Development of pressure-temperature limit curves considering unified constraint for reactor pressure vessel

Zheng, Y.; Wang, G.Z.; Tu, S.T.; Xuan, F.Z.

doi:10.1016/j.ijpvp.2023.105117

Cited by 3 publications

(1 citation statement)

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“…However, regardless of whether the approach is machine learning or a traditional empirical procedure, existing models are based on curves fitted from extensive experimental data, resulting in deterministic data that lack probabilistic reliability analysis. Given the requirements of probabilistic fracture mechanics for RPVs under various transients, such as pressurized thermal shock [28] and pressure-temperature limit curves [29], it is necessary to evaluate the uncertainty model of ∆RT NDT due to irradiation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Shift in Nil-Ductility Transition Reference Temperature for RPV Steels Due to Irradiation Embrittlement Using Probability Distributions and Gamma Process

Tang,

Li,

et al. 2024

Metals

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Reactor pressure vessel (RPV) steels are highly susceptible to irradiation embrittlement due to prolonged exposure to high temperature, high pressure, and intense neutron irradiation. This leads to the shift in nil-ductility transition reference temperature—∆RTNDT. The change in ∆RTNDT follows a certain distribution pattern and is impacted by factors including chemical composition, neutron fluence, and irradiation temperature. Existing empirical procedures can estimate ∆RTNDT based on fitting extensive irradiation embrittlement data, but their reliability has not been thoroughly investigated. Probability statistical distributions and the Gamma stochastic process were performed to model material property degradation in RPV steels from a pressurized water reactor due to irradiation embrittlement, with the probability models considered being normal, Weibull, and lognormal distributions. Comparisons with existing empirical procedures showed that the Weibull distribution model and the Gamma stochastic model demonstrate good reliability in predicting ∆RTNDT for RPV steels. This provides a valuable reference for studying irradiation embrittlement in RPV materials.

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Section: Introductionmentioning

confidence: 99%