非结构混合网格鲁棒自适应技术

doi:10.7527/S1000-6893.2019.22894

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

非结构混合网格鲁棒自适应技术

唐静^1,2, 崔鹏程¹, 贾洪印¹, 李彬¹, 李欢¹

1. 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;
2. 西北工业大学航空学院, 西安 710072

收稿日期:2019-01-08 修回日期:2019-02-03 出版日期:2019-10-15 发布日期:2019-02-26
通讯作者: 贾洪印 E-mail:hongyin@mail.ustc.edu.cn

Robust adaptation techniques for unstructured hybrid mesh

TANG Jing^1,2, CUI Pengcheng¹, JIA Hongyin¹, LI Bin¹, LI Huan¹

1. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China;
2. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received:2019-01-08 Revised:2019-02-03 Online:2019-10-15 Published:2019-02-26

摘要/Abstract

摘要： 数值格式、湍流模型和计算网格是影响CFD数值模拟精度的3个主要因素。结合流场信息的网格自适应技术具备动态优化计算网格的能力，被NASA列为未来CFD发展的一项关键技术。本文针对非结构混合网格，发展了网格单元分布优化、表面网格几何投影和空间网格协调匹配3项关键技术，建立了高鲁棒性几何保真的网格自适应系统。首先，为了提高自适应方法的鲁棒性和通用性，发展了基于标准面网格的多面体网格单元分布优化方法。其次，发展了仅依赖表面网格信息的局部曲面重构技术，采用参数点映射方法实现了新增表面网格点的几何投影，消除了自适应系统对几何CAD系统的依赖。再次，采用改进的距离函数方法实现了空间网格与投影后表面网格的快速匹配。最后，结合基于流场特征的自适应探测器，采用二阶格式的有限体积方法，开展了30P30N三段翼绕流和三角翼大迎角绕流的网格自适应数值模拟。结果表明，通过网格自适应对网格单元的分布进行优化后，流场求解的收敛性和模拟精度都得到了显著提高。

关键词: 网格自适应, 混合网格, 加密和粗化, 曲面重构, 表面网格投影, 30P30N三段翼, 三角翼

Abstract: Numerical schemes, turbulence models, and computational mesh are the three main factors that affect the precision of CFD simulation. Mesh adaptation based on the information of flow field has the ability to dynamically optimize the mesh, which is considered by NASA as a vital technique for the development of CFD in the future. Aimed at the unstructured hybrid mesh, three critical techniques, including the distribution optimization of mesh elements, surface mesh projection onto geometry, and volume mesh conformal deformation, are proposed to establish a robust adaptation system with high geometry fidelity. First, the distribution optimization technique based on polyhedral elements with standard faces are developed to improve the robustness and the universality of the adaptation system. Next, the local surface reconstruction method which relies only on the points of surface mesh is developed. The new inserted surface points can be parametrically projected onto the geometry, eliminating the dependency of mesh adaptation on the CAD system. Then, the improved distance function method is adopted to rapidly match the volume mesh elements to the projected surface mesh. Finally, combined with the mesh adaptation based on the flow features detector, the flow simulation with the second order finite volume method is carried out for the 30P30N airfoil and the delta-wing with a large angle of attack. The numerical results show that, after the mesh optimization with mesh adaptation proposed in this paper, both the convergence performance and precision of the flow simulation are significantly improved.

Key words: mesh adaptation, hybrid mesh, refinement and coarsening, surface reconstruction, surface mesh projection, 30P30N airfoil, delta-wing

中图分类号:

V211.3

唐静, 崔鹏程, 贾洪印, 李彬, 李欢. 非结构混合网格鲁棒自适应技术[J]. 航空学报, 2019, 40(10): 122894-122894.

TANG Jing, CUI Pengcheng, JIA Hongyin, LI Bin, LI Huan. Robust adaptation techniques for unstructured hybrid mesh[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(10): 122894-122894.

