Anchor-Oracle Fourier Neural Operator Framework for 3D Membrane Field Prediction in PEM Fuel Cells: From Steady-State Operation to Transient Degradation
Year: 2026
Type: preprint
Abstract: Membrane durability limits the service life of proton exchange membrane fuel cells, but the average degradation rate does not capture the degradation spatial heterogeneities. Existing approaches either resolve these spatial features at high computational cost (three-dimensional computational fluid dynamics modeling) or replace it with a scalar or curve-level surrogate; neither is fast enough to be queried across a wide spectrum of cell operation conditions. We develop an Anchor-Oracle Fourier Neural Operator, a three-stage neural operator that predicts the complete three-dimensional fields of membrane ionic conductivity and ionic current density from operating conditions and degradation time, achieving steady-state accuracy within one percent at four orders of magnitude lower cost than the reference solver. Using the surrogate as a full-field probe, we show that operating conditions reorganize the two fields' spatial heterogeneity along different axes, and that degradation rate and spatial heterogeneity vary non-monotonically with each other. We further find that current is progressively redistributed away from degraded regions over space and time. This redistribution is invisible to a single pooled correlation, and it advances together with the degradation front. We then use the surrogate to construct an inverse-design operating map, showing influences of different boundary conditions on power and durability.
Source: ChemRxiv
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204852588