Acoustic sensing and autoencoder approach for abnormal gas detection in a spent nuclear fuel canister mock-up
[1];
- Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Pacific Northwest National Laboratory, Richland, WA, USA
Currently, spent nuclear fuel (SNF) from commercial nuclear power plants is stored in stainless-steel canisters for interim dry storage. To provide an inert environment, these canisters are backfilled with helium after vacuum drying. However, the helium environment may be contaminated during extended storage because of the material degradation. For example, the heavier fission gas xenon may be released from the fuel rods into the canister cavity should the fuel cladding be breached. Other gases such as air and water vapor may also be present as a result of leakage caused by chloride-induced stress corrosion cracking on the canister walls or by insufficient vacuum drying. Therefore, monitoring the gas composition can provide critical information about the health of SNF canisters. In this study, noninvasive testing was conducted on a 2/3-scaled SNF canister mock-up using acoustic sensing. Ultrasonic transducers were placed on the exterior surface of the canister to probe the gas composition. A dataset was collected by sealing the canister mock-up and introducing up to 1.53% argon or 1.29% air into the helium background gas. Three methods were used to detect changes in the gas composition: the time-of-flight (TOF) method, the differential method, and the autoencoder method. Results showed that the TOF method had sufficient resolution to detect abnormal gas concentrations of less than 1.0%. The differential method demonstrated a periodic in-phase and out-of-phase behavior between the benchmark (i.e., pure helium) and abnormal (i.e., with argon or air) state signals. The variational autoencoder (VAE) and the Wasserstein autoencoder (WAE) were trained on the benchmark data and were applied directly to the abnormal state data. It was found that both the unsupervised VAE and the WAE were able to distinguish the benchmark and abnormal states of the canister mock-up based on the reconstruction error.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Nuclear Energy (NE)
- Grant/Contract Number:
- AC05-00OR22725; NE0009171
- OSTI ID:
- 2477698
- Journal Information:
- Structural Health Monitoring, Journal Name: Structural Health Monitoring; ISSN 1475-9217
- Publisher:
- SAGE PublicationsCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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