参考文献 40

[1]	BAKER T J. Mesh generation:Art or science[J]. Progress in Aerospace Sciences, 2005, 41(1):29-63.
[2]	SLOTNIK J, KHODADOUST A, ALONSO J, et al. CFD vision 2030 study:A path to revolutionary computational aeroscience:NASA/CR-2014-218178[R]. Washington, D.C.:NASA, 2014.
[3]	THOMPSON J F. A survey of dynamically-adaptive grids in the numerical solution of partial differential equations[J]. Applied in Numerical Mathematics, 1985, 1(1):3-37.
[4]	ROY C J. Review of discretization error estimators in scientific computing:AIAA-2010-0126[R]. Reston, VA:AIAA, 2010.
[5]	FIDKOWSKI K J, DARMOFAL D L. Review of output-based error estimation and mesh adaptation in computational fluid dynamics[J]. AIAA Journal, 2011, 49(4):673-694.
[6]	ALAUZET F, LOSEILLE A. A decade of progress on anisotropic mesh adaptation for computational fluid dynamics[J]. Computer-Aided Design, 2016, 72:13-39.
[7]	PARK M A, KRAKOS J A, MICHAL T, et al. Unstructured grid adaptation:Status, potential impacts, and recommended investments toward CFD vision 2030[C]//46th AIAA Fluid Dynamics Conference. Reston, VA:AIAA, 2016.
[8]	阎超, 于剑, 徐晶磊, 等. CFD模拟方法的发展成就与展望[J].力学进展, 2011, 41(5):562-589. YAN C, YU J, XU J L, et al. On the achievements and prospects for the methods of computational fluid dynamics[J]. Advances in Mechanics, 2011, 41(5):562-589(in Chinese).
[9]	CHEN J, ZHENG J, ZHENG Y, et al. Tetrahedral mesh improvement by shell transformation[J]. Engineering with Computers, 2017, 33(3):393-414.
[10]	SI H, GOERIGK N. On tetrahedralisations of generalized chazelle polyhedra with interior steiner points[J]. Computer-Aided Design, 2018, 103(1):61-72.
[11]	崔鹏程, 邓有奇, 唐静, 等. 基于伴随方程的网格自适应及误差修正[J]. 航空学报, 2016, 37(10):2992-3002. CUI P C, DENG Y Q, TANG J, et al. Adjoint equations-based grid adaptation and error correction[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(10):2992-3002(in Chinese).
[12]	LINN R V, AWRUCH A M. Edge-based anisotropic mesh adaptation of unstructured meshes with applications to compressible flows[J]. Engineering with Computers, 2017, 33:1007-1025.
[13]	ALAUZET F, LOSEILLE A, MARCUM D, et al. Assessment of anisotropic mesh adaptation for high-lift prediction of the HL-CRM configuration[C]//23rd AIAA Computational Fluid Dynamics Conference. Reston, VA:AIAA, 2017.
[14]	ALRUTZ T. Hybrid grid adaptation in TAU[C]//Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 2005:115.
[15]	JOUBARNE E, GUIBAULT F, BRAUN O, et al. Numerical capture of wing tip vortex improved by mesh adaptation[J]. International Journal for Numerical Methods in Fluids, 2009, 67(1):8-32.
[16]	LEPAGE C Y, SUERICH-GULICK F, HABASHI W G. Anisotropic 3-D mesh adaptation on unstructured hybrid meshes:AIAA-2002-14318[R]. Reston, VA:AIAA, 2002.
[17]	PARK M A. Three-dimensional turbulent RANS adjoint-based error correction:AIAA-2003-3849[R]. Reston, VA:AIAA, 2003.
[18]	李立, 白文, 梁益华. 基于伴随方程方法的非结构网格自适应技术及应用[J]. 空气动力学学报, 2011, 29(3):316-309. LI L, BAI W, LIANG Y H. An adjoint-based method for unstructured mesh adaptation and its applications[J]. Acta Aerodynamica Sinica, 2011, 29(3):316-309(in Chinese).
[19]	唐静, 郑鸣, 邓有奇, 等. 网格自适应技术在复杂外形流场模拟中的应用[J]. 计算力学学报, 2015, 32(6):752-757. TANG J, ZHENG M, DENG Y Q, et al. Grid adaptation for flow simulation of complicated configuration[J]. Chinese Journal of Computational Mechanics, 2015, 32(6):752-757(in Chinese).
[20]	SAHNI O, OVCHARENKO A, CHITALE K C, et al. Parallel anisotropic mesh adaptation with boundary layers for automated viscous flow simulations[J]. Engineering with Computers, 2017, 33:767-795.
