论文标题
学习非牛顿液的未知物理学
Learning Unknown Physics of non-Newtonian Fluids
论文作者
论文摘要
我们将物理信息的神经网络（PINN）方法扩展到仅使用速度测量值的两个非牛顿系统（聚合物熔体和颗粒的悬浮液）的粘度模型。 PINN的粘度模型与具有较大绝对值的剪切速率的经验模型一致，但在分析模型具有非物理奇点的剪切速率的剪切速率上偏离。一旦学习了粘度模型，我们就会使用PINN方法仅使用边界条件来解决非牛顿流体流动的动量保护方程。
We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. The PINN-inferred viscosity models agree with the empirical models for shear rates with large absolute values but deviate for shear rates near zero where the analytical models have an unphysical singularity. Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.
