Flaw Sizing Techniques for GWPA Inspection of DST Primary Liner Floors
- Guidedwave (FBS, Inc.)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Monitoring the integrity of underground, double-shell tanks (DSTs) utilized for the interim storage of high-level waste (HLW) is critical for protecting human health and the environment while supporting their critical role in the waste processing and disposition process. In-service inspections of physically-accessible areas of the primary and secondary liners are conducted to assess leak integrity, but the failure of an in-service DST tank provides clear evidence that this is an insufficient inspection scope. To more-fully assess the leak integrity of in-service HLW storage tanks, a method must be developed for inspecting the limited-access tank regions as well. Guidedwave (FBS, Inc.) has developed and matured a guided wave phased array (GWPA) technology for remote volumetric inspection of high-level waste (HLW) double-shell tank (DST) primary liner floors via access through refractory pad air slots in cooperation with Washington River Protection Solutions (WRPS) and Pacific Northwest National Laboratory (PNNL). Guidedwave has demonstrated that the GWPA technology is an excellent fit for this application and that it can provide efficient, reliable detection of corrosion and crack detection in open floor plates and welds. However, the GWPA technology is currently capable of locating the flaws but not classifying them by type nor sizing them quantitatively. Flaw type and size are the two flaw characteristics that are needed by Tank Operations at both DOE-EM sites in order to determine if a flaw in a tank’s carbon steel plate or weld meets the DOE flaw acceptance criteria established for high-level waste tanks. The two characteristics are also needed to support other decisions/actions: situational awareness to update tank risk level, deduction of degradation mechanisms to improve corrosion control, and estimations of remaining years of tank leak integrity to decide tank fate. Under this Phase I SBIR project, Guidedwave and PNNL have applied several maching learning methods to the GWPA datasets collected on DST mockups with manufactured flaws in order to demonstrate the feasibility of expanding Guidedwave’s GWPA software by integrating flaw characterization capabilities based on these machine learning models.
- Research Organization:
- Guidedwave (FBS, Inc.)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- SC0019796
- OSTI ID:
- 1638374
- Type / Phase:
- SBIR (Phase I)
- Report Number(s):
- DOE-FBS-19796; 8142343437
- Country of Publication:
- United States
- Language:
- English
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