[21]	OVCHARENKO A, CHITALE K, SAHNI O, et al. Parallel adaptive boundary layer meshing for CFD analysis[C]//Proceedings of the 21st International Meshing Roundtable. Berlin:Springer, 2013:455.
[22]	YANG H Q, CHEN Z J, PRZEKWAS A. Adaptive mesh refinement with high-order scheme for an unstructured pressure-based solver:AIAA-2014-0077[R]. Reston, VA:AIAA, 2014.
[23]	LIU Z, YANG Y, GONG A, et al. Unstructured adaptive grid refinement for flow feature capture[J]. Procedia Engineering, 2015, 99:477-483.
[24]	WOOPEN M, MAY G, SCHÜTZ J. Adjoint-based error estimation and mesh adaptation for hybridized discontinuous Galerkin methods[J]. International Journal for Numerical Methods in Fluids, 2014, 76(3):811-834.
[25]	张贺, 钟诚文, 宫建, 等. 气体动理论BGK格式的网格自适应方法[J]. 航空学报, 2014, 35(3):687-694. ZHANG H, ZHONG C W, GONG J, et al. Adaptive mesh refinement for gas-kinetic BGK scheme[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(3):687-694(in Chinese).
[26]	SENGUTTUVAN V, CHALASANI S, LUKE E A, et al. Adaptive mesh refinement using general elements:AIAA-2005-0927[R]. Reston, VA:AIAA, 2005.
[27]	张扬, 张来平, 赫新, 等. 基于自适应混合网格的脱体涡模拟[J]. 航空学报, 2016, 37(12):3605-3614. ZHANG Y, ZHANG L P, HE X, et al. Detached eddy simulation based on adaptive hybrid grids[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(12):3605-3614(in Chinese).
[28]	MOXEY D, GREEN M D, SHERWIN S J, et al. An isoparametric approach to high-order curvilinear boundary-layer meshing[J]. Computer Methods in Applied Mechanics and Engineering, 2015, 23(7):636-650.
[29]	HINDENLANG F, NEUDORFER J, GASSNER G, et al. Unstructured three-dimensional high order grids for discontinuous Galerkin schemes:AIAA-2011-3853[R]. Reston, VA:AIAA, 2011.
[30]	XU J, CHERNIKOV A N. Automatic curvilinear quality mesh generation driven by smooth boundary and guaranteed fidelity[J]. Procedia Engineering, 2014, 82:200-212.
[31]	唐静, 邓有奇, 马明生, 等. 飞翼气动优化中参数化和网格变形技术研究[J]. 航空学报, 2015, 36(5):1480-1490. TANG J, DENG Y Q, MA M S, et al. Parametrization and grid deformation techniques for flying-wing aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(5):1480-1490(in Chinese).
[32]	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.
[33]	ROE P L. Approximate Riemann solvers, parameter vectors, and difference schemes[J]. Journal of Computational Physics, 1981, 43:357-372.
[34]	KIM J S, KWON O J. Improvement on block LU-SGS scheme for unstructured mesh Navier-Stokes computations:AIAA-2002-1061[R]. Reston, VA:AIAA, 2002.
[35]	SPALART S R. A one-equation turbulence model for aerodynamic flows:AIAA-1992-0439[R]. Reston, VA:AIAA, 1992.
[36]	GONG X Q, CHEN J T, ZHOU N C, et al. The effects of turbulence model corrections on drag prediction of NASA common research model:AIAA-2014-4371[R]. Reston, VA:AIAA, 2014.
[37]	KAMKAR S J, WISSINK A M, SANKARAN V, et al. Feature-driven Cartesian adaptive mesh refinement for vortex-dominated flows[J]. Journal of Computational Physics, 2011, 230:6271-6298.
[38]	PASCHAL K, GOODMAN W, MCGHEE R, et al. Evaluation of tunnel sidewall boundary-layer-control systems for high-lift airfoil testing:AIAA-1991-3243[R]. Reston, VA:AIAA, 1991.
[39]	周云龙, 刘伟, 董义道, 等. 五阶HWCNS在低速复杂流场中的应用[J]. 国防科技大学学报, 2016, 38(4):1-7. ZHOU Y L, LIU W, DONG Y D, et al. Application of fifth-order accurate HWCNS for low-speed complex flow field[J]. Journal of National University of Defense Technology, 2016, 38(4):1-7(in Chinese).
[40]	CHU J, LUCKRING J. Experimental surface pressure data obtained on 65° delta wing across Reynolds number and Mach number ranges, Volume 1-Sharp leading edge:NASA TM 4645[R]. Washington, D.C.:NASA Langley Research Center, 1996.

