面向燃油电磁阀的流场仿真网格生成方法

doi:10.11996/JG.j.2095-302X.2026051095

图学学报 ›› 2026, Vol. 47 ›› Issue (5): 1095-1102.DOI: 10.11996/JG.j.2095-302X.2026051095

• 计算机图形学与虚拟现实 • 上一篇下一篇

面向燃油电磁阀的流场仿真网格生成方法

肖海丽¹, 胡宇桐²^,³^,⁴, 满春雷¹, 曹洋¹, 李海生²^,³^,⁴()

¹ 中国航发长春控制科技有限公司，吉林长春 130102
² 北京工商大学计算机与人工智能学院，北京 100048
³ 商业数据安全防护与智能治理北京市重点实验室，北京 100048
⁴ 食品安全大数据技术北京市重点实验室，北京 100048

收稿日期:2025-08-22 接受日期:2026-06-25 出版日期:2026-10-31 发布日期:2026-10-26
通讯作者:李海生，E-mail：lihsh@th.btbu.edu.cn
基金资助:
国家自然科学基金(62277001);北京市自然科学基金(L233026)

Flow-field simulation mesh generation method for fuel solenoid

XIAO Haili¹, HU Yutong²^,³^,⁴, MAN Chunlei¹, CAO Yang¹, LI Haisheng²^,³^,⁴()

¹ Aero Engine Corporation of China Changchun Control Technology Company, Changchun Jilin 130102, China
² School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
³ Beijing Key Laboratory of Commercial Data Security Protection and Intelligent Governance, Beijing 100048, China
⁴ The Key Laboratory of Big Data Technology for Food Safety, Beijing 100048, China

Received:2025-08-22 Accepted:2026-06-25 Published:2026-10-31 Online:2026-10-26
Contact: LI Haisheng，E-mail：lihsh@th.btbu.edu.cn
Supported by:
National Natural Science Foundation of China(62277001);Beijing Natural Science Foundation(L233026)

摘要/Abstract

摘要：

网格生成是数值仿真前处理中的关键环节，网格质量直接影响仿真结果的计算精度、收敛稳定性与计算效率。燃油电磁阀流场仿真涉及入口窄缝、狭窄通道、转角过渡区和近壁面区域等复杂结构，局部尺度小、几何变化明显，流体经过时易产生速度梯度和压力变化，因此需要生成尺寸更小、分布更密和质量更稳定的局部网格。传统网格生成方法在处理复杂小尺度流道结构时，易出现关键区域分辨率不足、边界层刻画不充分和局部单元质量不稳定等问题，影响流场仿真的可靠性。针对上述问题，设计一种面向燃油电磁阀的流场仿真网格生成方法。首先，对几何模型进行拓扑检查与缺陷修补，消除影响网格划分的几何错误，并识别入口窄缝、狭窄通道和近壁面等重点区域。随后，构建空间尺寸场，指导受控表面离散，使网格尺寸根据几何尺度和流动敏感区域进行调整；在壁面附近沿法线方向逐层铺设过渡单元，形成高分辨率边界层网格，提高近壁流动特征捕捉能力。进一步结合几何梯度与误差指标开展自适应局部细化，重点加密燃油流经的关键通道、窄缝和局部突变区域，并生成高质量四面体网格。最后，通过网格平滑与单元质量优化改善单元形状、尺寸过渡和分布均匀性。生成网格应用于燃油电磁阀流场仿真，并与传统网格划分结果进行仿真效率和精度对比。实验结果表明，面向燃油电磁阀的流场仿真网格生成方法可使前处理时间缩短73.62%，全流程时间缩短40.75%，在提高网格生成与仿真效率的同时，仿真结果精度与传统方法保持一致，具有良好的应用价值。

关键词: 燃油电磁阀, 网格生成, 流场仿真, 计算机辅助工程, 自适应局部细化

Abstract:

