DeepSets Surrogate Cuts Hydrogen Leak Detection Sensor Cost 89%
96.1% detection rate in 60 seconds with 0.12% blind area—AI optimizes garage sensors.
A computational framework combining CFD, genetic algorithm (GA), and DeepSets neural surrogate optimized hydrogen leak sensor placement in enclosed infrastructure. Using a 50 m × 30 m × 3 m garage database of 180 scenarios, the GA achieved 96.1% detection within 60 seconds and reduced blind areas to 0.12%—an approximately 5% improvement in composite fitness over a uniform baseline. The DeepSets surrogate cut CFD evaluations by 89% and computational time by two orders of magnitude while maintaining near-optimal configurations (fitness gap below 0.01), enabling rapid design iteration for safer hydrogen infrastructure.
- GA achieved 96.1% hydrogen leak detection within 60s and 0.12% blind area in a 50m×30m×3m garage.
- DeepSets neural surrogate reduced CFD evaluations by 89% and computational time by 100x with <0.01 fitness gap.
- Optimized layouts outperform uniform sensor grids by ~5% in composite detection performance.
Why It Matters
Enables rapid, cost-effective sensor deployment for hydrogen safety in parking garages—key for fuel cell vehicle adoption.