Online stress corrosion crack and fatigue usages factor monitoring and prognostics in light water reactor components: Probabilistic modeling, system identification and data fusion based big data analytics approach

Technical Report · Wed Dec 10 00:00:00 EST 2014

DOI:https://doi.org/10.2172/1168230· OSTI ID:1168230

Mohanty, Subhasish M. ^[1]; Jagielo, Bryan J. ^[2]; Iverson, William I. ^[3]; Bhan, Chi Bum ^[4]; Soppet, William S. ^[1]; Majumdar, Saurin M. ^[1]; Natesan, Ken N. ^[1]

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1. Argonne National Lab. (ANL), Argonne, IL (United States)
2. Argonne National Lab. (ANL), Argonne, IL (United States); Oakland Univ., Rochester, MI (United States)
3. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Illinois at Urbana-Champaign, Champaign, IL (United States)
4. Argonne National Lab. (ANL), Argonne, IL (United States); Pusan National Univ., Busan (Korea, Republic of)

Nuclear reactors in the United States account for roughly 20% of the nation's total electric energy generation, and maintaining their safety in regards to key component structural integrity is critical not only for long term use of such plants but also for the safety of personnel and the public living around the plant. Early detection of damage signature such as of stress corrosion cracking, thermal-mechanical loading related material degradation in safety-critical components is a necessary requirement for long-term and safe operation of nuclear power plant systems.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USNRC; USDOE Office of Nuclear Energy (NE)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1168230
• Report Number(s): ANL/LWRS-14/02
• Country of Publication: United States
• Language: ENGLISH

Related Subjects

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS