  In the present work, single- and segregated-network PINN architectures are
applied to predict momentum, species and temperature distributions of a dry air
humidification problem in a simple 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 CFD
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 modelling of multi-species problems, a
parameterised version of the segregated-network PINN is trained which could
produce results for different water vapour inlet velocities. The normalised
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 vapour mass fraction.
