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| 自适应网格技术在高速列车尾流场模拟中的应用 |
| Application of adaptive mesh refinement in wake flow simulation of high-speed trains |
| 投稿时间：2025-09-09 修订日期：2025-11-03 |
| DOI： |
| 中文关键词: 数值仿真 笛卡尔网格 自适应网格加密 流动捕捉 |
| 英文关键词:numerical simulation cartesian grid adaptive mesh refinement flow capture |
| 基金项目:无基金资助项目. |
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| 摘要点击次数: 64 |
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| 中文摘要: |
| 为解决高速列车尾流场模拟中尾迹区大尺度湍流结构捕捉对人工经验依赖强、网格敏感性高的问题，发展了基于单元型自适应笛卡尔网格（CAMR）的流场自适应方法，并探讨其在高速列车气动分析中的应用效果。结合雷诺平均Navier-Stokes方程和SST k-ω湍流模型,对比分析了基于涡量、湍动能及压力梯度三种自适应判据的数值模拟效果差异。研究采用八叉树结构的各向同性自适应策略,通过动态调整网格层级实现流场关键区域的局部加密,重点考察了不同判据对尾部大尺度分离涡结构的捕捉能力及其对数值模拟中风场分布结果的影响。结果表明：基于涡量和湍动能的判据可有效识别尾迹区高湍流区域,使自适应网格延伸至尾鼻尖后12.7倍车高范围,较初始网格新增约20%的网格量,但避免了人工加密块设置的经验依赖性,且显著提升了尾流核心速度幅值的计算精度和漩涡变形特征的解析能力；压力梯度判据虽能捕捉近壁面流线型区域的强梯度特征,但对远场尾迹的覆盖范围仅达6.7倍车高,导致数值结果中尾流区风速过早耗散。该研究为高速列车气动数值评估提供了兼顾计算效率与精度的网格自适应解决方案,揭示了不同判据的适用场景,其中涡量和湍动能判据在尾流捕捉方面优势显著。 |
| 英文摘要: |
| This study investigates the application of cell-based adaptive mesh refinement (CAMR) techniques to address empirical dependence and mesh sensitivity in high-speed train aerodynamic simulations. By combining RANS equations with the SST k-ω turbulence model, we compare three adaptive criteria: vorticity, turbulent kinetic energy (TKE), and pressure gradient. An isotropic octree-based strategy dynamically adjusts mesh resolution in critical flow regions, focusing on capturing large-scale separated vortex structures in the train wake. Results show that vorticity- and TKE-based criteria effectively identify high-turbulence regions, extending refinement up to 12.7 times the train height downstream, increasing total cell count by approximately 20% without manual subjectivity. These criteria significantly enhance computational accuracy for wake core velocities and vortex deformation. In contrast, the pressure gradient criterion refines only up to 6.7 times the train height, leading to premature velocity dissipation. This study provides a balanced CAMR solution for high-speed train aerodynamics, highlighting the superior performance of vorticity and TKE-based criteria in wake flow resolution. |
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