AMADEUS project established a novel computational framework for analysis of liquid water evacuation in
Polymer Electrolyte Membrane Fuel Cells (PEMFCs). The core of the model was constituted by
development and computer implementation of a high-fidelity Enriched Finite Element Method (EFEM)-
based model for microfluidics. The model accounts for complex liquid domain shape deformations,
presence of surface tensions as well as including a sophisticated scheme for representing contact of
liquid droplets with solid substrates of given physical/chemical characteristics. The model was
successfully validated using benchmark cases and was applied for analysis of droplets emerging from
Gas Diffusion Layers into gas channel. Additionally, a new hybrid “Computational Fluid Dynamics(CFD)-
Machine Learning” strategy was developed and implemented for facilitating the simulation of liquid water
propagation through highly complex Gas Diffusion Layer, which can be represented as a collection of
pores and throats. To this end, a two-stage multifidelity model was built combining a low-fidelity neural
network trained essentially using numerous “computationally cheap” analytical predictions (based
on Haagen-Poiseuille law) with a high-fidelity neural network trained with a few data points obtained using
the CFD simulations utilizing the model developed in the first part of AMADEUS. The multifidelity model
predicts the hydraulic conductance of the pore-throat system of an apriori unknown shape by using the
above-mentioned multifidelity machine learning system. The predicted hydraulic conductances were used
in the OpenPNM (open source pore network model) to obtain results that consider the shape complexities
of the pores/throats (a feature not available before). Overall, the established numerical framework allows
analyzing liquid water propagation both in the Gas Diffusion Layer (GDL) and the gas channel of the fuel
cells.