Physics-informed neural network integrate with unclosed mechanism model for turbulent mass transfer

Turbulent mass transfer is widespread in chemical engineering processes including separation, reaction, and others. Traditionally, turbulent mass transfer processes can be simulated by computational fluid dynamics (CFD) methods. However, CFD methods require adequate turbulent models for the fluid flow and mass (and heat) transfer, which are normally difficult to develop for reliable solutions. Moreover, the CFD simulation is computationally intensive and hard to use in the cases like process optimization or analysis where the simulation needs to be called repeatedly. In this paper, physics-informed neural network (PINN) is developed and trained by unclosed mechanism model and sparse observation data of turbulent mass transfer process. The PINN method shows stronger generalization ability in solving the velocity, pressure and concentration fields in a turbulent mass transfer process than traditional deep neural network (DNN) method and turbulent Schmidt model. Under different boundary conditions, the PINN developed in the present paper can instantly predict the concentration distributions with sufficient accuracy and be used for inverse computation for estimating the turbulent viscosity and mass diffusivity as output results. The PINN is also capable of handling data noise by adjusting parameters, suggesting its potential in integrating experimental data.