CFD and AI Optimize Hydrogen Leak Sensor Placement in Garages

A novel computational framework has been developed that combines CFD, genetic algorithms, and a DeepSets neural surrogate to enhance sensor placement for detecting hydrogen leaks in confined spaces. This research created a CFD database comprising 180 scenarios within a garage measuring 50 m x 30 m x 3 m, examining various leak locations, rates (ranging from 1 to 150 g/s), and ventilation conditions (ACH between 3 and 10 per hour). The multi-objective genetic algorithm achieved a detection rate of 96.1% in under 60 seconds and minimized blind spots to 0.12%, surpassing traditional uniform, random, and surrogate-assisted methods. This innovative approach tackles safety issues in hydrogen infrastructure for fuel cell vehicles, contrasting with existing reactive monitoring systems.