHAO X， DENG Z C， ZHANG W W. Sparse reconstruction of surface pressure coefficient based on compressed sensing［J］. Experiments in Fluids， 2022， 63（10）： 156. |
| [7] | 罗长童， 胡宗民， 刘云峰， 等. 高超声速风洞气动力/热试验数据天地相关性研究进展［J］. 实验流体力学， 2020， 34（3）： 78-89. |
| LUO C T， HU Z M， LIU Y F， et al. Research progress on ground-to-flight correlation of aerodynamic force and heating data from hypersonic wind tunnels［J］. Journal of Experiments in Fluid Mechanics， 2020， 34（3）： 78-89 （in Chinese）. | |
| [8] | DOWELL E H. Eigenmode analysis in unsteady aerodynamics-reduced-order models［J］. AIAA Journal， 1996， 34（8）： 1578-1583. |
| [9] | 邓晨， 陈功， 王文正， 等. 基于飞行试验和风洞试验数据的融合算法研究［J］. 空气动力学学报， 2022， 40（6）： 45-50. |
| DENG C， CHEN G， WANG W Z， et al. Research on the data fusion algorithm based on flight test data and wind tunnel test data［J］. Acta Aerodynamica Sinica， 2022， 40（6）： 45-50 （in Chinese）. | |
| [10] | LI S Y， GAO Z H， GAO C， et al. A successive gappy proper orthogonal decomposition approach and its application to inverse airfoil design［C］∥55th AIAA Aerospace Sciences Meeting. Reston： AIAA， 2017. |
| [11] | JIANG C Y， SOH Y C， LI H. Sensor and CFD data fusion for airflow field estimation［J］. Applied Thermal Engineering， 2016， 92： 149-161. |
| [12] | MOHAMED A， WOOD D. Deep learning predictions of unsteady aerodynamic loads on an airfoil model pitched over the entire operating range［J］. Physics of Fluids， 2023， 35（5）： 053113. |
| [13] | LEI R W， BAI J Q， WANG H， et al. Deep learning based multistage method for inverse design of supercritical airfoil［J］. Aerospace Science and Technology， 2021， 119： 107101. |
| [14] | LI K， KOU J Q， ZHANG W W. Deep learning for multifidelity aerodynamic distribution modeling from experimental and simulation data［J］. AIAA Journal， 2022， 60（7）： 4413-4427. |
| [15] | KOU J Q， NING C J， ZHANG W W. Transfer learning for flow reconstruction based on multifidelity data［J］. AIAA Journal， 2022， 60（10）： 5821-5842. |
| [16] | WANG X， KOU J Q， ZHANG W W. Unsteady aerodynamic prediction for iced airfoil based on multi-task learning［J］. Physics of Fluids， 2022， 34（8）： 087117. |
| [17] | YU J， HESTHAVEN J S. Flowfield reconstruction method using artificial neural network［J］. AIAA Journal， 2019， 57（2）： 482-498. |
| [18] | LIU X F， FENG Z M， CHEN Y H， et al. Multiple optimized support vector regression for multi-sensor data fusion of weigh-in-motion system［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2020， 234（12）： 2807-2821. |
| [19] | 孙岩， 邓小刚， 张征宇， 等. 跨声速风洞模型变形测量实验中标记点影响研究［J］. 空气动力学学报， 2013， 31（6）： 769-775. |
| SUN Y， DENG X G， ZHANG Z Y， et al. Target influence on video model deformation experiments in transonic wind tunnel［J］. Acta Aerodynamica Sinica， 2013， 31（6）： 769-775 （in Chinese）. | |
| [20] | 孙岩， 江盟， 孟德虹， 等. 交互式棱柱网格生成中翘曲现象形成机制及消除算法［J］. 航空学报， 2021， 42（6）： 124443. |
| SUN Y， JIANG M， MENG D H， et al. Formation mechanism and elimination algorithm of warping in interactive prismatic grid generation［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（6）： 124443 （in Chinese）. | |
| [21] | RENDALL T C S， ALLEN C B. Unified fluid-structure interpolation and mesh motion using radial basis functions［J］. International Journal for Numerical Methods in Engineering， 2008， 74（10）： 1519-1559. |
| [22] | RENDALL T S， ALLEN C B. Multi-dimensional aircraft surface pressure interpolation using radial basis functions［J］. Proceedings of the Institution of Mechanical Engineers， Part G： Journal of Aerospace Engineering， 2008， 222（4）： 483-495. |
| [23] | 孙岩， 孟德虹， 王运涛， 等. 基于径向基函数与混合背景网格的动态网格变形方法［J］. 航空学报， 2016， 37（5）： 1462-1472. |
| SUN Y， MENG D H， WANG Y T， et al. Dynamic grid deformation method based on radial basis function and hybrid background grid［J］. Acta Aeronautica et Astronautica Sinica， 2016， 37（5）： 1462-1472 （in Chinese）. | |
| [24] | NANS Glenn Research Center. RaE2822 airfoil Pressure distributions and flowfield date ［EB/OL］. （2021-02-10）［2025-10-24］. . |
| [25] | CHWALOWSKI P， HEEG J. FUN3D analyses in support of the first aeroelastic prediction workshop［C］∥ 51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Reston： AIAA， 2013. |
| [26] | BALLMANN J， DAFNIS A， KORSCH H， et al. Experimental analysis of high Reynolds number aero-structural dynamics in ETW［C］∥46th AIAA Aerospace Sciences Meeting and Exhibit， Reston： AIAA， 2008. |
| [27] | 郭秋亭， 孙岩， 郭正， 等. 风洞试验雷诺数/静气动弹性效应分离方法［J］. 航空学报， 2022， 43（11）： 526312. |
| GUO Q T， SUN Y， GUO Z， et al. Separation method for Reynolds number/static aeroelastic coupling effect in wind tunnel test［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（11）： 526312 （in Chinese）. |
/
| 〈 |
|
〉 |
CJA