Computers have been taught to clone granular soil particles for discrete element method simulations to alleviate difficulties of using three-dimensional imaging techniques for scanning a large number of particles. In this process, computers analyze a few scanned particles to extract morphological characteristics of the target soil, which are used to clone as many particles as necessary. However, many natural granular soils contain a wide range of particle shapes mixing more than one type of morphological characteristics, causing difficulties in cloning. This research aims to address this challenge by integrating spherical harmonics with Gaussian mixture model, expectation–maximization, and Dirichlet process. Spherical harmonics coefficients are used to characterize morphological information of the granular soil. Gaussian mixture model is used to fit a function to the mixed morphological characteristics. The expectation–maximization and Dirichlet process are used to estimate the fitting parameters in Gaussian mixture model. Then, Gaussian mixture model is used to generate new spherical harmonics coefficients and then generate new particles. The effectiveness and accuracy of the proposed methodology are verified using a Griffin sand. Although this approach is developed for granular soils, the proposed technique can also be used to clone other particulate materials.

中文翻译：

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通过将球谐函数与高斯混合模型，期望最大化和狄利克雷特过程相结合，克隆具有混合颗粒形态特征的颗粒状土壤

已经教导了计算机来克隆粒状土壤颗粒以进行离散元素方法模拟，以减轻使用三维成像技术扫描大量颗粒的困难。在此过程中，计算机会分析一些扫描的颗粒，以提取目标土壤的形态特征，然后根据需要克隆出尽可能多的颗粒。然而，许多天然粒状土壤包含多种混合超过一种形态特征的颗粒形状，导致克隆困难。这项研究旨在通过将球谐函数与高斯混合模型，期望最大化和Dirichlet过程相集成来应对这一挑战。球谐系数用于表征粒状土壤的形态信息。高斯混合模型用于拟合混合形态特征的函数。期望最大化和Dirichlet过程用于估计高斯混合模型中的拟合参数。然后，使用高斯混合模型生成新的球谐系数，然后生成新的粒子。使用格里芬沙验证了所提出方法的有效性和准确性。尽管这种方法是针对粒状土壤开发的，但所提出的技术也可以用于克隆其他粒状材料。使用格里芬沙验证了所提出方法的有效性和准确性。尽管这种方法是针对粒状土壤开发的，但所提出的技术也可以用于克隆其他粒状材料。使用格里芬沙验证了所提出方法的有效性和准确性。尽管这种方法是针对粒状土壤开发的，但所提出的技术也可以用于克隆其他粒状材料。