We harness the physics-informed neural network (PINN) approach to extend the utility of phenomenological models for particle migration in shear flow. Specifically, we propose to constrain the neural network training via a model for the physics of shear-induced particle migration in suspensions. Then, we train the PINN against experimental data from the literature, showing that this approach provides both better fidelity to the experiments, and novel understanding of the relative roles of the hypothesized migration fluxes. We first verify the PINN approach for solving the inverse problem of radial particle migration in a non-Brownian suspension in an annular Couette flow. In this classical case, the PINN yields the same value (as reported in the literature) for the ratio of the two parameters of the empirical model. Next, we apply the PINN approach to analyze experiments on particle migration in both non-Brownian and Brownian suspensions in Poiseuille slot flow, for which a definitive calibration of the phenomenological migration model has been lacking. Using the PINN approach, we identify the unknown/empirical parameters in the physical model through the inverse solver capability of PINNs. Specifically, the values are significantly different from those for the Couette cell, highlighting an inconsistency in the literature that uses the latter value for Poiseuille flow. Importantly, the PINN results also show that the inferred values of the empirical model's parameters vary with the shear P\'eclet number and the particle bulk volume fraction of the suspension, instead of being constant as assumed in previous literature.
翻译:我们利用物理知情的神经网络(PINN)方法来扩大神经神经模型在剪切流中的颗粒迁移中的应用。 具体地说, 我们提议通过剪切粒子迁移物理模型来限制神经网络培训。 然后, 我们用文献中的实验数据来培训 PINN, 表明这个方法既能提供更忠实的实验, 也能提供对虚度迁移流流的相对作用的新理解。 我们首先核查 PINN 方法, 以解决非布朗悬浮中的放射粒迁移参数的反向问题。 在这个古典的例子中, PINN 生成与实验模型中两种参数的比例相同的价值( 如文献所报告的那样 ) 。 接着, 我们应用 PINN 方法来分析非布朗和布朗悬浮的颗粒迁移实验, 在Poiseuille 模型中, Pociential 迁移模型中, 缺乏精确校正的体积值。 使用PINN 方法, 我们用PINTrental 的数值来识别实际模型中未知/ 的数值, 而后期的数值则显示, 数字的数值的数值的数值是不同的。