数值模拟方法对NASA CRM模型阻力预测的影响

doi:10.7527/S1000-6893.2019.23383

流体力学与飞行力学

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

数值模拟方法对NASA CRM模型阻力预测的影响

陈江涛, 赵娇, 章超, 刘深深, 张耀冰, 吴晓军

中国空气动力研究与发展中心计算空气动力研究所, 绵阳 621000

收稿日期:2019-08-14 修回日期:2019-10-28 出版日期:2020-04-15 发布日期:2019-10-24
通讯作者: 吴晓军 E-mail:huang7766@sina.com
基金资助:
国家数值风洞工程；装备预先研究项目（41406030102）

Effects of numerical simulation approaches on drag prediction of NASA CRM

CHEN Jiangtao, ZHAO Jiao, ZHANG Chao, LIU Shenshen, ZHANG Yaobing, WU Xiaojun

Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China

Received:2019-08-14 Revised:2019-10-28 Online:2020-04-15 Published:2019-10-24
Supported by:
National Numerical Windtunnel Program; Equipment Pre-research Project (41406030102)

摘要/Abstract

摘要： 在复杂工程外形的数值模拟中，网格类型、规模和分布、湍流模型、数值格式等都会不同程度影响模拟结果。如何评估这些模拟方法对模拟结果的影响，并识别对模拟结果影响较显著的因素，对于CFD的发展方向有积极的借鉴指导意义。为了综合研究不同因素对商用运输机外形阻力预测的影响，以AIAA第六届阻力预测会议外形NASA CRM为研究对象，同时考虑了网格、湍流模型、无黏通量格式和体心梯度求解方法等因素对阻力预测的影响。分析中采用枚举法和正交试验设计两种方法，并使用了基于聚类分析的定性敏感性分析方法和基于Mckay主影响分析的定量方法，识别出对阻力预测影响较大的因素，这为数值模拟方法的发展指明了方向。

关键词: 阻力预测, 网格加密, 湍流模型, 敏感性分析, 数值格式, CRM

Abstract: In numerical simulations of industry relevant geometries, plenty of factors will more or less affect the final results, including grid type/amount/distribution, turbulence model, numerical schemes, etc. It has become meaningful to quantify the effects of factors mentioned before on the simulation results and identify important areas needing additional research and development of CFD. In order to comprehensively study the impacts of several simulation options on the drag prediction of commercial transports, numerical investigations of the NASA Common Research Model from the 6th AIAA CFD Drag Prediction Workshop are performed in this paper. The effects of grid refinement, turbulence model, inviscid flux scheme, and the gradient computation method on drag prediction are investigated. Enumeration and orthogonal design of experiment are used to provide sampling data for analysis. The factors that have important contributions are identified by the kmeans clustering analysis and Mckay main effect analysis. The gradient computation method is recognized to be a key factor for total drag prediction, and more attention is needed in simulations of similar configurations and conditions.

Key words: drag prediction, grid refinement, turbulence model, sensitivity analysis, numerical scheme, CRM

中图分类号:

V211.3

陈江涛, 赵娇, 章超, 刘深深, 张耀冰, 吴晓军. 数值模拟方法对NASA CRM模型阻力预测的影响[J]. 航空学报, 2020, 41(4): 123383-123383.

CHEN Jiangtao, ZHAO Jiao, ZHANG Chao, LIU Shenshen, ZHANG Yaobing, WU Xiaojun. Effects of numerical simulation approaches on drag prediction of NASA CRM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(4): 123383-123383.

