论文标题
计算流体动力学和机器学习作为优化微质子几何形状的工具
Computational Fluid Dynamics and Machine Learning as tools for Optimization of Micromixers geometry
论文作者
论文摘要
这项工作探讨了一种新的在微流体领域优化的方法，使用CFD（计算流体动力学）和机器学习技术的组合。这种组合的目的是以较低的计算成本实现全球优化。初始几何形状的灵感来自Y型微质子器，其表面上具有圆柱凹槽，并且内部的障碍物。使用OpenFOAM软件进行了圆形障碍物的模拟，以观察障碍的影响。研究了障碍物直径（OD）和（OF）在[20,140]毫米和[10,160]毫米范围内的影响，研究了混合百分比（$φ$），压降（$ΔP$）和能源成本（$ΔP/φ$）的影响。使用机器学习分析数值实验。首先，使用神经网络来训练由输入OD和输出$φ$和$ΔP$组成的数据集。选择以数值优化具有凹槽和障碍物的微弹器的性能的目标函数（OBF）为$φ$，$ΔP$，$ΔP/φ$。遗传算法获得的几何形状提供了$φ$的最大值和$ΔP_S$的最小值。结果表明，$φ$随着OD的所有值的增加而单调增加。随着偏移的增加，观察到逆。此外，结果表明，$ΔP$ e $ΔP/φ$也随着OD而增加。另一方面，压降和混合能量的成本呈现出接近最低值的最大值。最后，直径获得的最佳值为OD = 131 mm，并且偏移= 10 mm，这对应于靠近通道壁的中等尺寸的阻塞。
This work explores a new approach for optimization in the field of microfluidics, using the combination of CFD (Computational Fluid Dynamics), and Machine Learning techniques. The objective of this combination is to enable global optimization with lower computational cost. The initial geometry is inspired in a Y-type micromixer with cylindrical grooves on the surface of the main channel and obstructions inside it. Simulations for circular obstructions were carried out using the OpenFOAM software to observe the influences of obstacles. The effects of obstruction diameter (OD), and offset (OF) in the range of [20,140] mm and [10,160] mm, respectively, on percentage of mixing ($φ$), pressure drop ($ΔP$) and energy cost ($ΔP/φ$) were investigated. Numerical experiments were analyzed using machine learning. Firstly, a neural network was used to train the dataset composed by the inputs OD and OF and outputs $φ$ and $ΔP$. The objective functions (ObF) chosen to numerically optimize the performance of micromixers with grooves and obstructions were $φ$, $ΔP$, $ΔP/φ$. The genetic algorithm obtained the geometry that offers the maximum value of $φ$ and the minimum value of $ΔP_s$. The results show that $φ$ increases monotonically with increasing OD at all values of OF. The inverse is observed with increasing offset. Furthermore, the results reveal that $ΔP$ e $ΔP/φ$ also increase with OD. On the other hand, the pressure drop and the cost of mixing energy present a maximum close to the lowest values of OF. Finally, the optimal value obtained for the diameter was OD=131 mm and for the offset OF=10 mm, which corresponds to obstruction of medium size close to the channel wall.
