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摘要: 计算网格是流体数值模拟误差的主要来源之一, 极大地影响着流动模拟结果的精度. 传统网格生成强烈依赖于用户经验, 加大了复杂飞行器网格生成的难度, 增加了气动特性预测数据的不确定度. 网格自适应技术是结合流动特性的计算网格自主优化技术, 可通过迭代优化消除网格因素造成的数值模拟误差, 能有效提高飞行器气动特性预测精度. 近年来在运输机高升力复杂构型的成功应用, 表明了网格自适应技术已发展到了较为成熟的阶段. 本文针对计算流体力学, 首先系统总结了网格自适应涉及的误差估计、网格编辑和物面几何保形三项关键技术的研究进展, 并介绍了相关的主要并行实现技术. 其次, 文中介绍了网格自适应技术在网格相关性分析、流场细节捕捉、气动特性计算和非定常流动模拟中的主要应用情况. 最后, 本文提出了网格自适应技术研究存在的问题及未来研究方向.Abstract: Computational mesh is one of the main source of errors in fluid numerical simulation, which greatly affects the accuracy of flow simulation result. Traditional mesh generation strongly depends on user experience, which increases the difficulty of mesh generation for complicated aircraft and increases the uncertainty of aerodynamic characteristics prediction data. Mesh adaptation is a mesh autonomous optimization technology combined with flow characteristics, which can eliminate numerical errors caused by mesh factors through iterative procedure, and can effectively improve the accuracy of aircraft aerodynamics prediction. In recent years, the successful application of mesh adaptation in the high-lift complicated configuration of transport aircraft shows that the adaptation technology has developed to a relatively mature stage. In this paper, for computational fluid dynamics, first of all, the research progress of three key techniques related to mesh adaptation, including error estimation, mesh editing and geometry shape preservation, is systematically summarized, and their parallel implementation techniques are briefly introduced. Secondly, the main applications of mesh adaptation in mesh correlation analysis, flow detail capture, aerodynamics prediction and unsteady flow simulation are introduced. Finally, the future research direction to tackle the existing problems of mesh adaptation are proposed at the end of the paper.
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图 2 两个度量张量相交的几何意义示意(Alauzet et al. 2007)
图 3 度量张量自适应与伴随自适应对比(Balan et al. 2019). 上: 阻力随网格规模的收敛特性, 下: 自适应后网格
图 4 r型网格优化示意图(Vivarelli et al. 2021). (a)优化前网格, (b)优化后网格
图 8 四面体各向异性网格生成(Alauzet和Loseille 2016). (a)流场等值线, (b)各向异性网格
图 10 基于单元的网格剖分模式 (Soukov 2022). (a) 六面体单元, (b) 三棱柱单元, (c) 金字塔单元, (d) 四面体单元
图 12 局部Coons曲面拟合示意图(Hindenlang et al. 2011)
图 13 后台阶流动网格相关性分析(Chila & Kaminski 2006). (a)监测点速度, (b)分离再附点位置
图 14 第三届高升力构型自适应计算(Alauzet & Clerici et al. 2022). (a)升力系数收敛过程, (b) 阻力系数收敛过程
图 17 阿波罗返回舱头激波自适应模拟流场(Bibb et al. 2006). (a)初始网格, (b)自适应两次网格, (c)自适应最终网格
图 18 声爆激波捕捉自适应模拟压力分布对比(Jones et al. 2006). (a)初始网格压力对比, (b)自适应后压力对比
图 19 C608低声爆飞机复杂波系各向异性自适应模拟后网格及流场分布(Vanharen et al. 2021)
图 20 NASA Rotor 37压气机流场模拟(Alauzet & Frazza et al. 2022). (a)自适应后网格及流场, (b)总压比收敛过程
图 21 第三届高升力构型自适应网格收敛计算(Alauzet & Clerici et al. 2022). (a)自适应网格收敛过程, (b)自适应后机翼等展向位置截面网格分布及流场
图 22 结合LES和网格自适应圆柱绕流模拟的分离涡系结构(Gou et al. 2018). (a)未使用自适应, (b)使用自适应
图 23 结合LES和压气机网格自适应模拟流动参数(Odier et al. 2021). (a)压气机构型, (b)自适应前后流动参数对比
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