Flow-field simulation mesh generation method for fuel solenoid

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

Abstract

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

CLC Number:

TP 391

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.

Figures/Tables 10

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

Fig. 2 Examples of mesh generated

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

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

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

Fig. 6 Mesh quality assessment of the fuel solenoid valve

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

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

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

Fig. 7 Pressure field results

References 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).