Diffusion-limited aggregation （DLA） assumes that particles perform pure random walk at a finite tem- perature and aggregate when they come close enough and stick together. Although it is well known that DLA in two d...Diffusion-limited aggregation （DLA） assumes that particles perform pure random walk at a finite tem- perature and aggregate when they come close enough and stick together. Although it is well known that DLA in two dimensions results in a ramified fractal structure, how the particle shape influences the formed morphology is still un- clear. In this work, we perform the off-lattice two-dimensional DLA simulations with different particle shapes of triangle, quadrangle, pentagon, hexagon, and octagon, respectively, and compare with the results for circular particles. Our results indicate that different particle shapes only change the local structure, but have no effects on the global structure of the formed fractal duster. The local compactness decreases as the number of polygon edges increases.展开更多
In order to study the constitutive behavior of concrete in mesoscopic level, a new method is proposed in this paper. This method uses random polygon particles to simulate full grading broken aggregates of concrete. Ba...In order to study the constitutive behavior of concrete in mesoscopic level, a new method is proposed in this paper. This method uses random polygon particles to simulate full grading broken aggregates of concrete. Based on computational geometry, we carry out the automatic generation of the triangle finite element mesh for the model of random polygon particles of concrete. The finite element mesh generated in this paper is also applicable to many other numerical methods.展开更多
The drag coefficient,as the most important parameter that characterizes particle dynamics in flows,has been the focus of a large number of investigations.Although good predictability is achieved for simple shapes,it i...The drag coefficient,as the most important parameter that characterizes particle dynamics in flows,has been the focus of a large number of investigations.Although good predictability is achieved for simple shapes,it is still challenging to accurately predict drag coefficient of complex-shaped particles even under moderate Reynolds number(Re).The problem is that the small-scale shape details of particles can still have considerable impact on the drag coefficient,but these geometrical details cannot be described by single shape factor.To address this challenge,we leverage modern deep-learning method's ability for pattern recognition,take multiple shape factors as input to better characterize particle-shape details,and use the drag coefficient as output.To obtain a high-precision data set,the discrete element method coupled with an improved velocity interpolation scheme of the lattice Boltzmann method is used to simulate and analyze the sedimentation dynamics of polygonal particles.Four different machine-learning models for predicting the drag coefficient are developed and compared.The results show that our model can well predict the drag coefficient with an average error of less than 5%for particles.These findings suggest that data-driven models can be an attractive option for the drag-coefficient prediction for particles with complex shapes.展开更多
A two-dimensional coupled lattice Boltzmann immersed boundary discrete element method is introduced for the simulation of polygonal particles moving in incompressible viscous fluids. A collision model of polygonal par...A two-dimensional coupled lattice Boltzmann immersed boundary discrete element method is introduced for the simulation of polygonal particles moving in incompressible viscous fluids. A collision model of polygonal particles is used in the discrete element method. Instead of a collision model of circular particles, the collision model used in our method can deal with particles of more complex shape and efficiently simulate the effects of shape on particle–particle and particle–wall interactions. For two particles falling under gravity, because of the edges and corners, different collision patterns for circular and polygonal particles are found in our simulations. The complex vortexes generated near the corners of polygonal particles affect the flow field and lead to a difference in particle motions between circular and polygonal particles. For multiple particles falling under gravity, the polygonal particles easily become stuck owing to their corners and edges, while circular particles slip along contact areas. The present method provides an efficient approach for understanding the effects of particle shape on the dynamics of non-circular particles in fluids.展开更多
基金Supported by the Hundred Talent Program of the Chinese Academy of Sciences (CAS)the National Natural Science Foundation of China under Grant Nos. 10974208, 11121403, 1083401401, and 91027045
文摘Diffusion-limited aggregation （DLA） assumes that particles perform pure random walk at a finite tem- perature and aggregate when they come close enough and stick together. Although it is well known that DLA in two dimensions results in a ramified fractal structure, how the particle shape influences the formed morphology is still un- clear. In this work, we perform the off-lattice two-dimensional DLA simulations with different particle shapes of triangle, quadrangle, pentagon, hexagon, and octagon, respectively, and compare with the results for circular particles. Our results indicate that different particle shapes only change the local structure, but have no effects on the global structure of the formed fractal duster. The local compactness decreases as the number of polygon edges increases.
文摘In order to study the constitutive behavior of concrete in mesoscopic level, a new method is proposed in this paper. This method uses random polygon particles to simulate full grading broken aggregates of concrete. Based on computational geometry, we carry out the automatic generation of the triangle finite element mesh for the model of random polygon particles of concrete. The finite element mesh generated in this paper is also applicable to many other numerical methods.
基金National Natural Science Foundation of China,Grant/Award Number:11972194。
文摘The drag coefficient,as the most important parameter that characterizes particle dynamics in flows,has been the focus of a large number of investigations.Although good predictability is achieved for simple shapes,it is still challenging to accurately predict drag coefficient of complex-shaped particles even under moderate Reynolds number(Re).The problem is that the small-scale shape details of particles can still have considerable impact on the drag coefficient,but these geometrical details cannot be described by single shape factor.To address this challenge,we leverage modern deep-learning method's ability for pattern recognition,take multiple shape factors as input to better characterize particle-shape details,and use the drag coefficient as output.To obtain a high-precision data set,the discrete element method coupled with an improved velocity interpolation scheme of the lattice Boltzmann method is used to simulate and analyze the sedimentation dynamics of polygonal particles.Four different machine-learning models for predicting the drag coefficient are developed and compared.The results show that our model can well predict the drag coefficient with an average error of less than 5%for particles.These findings suggest that data-driven models can be an attractive option for the drag-coefficient prediction for particles with complex shapes.
基金This study was funded by the National Science Foundation of China （Grant No. 11272176）.
文摘A two-dimensional coupled lattice Boltzmann immersed boundary discrete element method is introduced for the simulation of polygonal particles moving in incompressible viscous fluids. A collision model of polygonal particles is used in the discrete element method. Instead of a collision model of circular particles, the collision model used in our method can deal with particles of more complex shape and efficiently simulate the effects of shape on particle–particle and particle–wall interactions. For two particles falling under gravity, because of the edges and corners, different collision patterns for circular and polygonal particles are found in our simulations. The complex vortexes generated near the corners of polygonal particles affect the flow field and lead to a difference in particle motions between circular and polygonal particles. For multiple particles falling under gravity, the polygonal particles easily become stuck owing to their corners and edges, while circular particles slip along contact areas. The present method provides an efficient approach for understanding the effects of particle shape on the dynamics of non-circular particles in fluids.
