AI Optimizes Sensor Placement for Hydrogen Leak Detection.

Hydrogen infrastructure in enclosed spaces, such as fuel cell vehicle parking facilities, poses significant safety risks due to hydrogen's high flammability. Current leak detection systems are often reactive, only identifying hazards after dangerous concentrations have formed. This research introduces a proactive computational framework for optimizing sensor placement, combining computational fluid dynamics (CFD), genetic algorithms (GA), and a DeepSets neural surrogate model. A comprehensive CFD database was created from 180 scenarios in a simulated garage, covering various leak conditions. The multi-objective GA optimized sensor placement, achieving a 96.1% detection rate within 60 seconds and reducing blind areas to 0.12%, a 5% improvement over uniform layouts. Crucially, the DeepSets neural surrogate reproduced near-optimal configurations with minimal fitness gap, while reducing CFD evaluations by 89% and computational time by two orders of magnitude. This surrogate-assisted optimization allows for rapid design iterations, demonstrating that CFD-informed optimization can enhance detection effectiveness and reduce sensor requirements, laying a foundation for integration with digital twin systems for real-time risk assessment.