Real-time monitoring of hydrogen composite pressure vessels using surface-applied distributed fiber optic sensors
In this paper, we report to the best of our knowledge for the first time on continuous real-time monitoring of composite overwrapped pressure vessels (COPVs) designed for hydrogen storage using surface-applied distributed fiber optic sensors (DFOS). We conducted continuous and real-time DFOS measure...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | JPhys Photonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7647/adb9ac |
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| Summary: | In this paper, we report to the best of our knowledge for the first time on continuous real-time monitoring of composite overwrapped pressure vessels (COPVs) designed for hydrogen storage using surface-applied distributed fiber optic sensors (DFOS). We conducted continuous and real-time DFOS measurements during pressure cycling tests consisting of periodic pressure fluctuations between 20 bar and 875 bar, with a rate of 5 cycles min ^−1 . During pressure cycling, the DFOS system measured strain changes, that under normal operating conditions were linearly correlated to changes in pressure. To detect and quantify damage-related anomalies, we trained a simple regression model to predict strain from pressure data and used the difference between predicted and measured values as a damage indicator. With our approach, the DFOS system not only detected and localized the damage but also continuously tracked its evolution in real time under dynamic pressure conditions. Furthermore, unlike previous studies where optical fibers were embedded within the composite structure, we applied them on the COPV surface, reducing both implementation cost and time while eliminating the need to modify the COPV manufacturing process. Based on our results, we are confident that DFOS can enhance safety and facilitate the transition from time-consuming periodic inspections to more efficient, machine learning-based predictive maintenance. |
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| ISSN: | 2515-7647 |