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A Stochastic Filling and Modeling Algorithm of Non-Equal Diameter Particles with Specified Probability Density for Porous Permeable Materials.
Zhang, Wei; He, Lile; Wang, Fazhan; Zhang, Guangyong.
Afiliação
  • Zhang W; School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
  • He L; School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
  • Wang F; School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
  • Zhang G; State Key Laboratory of Metal Extrusion and Forging Equipment Technology, China National Heavy Machinery Research Institute Co., Ltd., Xi'an 710018, China.
Materials (Basel) ; 15(14)2022 Jul 06.
Article em En | MEDLINE | ID: mdl-35888200
ABSTRACT
In this paper, a model generation algorithm for non-equal diameter particles with a specified probability density distribution is proposed. Based on considering the randomness of the size and distribution of the particles, the compact stacking of the particles is realized by the compactness algorithm, and then the spatial distribution of the tightly compacted particles is made to meet the random distribution of the specified probability density and the specified volume fraction by the filtering algorithm. The computational efficiency and effectiveness of the algorithm are verified, and the effects of the particle size and volume fraction on the distribution are analyzed. Finally, the proposed model has been used to study the permeability of a titanium porous filter cartridge. The results show that the size and location of the particle samples that are generated by the proposed algorithm follow specified probability distributions according to the requirements, and the volume fraction can be adjusted. Compared with the traditional algorithm, the computational effort and complexity are reduced. The resultant model can be used to study the permeability of porous materials and provide modeling support for structural optimization and further simulation of porous materials.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Materials (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Materials (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China