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

非结构混合网格鲁棒自适应技术

Robust adaptation techniques for unstructured hybrid mesh

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 40

相关文章 15

编辑推荐

Metrics

本文评价

[1]	唐亮, 王凯, 李文龙, 刘亚洲, 张波涛, 任孝文. 倾斜射流撞壁铺展的数值仿真[J]. 航空学报, 2023, 44(4): 126428-126428.
[2]	韩荣, 刘伟, 杨小亮. 高超声速飞行器自适应减阻盘动态减阻机理[J]. 航空学报, 2023, 44(4): 126633-126633.
[3]	陈树生, 张兆康, 李金平, 冯聪, 高正红. 一种宽速域乘波三角翼气动布局设计[J]. 航空学报, 2023, 44(23): 128441-128441.
[4]	陈尹, 顾蕴松, 孙之骏, 黄紫. 基于翼面压力的飞行器气动力感知技术与自由飞验证[J]. 航空学报, 2021, 42(3): 124138-124138.
[5]	杨起, 刘伟, 杨小亮, 李昊. 三角翼机翼摇滚主动控制多学科耦合数值模拟[J]. 航空学报, 2021, 42(12): 124685-124685.
[6]	唐静, 张健, 李彬, 崔鹏程, 周乃春. 非结构混合网格自适应并行技术[J]. 航空学报, 2020, 41(1): 123202-123202.
[7]	牛中国, 赵光银, 梁华, 柳平. 三角翼DBD等离子体流动控制研究进展[J]. 航空学报, 2019, 40(3): 22201-022201.
[8]	张培红, 张耀冰, 周桂宇, 陈江涛, 邓有奇. 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): 122019-122030.
[9]	张培红, 张耀冰, 周桂宇, 陈江涛, 邓有奇. 面向非结构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): 121415-121415.
[10]	孙俊磊, 王和平, 周洲, 雷珊. 螺旋桨滑流对菱形翼布局无人机气动的影响[J]. 航空学报, 2018, 39(1): 121431-121431.
[11]	王科雷, 祝小平, 周洲, 王红波. 低雷诺数分布式螺旋桨滑流气动影响[J]. 航空学报, 2016, 37(9): 2669-2678.
[12]	张军, 艾宇, 黄达, 刘晶. 三角翼大迎角风洞试验支架干扰数值模拟研究[J]. 航空学报, 2016, 37(8): 2481-2489.
[13]	张扬, 张来平, 赫新, 邓小刚. 基于自适应混合网格的脱体涡模拟[J]. 航空学报, 2016, 37(12): 3605-3614.
[14]	张健, 邓有奇, 李彬, 张耀冰. 一种适用于三维混合网格的GMRES加速收敛新方法[J]. 航空学报, 2016, 37(11): 3226-3235.
[15]	崔鹏程, 邓有奇, 唐静, 李彬. 基于伴随方程的网格自适应及误差修正[J]. 航空学报, 2016, 37(10): 2992-3002.