Multiphase Computational Fluid Dynamics Simulation-Based Performance Investigation of Hydrogen Production With Patterned Electrodes in Alkaline-Water Electrolysis

Abstract Hydrogen has emerged as a clean fuel for the energy transition toward net-zero carbon emissions, with water electrolysis identified as the most promising method for green hydrogen production at scale. Increasing cell efficiencies involves improving every part of the system, including the electrodes. Recent investigations have shown that electrode surface structure affects hydrogen evolution by increasing the active surface area for reactions with micro-pillars and pits, which have complex manufacturing processes. This study investigates surface macro-pits and patterns for industrial applications. Steady-state, multiphase computational simulations were carried out to investigate the characteristics of flat/macro-dimpled electrodes for hydrogen evolution. Results show that macro-dimples significantly eliminate static gas pockets and dimple size affects the hydrogen evolution process. To predict hydrogen evolution from patterned cathode electrodes, an artificial neural network model was developed—surface area, current density, and cathode position as variables. The developed model was trained using 440 data points extracted from simulations. High predictive accuracy was obtained. The model achieved a high coefficient of determination of 0.9772 and a low root mean squared error of 0.0022, indicating an excellent fit with the data. These findings highlight the potential of a macro-patterned electrode for controlling hydrogen bubble evolution for improved green hydrogen production performance.