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Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (8): 1597-1605    DOI: 10.3785/j.issn.1008-973X.2022.08.014
    
Discretizing approach for complex three-dimensional geometry based on meshless particle methods
Feng WANG(),Zhong-guo SUN*(),Xu ZHENG,Pei-dong HAN,Guang XI
School of Energy and Power Engineering, Xi'an Jiaotong University, Xi’an 710049, China
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Abstract  

The construction of three-dimensional complex shapes is one of the difficulties for application of meshless particle methods on internal flow simulation of fluid machinery. Based on the moving particle semi-implicit method (MPS), a generic discretizing approach was proposed to discretize arbitrary complex 3D geometry in fluid machines by combining the generic smooth wall model (GSW) and non-surface detection technique (NSD). The CAD models were discretized with shell meshes, and position of the nodes was extracted to generate single-layer wall particles. Initial fluid distribution was obtained by inputting fluid particles to the wall model based on the adaptability of fluid particles. Considering the main characteristic of the GSW model that the scale of wall particles has no influence on simulation, a coupled refining method including the local and nested refinement was developed. Surfaces with large curvature were locally refined, and the nesting refinement technique was applied to the surfaces that suffered from disturbance of wall particles on different surfaces. With this technique, the accuracy of normal vectors can be guaranteed and fewer particles were used to represent high-accuracy wall shape. A discretizing accuracy evaluation method was proposed, in which the normal smooth angle (NSA) was regarded as the criterion. Taking the actual centrifugal pump model as the example, the geometric accuracy of the discrete model was quantitatively described, and the recommended value of the NSA value of the characteristic model was analyzed and given. The whole procedure of constructing high-accuracy wall particle models was demonstrated, and the stability and accuracy of the proposed models were validated by two 3D hydrostatic cases with complex geometries, the dam-break impacting on an obstacle case and the inducer rotating in water case.



Key wordsmoving?particle?semi-implicit method      wall boundary condition      discretizing approach      fluid machinery      local refinement     
Received: 29 October 2021      Published: 30 August 2022
CLC:  O 357.1  
Fund:  国家自然科学基金资助项目(51922085,51790512)
Corresponding Authors: Zhong-guo SUN     E-mail: wangfeng3119@stu.xjtu.edu.cn;sun.zg@xjtu.edu.cn
Cite this article:

Feng WANG,Zhong-guo SUN,Xu ZHENG,Pei-dong HAN,Guang XI. Discretizing approach for complex three-dimensional geometry based on meshless particle methods. Journal of ZheJiang University (Engineering Science), 2022, 56(8): 1597-1605.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.08.014     OR     https://www.zjujournals.com/eng/Y2022/V56/I8/1597


基于无网格粒子法的复杂三维形状通用离散方法

复杂形状的三维建模问题一直是无网格粒子法应用于流体机械内流计算的难点之一,基于移动粒子半隐式法(MPS),结合通用光滑壁面边界(GSW)和无表面粒子判定技术(NSD),提出适用于流体机械任意复杂型面的通用离散方法. 将CAD壁面模型通过面网格进行离散,提取网格节点并生成单层壁面粒子. 利用粒子的自适应性对流体粒子进行填充布置以获取初始流体域. 基于GSW模型壁面粒子尺度无关性的特点,提出壁面粒子局部加密与嵌套加密耦合的技术. 针对主曲率较大的曲面采用局部加密,针对异面粒子干涉作用较强的薄壁曲面采用嵌套加密,从而在保证壁面法向量计算准确的前提下,用更少的壁面粒子实现复杂形状的精确表达. 定义以法向光滑角度(NSA)为准则数的离散精度评价体系,以实际离心泵模型为例,定量描述离散模型的几何精度,分析并给出特征模型准则数的参考值. 通过三维复杂静水算例、溃坝撞击挡板算例和诱导轮水中旋转算例阐述整套离散建模流程并验证本方法流动计算的准确性与稳定性.


关键词: 移动粒子半隐式法,  壁面边界条件,  离散方法,  流体机械,  局部加密 
Fig.1 Concept of genetic smooth wall model
Fig.2 Schematic diagram of contact model
Fig.3 Approach to compensate particle-number-density loss around complex geometry
Fig.4 Approach of establishing wall particle models
Fig.5 Local refinement for wall particles
Fig.6 Schematic diagram of nested encryption
Fig.7 Schematic diagram of normal smooth angle
Fig.8 Geometry of complicated hydrodynamic cases
Fig.9 Wall particle models of two hydrostatic cases
Fig.10 Position of free surfaces and pressure distribution of complicated hydrodynamic cases
Fig.11 Comparison of liquid’s shape between experiment result (t=0.4 s) and simulation result (t=0.439 s)
Fig.12 Comparison of time history of pressure on baffle
Fig.13 Geometry of inducer and monitoring position at bottom of water tank
Fig.14 Wall particle model of inducer
Fig.15 Discretization of fluid domains by particle-based and mesh-based methods
Fig.16 Comparison of pressure at bottom and surface of inducer rotating case
Fig.17 Position of monitoring points and comparison of pressure at surface monitoring points
Fig.18 Comparison of fluids fields of inducer rotating case
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