Simulation of multi-species flow and heat transfer using physics-informed neural networks

Abstract

In the present work, single- and segregated-network physics-informed neural network (PINN) architectures are applied to predict momentum, species, and temperature distributions of a dry air humidification problem in a simple two-dimensional (2D) rectangular domain. The created PINN models account for variable fluid properties, species- and heat-diffusion, and convection. Both the mentioned PINN architectures were trained using different hyperparameter settings, such as network width and depth, to find the best-performing configuration. It is shown that the segregated-network PINN approach results in on-average 62% lower losses when compared to the single-network PINN architecture for the given problem. Furthermore, the single-network variant struggled to ensure species mass conservation in different areas of the computational domain, whereas the segregated approach successfully maintained species conservation. The PINN predicted velocity, temperature, and species profiles for a given set of boundary conditions were compared to results generated using OpenFOAM software. Both the single- and segregated-network PINN models produced accurate results for temperature and velocity profiles, with average percentage difference relative to the computational fluid dynamics results of approximately 7.5% for velocity and 8% for temperature. The mean error percentages for the species mass fractions are 9% for the single-network model and 1.5% for the segregated-network approach. To showcase the applicability of PINNs for surrogate modeling of multi-species problems, a parameterized version of the segregated-network PINN is trained that could produce results for different water vapor inlet velocities. The normalized mean absolute percentage errors, relative to the OpenFOAM results, across three predicted cases for velocity and temperature are approximately 7.5% and 2.4% for water vapor mass fraction.