基于Kriging模型的风洞应变天平静态校准方法
收稿日期: 2016-01-15
修回日期: 2016-03-09
网络出版日期: 2016-03-18
基金资助
国家自然科学基金(11372337)
A static calibration method of wind tunnel strain-gage balance based on Kriging model
Received date: 2016-01-15
Revised date: 2016-03-09
Online published: 2016-03-18
Supported by
National Natural Science Foundation of China (11372337)
本文提出了一种基于Kriging代理模型的风洞应变天平静态校准方法。采用拉丁超立方设计的试验设计(DOE)方法选取校准加载点,以六分量组合加载技术为支撑,采用一阶回归基函数和EXP关联函数对校准数据构建Kriging模型,以电压信号增量和其他条件变量为自变量,通过Kriging模型对天平所受载荷进行预测。校准结果表明,与传统的OFAT(One Factor at A Time)校准方法相比,该方法具有相同的准度,加载点数量极大减少。将该方法应用于某矢量喷管推力测量天平的校准工作中,以通气流量和天平电压信号增量作为自变量,较好预测了喷管产生的推力,解决了传统校准方法难以克服的通气影响问题。
刘志勇 , 苗磊 , 陶洋 , 张诣 , 王树民 . 基于Kriging模型的风洞应变天平静态校准方法[J]. 航空学报, 2016 , 37(12) : 3685 -3691 . DOI: 10.7527/S1000-6893.2016.0069
A new static calibration method for strain-gage balance used in wind tunnel test is introduced here. A design of experiments (DOE) method, Latin hypercube design, is used to choose samples. Kriging surrogate models constructed by using first order regression base function and EXP correlation function are mathematical models which predict loads by taking measured voltage increments and other factors as parameters, and six-component combining load calibration technique is used too. Compared with the traditional one factor at a time (OFAT) method, the new method not only has equivalent accuracy, but also remarkably reduces the number of samples. The new method is used in the calibration of some balance which is used to measure the thrust of a vector nozzle, and predicted thrust forces well. It figures out the interference of gas inflow which is hardly handled by traditional calibration method.
Key words: Kriging model; design of experiments; strain-gage balance; calibration; wind tunnel
[1] 王发祥. 高速风洞试验[M]. 北京:国防工业出版社, 2003. WANG F X. High speed wind tunnel test[M]. Beijing:National Defense Industry Press, 2003(in Chinese).
[2] PARKER P A. A single-vector force calibration method featuring the modern design of experiments:AIAA-2001-0170[R]. Reston:AIAA, 2001.
[3] DELOACH R, PHILIPSEN I. Stepwise regression analysis of MDOE balance calibration data acquired at DNW[C]//45th AIAA Aerospace Sciences Meeting and Exhibit. Reston:AIAA, 2007.
[4] ULBRICH N, VOLDEN T. A new approach to strain-gage balance calibration analysis[C]//5th International Symposium on Strain-Gauge Balances, 2006.
[5] DELOACH R. Applications of modern experiment design to wind tunnel testing at NASA Langley Research Center[C]//36th AIAA Aerospace Sciences Meeting and Exhibit. Reston:AIAA,1998.
[6] BERGMANN R. An experimental comparison of different load tables for balance calibration:AIAA-2010-4544[R]. Reston:AIAA, 2010.
[7] HUFNAGEL K. The 2nd generation balance calibration machine of Darmstadt University of Technology:AIAA-2007-0148[R]. Reston:AIAA, 2007.
[8] ULBRICH N. Combined load diagram for a wind tunnel strain-gage balance:AIAA-2010-4203[R]. Reston:AIAA, 2010.
[9] KEITH C L, SEAN A C, THOMAS H J, et al. Thermal and pressure characterization of a wind tunnel force balance using the single vector system[C]//49th AIAA Aerospace Sciences Meeting. Reston:AIAA,2011.
[10] ULBRICH N. Iterative strain-gage balance calibration data analysis for extended independent variable sets:AIAA-2011-0011[R]. Reston:AIAA, 2011.
[11] LANDMAN D, YODER D. Wind-tunnel balance calibration with temperature using design of experiments[J]. Journal of Aircraft, 2014, 51(3):841-848.
[12] ULBRICH N, VOLDEN T. Development of a new software tool for balance calibration:AIAA-2006-3434[R]. Reston:AIAA, 2006.
[13] 湛华海, 张旭, 吕治国, 等. 一种单矢量风洞天平校准系统设计[J]. 实验流体力学, 2014, 28(1):70-74. ZHAN H H, ZHANG X, LU Z G, et al. Design of single vector wind tunnel balance calibration system[J]. Journal of Experiments in Fluid Mechanics, 2014, 28(1):70-74(in Chinese).
[14] 罗华云, 赖传兴, 王月贵, 等. 喷管模型试验器六分量天平校准技术[J]. 航空动力学报, 2013, 28(1):67-73. LUO H Y, LAI C X, WANG Y G, et al. Six-component balance calibration technology for nozzle model testing facility[J]. Journal of Aerospace Power, 2013, 28(1):67-73(in Chinese).
[15] 王晓锋, 席光, 王尚锦. Kriging与响应面方法在气动优化设计中的应用[J]. 工程热物理学报, 2005, 26(3):423-425. WANG X F, XI G, WANG S J. Application of Kriging and response surface method in aerodynamics optimization design[J]. Journal of Engineering Thermophysics, 2005, 26(3):423-425(in Chinese).
[16] SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments[J]. Statistical Science, 1989, 4(4):409-435.
[17] NOEL C. Spatial prediction and ordinary Kriging[J]. Mathematical Geology, 1988, 20(4):405-421.
[18] 邹林君. 基于Kriging模型的全局优化方法研究[D]. 武汉:华中科技大学, 2011. ZOU L J. Research on global optimization algorithm based on Kriging model[D]. Wuhan:Huazhong University of Science and Technology, 2011(in Chinese).
[19] TIMOTHY W S, JANET K A, FARROKH M. Spatial correlation metamodels for global approximation in structural design optimization[C]//Proceedings of ASME Design Engineering Technical Conference, 1998:1-12.
[20] ART B O. Orthogonal arrays for computer experiments, integration and visualization[J]. Statistical Science, 1992, 2(2):439-452.
[21] VIANA F A C. Surrogates toolbox user's guide[M]. (2010)[2016-01-15]. http://sites.google.com/site/fchegury.
/
〈 | 〉 |