Machine learning and numerical investigation on drag coefficient of arbitrary polygonal particles

IF 3.4 Q1 ENGINEERING, MECHANICAL 国际机械系统动力学学报(英文) Pub Date : 2024-08-27 DOI:10.1002/msd2.12124
Haonan Xiang, Cheng Cheng, Pei Zhang, Genghui Jiang
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Abstract

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.

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任意多边形颗粒阻力系数的机器学习和数值研究
阻力系数是表征粒子在流动中动力学特性的最重要参数,一直是大量研究的重点。虽然对于形状简单的颗粒可以实现很好的预测,但即使在中等雷诺数(Re)条件下,准确预测形状复杂的颗粒的阻力系数仍然具有挑战性。问题在于,颗粒的小尺度形状细节仍会对阻力系数产生相当大的影响,但这些几何细节无法用单一形状因子来描述。为了解决这一难题,我们利用现代深度学习方法的模式识别能力,将多个形状因子作为输入,以更好地描述颗粒形状细节,并将阻力系数作为输出。为了获得高精度的数据集,我们采用离散元法和改进的格点玻尔兹曼法速度插值方案来模拟和分析多边形颗粒的沉积动力学。开发了四种不同的机器学习模型来预测阻力系数,并进行了比较。结果表明,我们的模型可以很好地预测颗粒的阻力系数,平均误差小于 5%。这些研究结果表明,对于形状复杂的颗粒,数据驱动模型是预测阻力系数的一种有吸引力的选择。
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