参考文献 31

[1]	LEVY D W, ZICKUHR T. Data summary from the first AIAA computational fluid dynamics drag prediction workshop[J]. Journal of Aircraft, 2003, 40(5):875-882.
[2]	LAFLIN K R, KLAUSMEYER S M. Data summary from second AIAA computational fluid dynamics drag prediction workshop[J]. Journal of Aircraft, 2005, 42(5):1165-1178.
[3]	VASSBERG J C, TINOCO E N. Abridged summary of the third AIAA computational fluid dynamics drag prediction workshop[J]. Journal of Aircraft, 2008, 45(3):781-798.
[4]	VASSBERG J C, TINOCO E N. Summary of the fourth AIAA computational fluid dynamics drag prediction workshop[J]. Journal of Aircraft, 2014, 51(4):1070-1089.
[5]	LEVY D W, LAFLIN K R. Summary of data from the fifth computational fluid dynamics drag prediction workshop[J]. Journal of Aircraft, 2014, 51(4):1194-1213.
[6]	TINOCOL E N, BRODERSEN O P. Summary of data from the sixth AIAA CFD drag prediction workshop:AIAA-2017-1208[R]. Reston, VA:AIAA, 2017.
[7]	RUMSEY C L, ALLISON D O. CFD sensitivity analysis of a modern civil transport near buffet-onset conditions:NASA TM-2001-211263[R]. Washington, D.C.:NASA, 2001.
[8]	王运涛. DPW IV~DPW VI数值模拟技术综述[J].航空学报,2018,39(4):021836. WANG Y T. An overview of DPW IV-DPW VI numerical simulation technology[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(4):021836(in Chinese).
[9]	王运涛,孟德虹. CRM-WB风洞模型高阶精度数值模拟[J].航空学报,2018,39(4):121642. WANG Y T, MENG D H. High-order numerical simulation of CRM-WB wind tunnel method[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(4):121642(in Chinese).
[10]	李伟,孟德虹.网格拓扑对DLR-F6构型数值模拟的影响[J].航空学报,2017,38(2):120177. LI W, MENG D H. Effect of mesh topology on numerical simulation of DLR-F6 configuration[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(2):120177(in Chinese).
[11]	张培红,张耀冰.面向混合网格高精度阻力预测的梯度求解方法[J].航空学报, 2018, 39(1):121415. ZHANG P H, ZHANG Y B. Gradient calculation method of unstructured mixed grids for improving drag prediction accuracy[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(1):121415(in Chinese).
[12]	张培红,张耀冰.面向混合网格高精度阻力预测的熵修正方法[J].航空学报, 2018, 39(9):122019. ZHANG P H, ZHANG Y B. Entropy correction method of unstructured mixed grids for improving drag prediction accuracy[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(9):122019(in Chinese).
[13]	VASSBERG J C, DEHAAN M A. Development of a common research model for applied CFD validation studies:AIAA-2008-6919[R]. Reston, VA:AIAA, 2008.
[14]	RIVERS M, DITTBERNER A. Experimental investigations of the NASA common research model:AIAA-2010-4218[R]. Reston, VA:AIAA, 2010.
[15]	RIVERS M, DITTBERNER A. Experimental investigations of the NASA common research model in the NASA Langley National Transonic Facility and NASA Ames 11-Ft transonic wind tunnel:AIAA-2011-1126[R]. Reston, VA:AIAA, 2011.
[16]	European Commission. European strategic wind tunnels improved research potential[EB/OL].(2014-10-30)[2016-11-23]. http://www.eswirp.aero/.
[17]	CHEN J T, ZHANG Y B, ZHOU N C, et al. Numerical investigations of the high-lift configuration with MFlow solver[J]. Journal of Aircraft, 2015, 52(4):1051-1062.
[18]	DISKIN B, THOMAS J L. Comparison of node-centered and cell-centered unstructured finite volume discretizations:Inviscid fluxes[J]. AIAA Journal, 2011, 49(4):836-854.
[19]	MAVRIPLIS D J. Revisiting the least-squares procedure for gradient reconstruction on unstructured meshes:AIAA-2003-3986[R]. Reston, VA:AIAA, 2003.
[20]	ROE P L. Approximate Riemann solvers[J]. Journal of Computational Physics,1981, 135(2):250-258.
[21]	KIM K H, KIM C, RHO O H. Accurate computations of hypersonic flows using AUSMPW+scheme and shock aligned grid technique:AIAA-1998-2442[R]. Reston, VA:AIAA, 1998.
[22]	TRAMEL R W, NICHOLS R H. Addition of improved shock-capturing schemes to OVERFLOW 2.1:AIAA-2009-3988[R]. Reston, VA:AIAA, 2009.
[23]	SPALART P R, ALLMARAS S R. A one-equation turbulence model for aerodynamic flows:AIAA-1992-0439[R]. Reston, VA:AIAA, 1992.
[24]	MENTER F R. Two-equation eddy-viscosity turbulence models for engineering applications[J]. AIAA Journal, 1994, 32(8):1598-1605.
[25]	SPALART P R. Strategies for turbulence modelling and simulation[J]. International Journal of Heat and Fluid Flow, 2000, 21:252-263.
[26]	MANI M, BABCOCK D A, WINKLER C M, et al. Predictions of a supersonic turbulent flow in a square duct:AIAA-2013-0860[R]. Reston, VA:AIAA, 2013.
[27]	SCLAFANI A J, VASSBERG J C, RUMSEY C, et al. Drag prediction for the NASA CRM wing/body/tail using CFL3D and OVERFLOW on an overset mesh:AIAA-2010-4219[R]. Reston, VA:AIAA, 2010.
[28]	YAMAMOTO K, TANAKA K, MURAYAMA M. Effect of a nonlinear constitutive relation for turbulence modeling on predicting flow separation at wing-body juncture of transonic commercial aircraft:AIAA-2012-2895[R]. Reston, VA:AIAA, 2012.
[29]	ARTHUR D, VASSILVITSKII S. K-means++:The advantages of careful seeding[M]//SODA'07:Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007:1027-1035.
[30]	田口玄一.试验设计法(上)[M].北京:机械工业出版社,1987. GENICHI T. Experimental design method (part 1)[M]. Beijing:Machinery Industry Press, 1987(in Chinese).
[31]	茆诗松,周纪芗,陈颖主.试验设计[M]. 2版.北京:中国统计出版社,2012. MAO S S, ZHOU J X, CHEN Y Z. Experimental design[M]. 2nd ed. Beijing:China Statistics Press, 2012(in Chinese).

