摘要
在计算流体力学中,网格质量极大影响着数值模拟结果精度及运算效率。气泡堆积法无需考虑相交判断且数据结构较为简单,在网格生成效率和质量上具有明显优势。本文在传统气泡堆积法的基础上,优化了通过移动节点提升网格质量的过程,并将其定义为Bubble-Opt方法。其中,采用了与神经网络结合的气泡半径选取方法生成初始气泡,利用改进的气泡动态移动技术将气泡调整至合适位置,进而通过Delaunay方法连接气泡中心形成最终优化网格。然后,将不同气泡半径选取方法以及不同过程参数下Bubble-Opt方法的优化效果进行对比。以二维圆柱绕流为例,测试了优化前后网格几何质量和过渡比。对于该算例,存在一组最优参数和最佳半径选取方法,使得网格质量优化效果最佳,平均过渡比可提高约17.37%,平均网格质量可提高约13.60%,并且可显著提高最低过渡比以及最低网格质量。最后,在该半径选取方法和过程参数下,以二维圆柱绕流和NACA0012翼型流动为例,分别从定性和定量的角度将数值模拟结果与试验数据对比,可见整体网格质量显著提高。
In computational fluid dynamics,mesh quality greatly affects the accuracy and computational efficiency of numerical simulation results.The Bubble does not require the consideration of intersection judgments and has a relatively simple data structure,which has significant advantages in mesh generation efficiency and quality.The process of improving the mesh quality by moving nodes based on the traditional Bubble is optimized in this article,and we define it as the Bubble-Opt method.In this method,a bubble radius selection method combined with neural networks is used to generate the initial bubbles,and an improved bubble dynamic movement technique is used to adjust the bubbles to the appropriate position.The Delaunay method is used to connect the center of bubbles to form the final optimized mesh.Then,the optimization effects of different bubble radius selection methods and Bubble-Opt methods are compared under different process parameters.Taking the flow around a 2D cylinder as an example,the geometric quality and transition ratio of the mesh before and after optimization are tested.For this example,there is a set of optimal parameters and a radius selection method that achieve the best mesh quality optimization effect.The average transition ratio can be improved by about 17.37%,the average mesh quality can be improved by about 13.60%,and the minimum transition ratio and minimum mesh quality can be significantly improved.Finally,under the radius selection method and process parameters,taking two-dimensional cylindrical flow and NACA0012 airfoil flow as examples,the numerical simulation results are compared with experimental data from both qualitative and quantitative perspectives,indicating a significant improvement in the overall grid quality.
作者
王娜娜
韩升
田野
WANG Nana;HAN Sheng;TIAN Ye(Network Security Department,Shanxi Police College,Taiyuan 030401,China;College of Computer Science and Technology,Beijing Jiaotong University,Beijing 100044,China;School of Computer Science and Technology,Taiyuan Normal University,Jinzhong 030619,China)
出处
《计算力学学报》
北大核心
2025年第5期871-876,共6页
Chinese Journal of Computational Mechanics
基金
山西省高等学校科技创新项目(2020L0716)
山西省高等学校一般性教学改革项目(J2021844,J20231556)
山西省青少年发展研究立项课题(JT2023E86)资助项目.
关键词
气泡堆积法
网格优化
机器学习
计算流体力学
Bubble
mesh optimization
machine learning
computational fluid dynamics