Mesh generation is a key step in the preprocessing stage of numerical simulation, and mesh quality directly affects the computational accuracy, convergence stability, and computational efficiency of simulation results. Flow-field simulation of fuel solenoid valves involves complex structures such as inlet narrow slots, narrow channels, transition corners, and near-wall regions. These regions have small local scales and obvious geometric variations, and velocity gradients and pressure changes are likely to occur when fluid passes through them. Therefore, local meshes with smaller element sizes, denser distributions, and more stable quality are required. Traditional mesh generation methods often suffer from insufficient resolution in key regions, inadequate boundary-layer representation, and unstable local element quality when dealing with complex small-scale flow-channel structures, which affects the reliability of flow-field simulation. To address these problems, a mesh generation method for flow-field simulation of fuel solenoid valves was designed. First, topological inspection and defect repair were performed on the geometric model to eliminate geometric errors that may affect mesh generation, and key regions such as inlet narrow slots, narrow channels, and near-wall regions were identified. Then, a spatial size field was constructed to guide controlled surface discretization, so that the mesh size can be adjusted according to geometric scales and flow-sensitive regions. Transition elements were also arranged layer by layer along the normal direction near the wall surface to form a high-resolution boundary-layer mesh, thereby improving the ability to capture near-wall flow characteristics. Furthermore, adaptive local refinement was carried out by combining geometric gradients and error indicators. Key channels, narrow slots, and local abrupt regions through which fuel flows were refined, and high-quality tetrahedral meshes were generated. Finally, mesh smoothing and element quality optimization were used to improve element shape, size transition, and distribution uniformity. The generated mesh was applied to the flow-field simulation of the fuel solenoid valve, and its simulation efficiency and accuracy were compared with those of traditional meshing. The experimental results showed that the mesh generation method for flow-field simulation of fuel solenoid valves reduced preprocessing time by 73.62% and overall workflow time by 40.75%. While improving mesh generation and simulation efficiency, the simulation accuracy remained consistent with that of traditional methods, showing good application value.

Key words: fuel solenoid valve, mesh generation, flow-field simulation, computer aided engineering, adaptive local refinement

中图分类号:

TP 391

肖海丽, 胡宇桐, 满春雷, 曹洋, 李海生. 面向燃油电磁阀的流场仿真网格生成方法[J]. 图学学报, 2026, 47(5): 1095-1102.

XIAO Haili, HU Yutong, MAN Chunlei, CAO Yang, LI Haisheng. Flow-field simulation mesh generation method for fuel solenoid[J]. Journal of Graphics, 2026, 47(5): 1095-1102.

图/表 10

图1 燃油电磁阀外观结构示意图

Fig. 1 Schematic diagram of the appearance and structure of the fuel solenoid valve

图2 本文网格生成效果示意图

Fig. 2 Examples of mesh generated

图3 面向燃油电磁阀的网格生成与优化流程

Fig. 3 Mesh generation and optimization workflow for a fuel solenoid valve

图4 面向燃油电磁阀复杂流动特征的自适应网格生成与质量优化示例

Fig. 4 Adaptive mesh generation and quality-optimization example for the fuel solenoid valve flow domain

图5 燃油电磁阀体网格模型剖面

Fig. 5 Cross-sectional mesh model of the fuel solenoid valve

图6 燃油电磁阀体网格质量评价

Fig. 6 Mesh quality assessment of the fuel solenoid valve

表1 不同网格生成方法的网格质量对比

Table 1 Comparison of mesh quality among different mesh generation methods

方法	单元总数	平均偏度	最大偏度	偏度小于0.25占比/%
基于Delaunay 的自动四面体方法	1.00×10⁶	0.31	0.82	68.4
基于前沿推进的局部控制方法	0.95×10⁶	0.27	0.65	74.6
本文方法	0.92×10⁶	0.21	0.48	86.3

表2 仿真前处理时间对比

Table 2 Comparison of mesh generation time

应用型号	传统仿真时间/h	现行仿真时间/h	仿真效率提升/%
某型号A	12.87	3.42	73.43
某型号B	12.42	3.13	74.80
某型号C	13.20	3.65	72.35
某型号D	12.25	3.20	73.88

表3 仿真时间对比

Table 3 Comparison of mesh simulation time

应用型号	传统仿真时间/h	现行仿真时间/h	仿真效率提升/%
某型号A	29.75	17.61	40.81
某型号B	29.22	17.20	41.14
某型号C	30.62	18.22	40.50
某型号D	29.09	17.29	40.56