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

数值模拟方法对NASA CRM模型阻力预测的影响

Effects of numerical simulation approaches on drag prediction of NASA CRM

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 31

相关文章 15

编辑推荐

Metrics

本文评价

[1]	汪洪波, 曾宇, 熊大鹏, 杨揖心, 孙明波. SST湍流模型的激波与可压缩效应改进[J]. 航空学报, 2024, 45(3): 128694-128694.
[2]	姜乐, 陈以彪, 李炎军, 李贵林, 刘涛. 航空发动机增压式离心通风器流动与分离特性[J]. 航空学报, 2024, 45(2): 128675-128675.
[3]	张恺玲, 李思怡, 段毅, 阎超. 进气道流动中SST湍流模型参数的不确定度量化[J]. 航空学报, 2023, 44(S2): 729429-729429.
[4]	孙晓哲, 侯东, 杨建忠. 双余度机电作动器力纷争机理及敏感性[J]. 航空学报, 2023, 44(S1): 727661-727661.
[5]	曾宇, 汪洪波, 孙明波, 王超, 刘旭. SST湍流模型改进研究综述[J]. 航空学报, 2023, 44(9): 27411-027411.
[6]	王志凯, 陈盛, 范玮. 神经网络宽度对燃烧室排放预测的影响[J]. 航空学报, 2023, 44(5): 126816-126816.
[7]	张振康, 徐万武, 李智严, 叶伟. 钝锥体辐射平衡温度不确定度量化分析[J]. 航空学报, 2023, 44(15): 528673-528673.
[8]	刘锬韬, 李瑞宇, 高丽敏, 赵磊. 基于数据同化的试验数据驱动的叶栅流场预测[J]. 航空学报, 2023, 44(14): 628201-628201.
[9]	陈涛, 徐兴平, 张宏达, 韩省思. 自适应湍流耦合建表燃烧模型的振荡燃烧数值模拟[J]. 航空学报, 2023, 44(14): 628207-628207.
[10]	舒博文, 黄江涛, 高正红, 刘刚, 何成军, 夏露. 二元激波矢量喷管矢量性能敏感性分析[J]. 航空学报, 2023, 44(13): 127831-127831.
[11]	陈伟芳, 孙精华, 田得阳, 陈烨斯. 物理化学模型对流场电磁散射特性的影响[J]. 航空学报, 2023, 44(12): 127707-127707.
[12]	田得阳, 平熠, 陈烨斯, 陈伟芳. 物理化学模型对流场电磁波传播特性影响[J]. 航空学报, 2022, 43(S2): 214-224.
[13]	王睿, 周洲, 郭荣化, 黄悦琛. 太阳能无人机伞降着陆多体动力学仿真与试验[J]. 航空学报, 2022, 43(8): 225721-225721.
[14]	张昊元, 孙东, 邱波, 朱言旦, 王安龄. 湍动能在激波/边界层干扰流动中的影响[J]. 航空学报, 2022, 43(7): 125504-125504.
[15]	张冬辉, 张泰华, 崔燕香, 陈臣, 王生. 平流层系留气球气动参数敏感性分析[J]. 航空学报, 2022, 43(5): 125083-125083.