PSI - Issue 72

Sergio Arrieta et al. / Procedia Structural Integrity 72 (2025) 362–369

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2. Objectives The INCEFA-SCALE project investigates the mechanisms of EAF to enhance the applicability of laboratory derived fatigue data to actual component loading conditions within NPPs. A recognized knowledge deficit exists in predictive fatigue life assessment, as highlighted by international consensus. Addressing this, EPRI is conducting component-scale EAF testing (Steininger et al. (2017)) to increase the existing dataset. These tests are designed to generate data representative of component operational conditions, facilitating correlation with laboratory-scale results and improving predictive capabilities. The strategic approach of the INCEFA-SCALE project comprises three primary phases (Beswick et al. (2023)): 1. A comprehensive investigation of mechanical behavior will be conducted through extensive characterization of specimens under EAF conditions, coupled with detailed data mining procedures. 2. A targeted experimental program will be executed, focusing on the characteristic aspects of cyclic loading experienced by real-world components. 3. The project will culminate in the development of a methodology for extrapolating laboratory-derived data to predict performance under realistic loading scenarios and within actual component geometries. 3. Background Austenitic stainless steels are the material of choice for primary coolant piping in PWRs due to their required mechanical and corrosion resistance. These components operate under elevated temperature and pressure regimes within a chemically controlled aqueous environment. Furthermore, they experience non-uniform, dynamic stress states. Design codes, like ASME (2021), and experimental methodologies are employed to evaluate component fatigue life using simplified data. This process involves the application of an environmental factor to determine the accumulated fatigue damage, as described in Chopra and Stevens (2018). Comparative analyses with empirical data from real-scale components suggest a potential for conservatism in these estimations, possibly attributable to challenges in extrapolating laboratory-derived data to representative operational loads and component geometries. The correlation between these analytical results and observed component-scale behavior within operational plants is at present an area requiring further investigation and improved understanding, as noted by Tice et al. (2018). Recent studies have refined predictive models for the fatigue life of stainless steel under various conditions. Currie et al. (2018) improved fatigue life prediction under thermal transients, while McLennan et al. (2020) quantified the impact of surface finish on fatigue performance. These advancements facilitate more accurate simulations of operational conditions, potentially reducing conservative design margins. However, further research is required to fully elucidate the behavior of stainless steels subjected to EAF processing and to establish robust methodologies for extrapolating laboratory-derived data to component-level performance. The INCEFA-SCALE project is designed to address these critical knowledge gaps. The INCEFA-PLUS project, the precursor to INCEFA-SCALE, investigated the influence of several parameters on the fatigue life of austenitic steels. These parameters included strain range, environment (air and simulated PWR), surface roughness, mean strain, strain rate, and hold time, along with their potential interactions. Over 250 fatigue tests were conducted. Statistical analysis, detailed in Bruchhausen et al. (2021), identified strain range, environment, and surface roughness as significant factors. Furthermore, statistically significant interaction effects were observed between environment and surface roughness, and between environment and strain range. Mean strain and hold time were not found to significantly affect fatigue life within the tested parameters. 4. Project organization The INCEFA-SCALE project is structured into six interconnected work packages: Project Management (WP1), Data Mining (WP2), Test Program (WP3), Modeling and Development of Assessment Rules (WP4), Mechanical Understanding (WP5), and Dissemination and Training (WP6). The project consortium consists of seventeen participating organizations: Amentum (UK, Project Coor dinator), PSI (Switzerland), ÚJV Řež (Czech Republic), VTT Technical Research Centre (Finland), CIEMAT (Spain), ASNR (France), University of Cantabria (Spain), CEA (France), JRC (the Netherlands), Framatome (France), EDF (France), Innomerics (Spain), Rolls-Royce (UK),