图7 压力场结果图

Fig. 7 Pressure field results

参考文献 17

[1]	WANG L, QIAN X D. Optimization-improved thermal- mechanical simulation of welding residual stresses in welded connections[J]. Computer-Aided Civil and Infrastructure Engineering, 2024, 39(9): 1275-1293. DOI URL
[2]	WANG B Q, YANG W D, YAO J, et al. The effects of temperature and indentation parameters on mechanical properties of calcite through molecular dynamics simulation[J]. Computers and Geotechnics, 2025, 177: 106835. DOI URL
[3]	HU Y T, SHI Y Z, LI H S, et al. A self-supervised framework for mesh denoising using graph neural networks in dental applications[J]. Applied Soft Computing, 2025, 185: 113989. DOI URL
[4]	HU Y T, LI H S, CHEN L, et al. Orthotvnet: a self-supervised denoising method on raw mesh for complex dental geometry[C]// 2026 IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE Press, 2026: 7462-7466.
[5]	KARADENIZ N, HAJNAL J V, IPEK Ö. Design of multi-row parallel-transmit coil arrays for enhanced SAR efficiency with deep brain electrodes at 3T: an electromagnetic simulation study[J]. Magnetic Resonance Materials in Physics, Biology and Medicine, 2025, 38(1): 107-120. DOI
[6]	黄猛, 李少猛, 王玉菊, 等. 基于电磁仿真和机器学习的快速目标成像模型[J]. 电波科学学报, 2025, 40(1): 58-62.
	HUANG M, LI S M, WANG Y J, et al. A fast target imaging model based on electromagnetic simulation and machine learning[J]. Chinese Journal of Radio Science, 2025, 40(1): 58-62 (in Chinese).
[7]	GARNIER P, VIQUERAT J, RABAULT J, et al. A review on deep reinforcement learning for fluid mechanics[J]. Computers & Fluids, 2021, 225: 104973. DOI URL
[8]	SONG M, LI C, GUO X W, et al. An adaptive gradient correction method based on mesh skewness for finite volume fluid dynamics simulations[J]. Physics of Fluids, 2025, 37(1): 015140. DOI URL
[9]	CANT R S, AHMED U, FANG J, et al. An unstructured adaptive mesh refinement approach for computational fluid dynamics of reacting flows[J]. Journal of Computational Physics, 2022, 468: 111480. DOI URL
[10]	MANI M, DORGAN A J. A perspective on the state of aerospace computational fluid dynamics technology[J]. Annual Review of Fluid Mechanics, 2023, 55: 431-457. DOI URL
[11]	AULTMAN M, WANG Z Y, AUZA-GUTIERREZ R, et al. Evaluation of CFD methodologies for prediction of flows around simplified and complex automotive models[J]. Computers & Fluids, 2022, 236: 105297. DOI URL
[12]	MAHDI E J, ALGBURI S, AL-ABADI N, et al. Photovoltaic panel cooling using ground source energy: CFD simulation[J]. Results in Engineering, 2024, 22: 102144. DOI URL
[13]	DIAZZI L, PANOZZO D, VAXMAN A, et al. Constrained Delaunay tetrahedrization: a robust and practical approach[J]. ACM Transactions on Graphics, 2023, 42(6): 181.
[14]	YU K X, CHEN J J, FU K J, et al. On the efficiency of the advancing-front surface mesh generation algorithm[J]. Computer-Aided Design, 2022, 153: 103403. DOI URL
[15]	GUO Y C, CAO Y P, WANG C, et al. VMesh: hybrid volume- mesh representation for efficient view synthesis[C]// SIGGRAPH Asia 2023 Conference Papers. New York: ACM, 2023: 17.
[16]	刘金宝, 高翔, 张翔, 等. 基于八叉树细化的高效四面体网格生成[J]. 空气动力学学报, 2025, 43(8): 50-58.
	LIU J B, GAO X, ZHANG X, et al. Efficient tetrahedral mesh generation algorithm based on octree refinement[J]. Acta Aerodynamica Sinica, 2025, 43(8): 50-58 (in Chinese).
[17]	刘增晟, 高翔, 张翔, 等. 面向科学工程计算的数模孔洞填充修复[J]. 国防科技大学学报, 2025, 47(2): 165-172.
	LIU Z S, GAO X, ZHANG X, et al. Filling and repairing holes in geometric model for scientific engineering computing[J]. Journal of National University of Defense Technology, 2025, 47(2): 165-172 (in Chinese).

面向燃油电磁阀的流场仿真网格生成方法

Flow-field simulation mesh generation method for fuel solenoid

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 17

相关文章 4

编辑推荐

Metrics

本文评价

[1]	张浩轩, 李海生, 王敏, 李楠. 基于物理信息神经网络的通用网格生成方法[J]. 图学学报, 2025, 46(5): 990-997.
[2]	冷珏琳, 徐权, 鲍献丰. 基于几何特征的非均匀矩形网格生成方法[J]. 图学学报, 2025, 46(4): 818-825.
[3]	王浩淼, 桑胜举, 段晓东, 张伟华, 陶体伟, 马婷. 虚拟现实环境下的协同式三维建模方法[J]. 图学学报, 2024, 45(1): 169-182.
[4]	岳明宇, 高希峰, 毕重科. 三维建筑模型的低模网格生成[J]. 图学学报, 2023, 44(4): 764-774.