Angelis Dimitrios, Sofos Filippos, Papastamatiou Konstantinos, Karakasidis Theodoros E
Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, 35100 Lamia, Greece.
Micromachines (Basel). 2023 Jul 19;14(7):1446. doi: 10.3390/mi14071446.
In this paper, we propose an alternative road to calculate the transport coefficients of fluids and the slip length inside nano-conduits in a Poiseuille-like geometry. These are all computationally demanding properties that depend on dynamic, thermal, and geometrical characteristics of the implied fluid and the wall material. By introducing the genetic programming-based method of symbolic regression, we are able to derive interpretable data-based mathematical expressions based on previous molecular dynamics simulation data. Emphasis is placed on the physical interpretability of the symbolic expressions. The outcome is a set of mathematical equations, with reduced complexity and increased accuracy, that adhere to existing domain knowledge and can be exploited in fluid property interpolation and extrapolation, bypassing timely simulations when possible.
在本文中，我们提出了一条可替代的途径，用于计算类泊肃叶几何形状纳米管道内流体的输运系数和滑移长度。这些都是计算量很大的属性，它们取决于所涉及流体和壁材料的动力学、热学及几何特性。通过引入基于遗传编程的符号回归方法，我们能够根据先前的分子动力学模拟数据推导出基于数据的可解释数学表达式。重点在于符号表达式的物理可解释性。结果是得到了一组数学方程，其复杂性降低而准确性提高，这些方程符合现有领域知识，可用于流体属性的内插和外推，尽可能绕过耗时的模拟。
