© V. Pištora et al., Published by EDP Sciences, 2025

1. Introduction

Long term operation of the existing fleet of nuclear power plants is the key issue for maintaining of secure energy supply under the requirement of decarbonisation. During LTO various degradation mechanisms affect the key components of NPPs. Some of the components are difficult and expensive to replace or are practically irreplaceable (like the reactor pressure vessel (RPV)). Deep understanding of aging of those components and their material degradation on the one hand and development of advanced methods of the assessment of NPP components integrity and lifetime on the other hand is the only way to ensure safe operation of NPPs for LTO.

The most important degradation mechanisms are fatigue and irradiation embrittlement. They were addressed in three projects funded within Euratom research and training programme HORIZON 2020, namely in APAL (Advanced PTS Analysis for LTO), INCEFA-SCALE (INcreasing safety in NPPs by Covering gaps in Environmental Fatigue Assessment – focusing on gaps between laboratory data and component SCALE) and FRACTESUS (Fracture mechanics testing of irradiated RPV steels by means of sub-sized specimens). These projects objectives, structure and main achievements are described in this paper. Let us note that at the time of writing this paper, the APAL and FRACTESUS projects had been completed, while INCEFA-SCALE was still ongoing.

2. Euratom projects presentation

2.1. APAL

2.1.1. APAL background

Irradiation embrittlement belongs to most significant aging mechanisms of the RPV. The assessment of RPV resistance against fast (brittle) fracture consists in comparison of the “loading curve”, i.e. the temperature dependency of stress intensity factor (calculated for each point of the postulated crack front) with the “resistance curve”, i.e. with the temperature dependency of fracture toughness of the (embrittled) RPV material. The most severe loading occurs during the emergency event of pressurised thermal shock (PTS) type, which is characterized by rapid cooling of the reactor downcomer, accompanied in most cases by high pressure in the RPV. The postulated flaw should conservatively “envelop” all flaws potentially existing in the RPV wall. Thus, the RPV integrity assessment for PTS is one of the most limiting safety assessments for the long-term operation of NPPs.

PTS assessment is a multidisciplinary task, consisting of selection of the assessed transients, thermal-hydraulic (TH) analyses (system TH analyses of the whole NPP and detailed mixing analyses in the reactor downcomer), structural (ST) analyses and fracture-mechanics (FM) analyses.

The currently performed PTS analyses (both thermal-hydraulic and structural and fracture-mechanics) in Europe are based on deterministic assessments. Deterministic analyses treat all input parameters as fixed values, in most cases with some additional margin on conservative side. To quantify the safety margins in terms of risk of RPV failure during PTS, application of advanced probabilistic methods is necessary. Probabilistic analyses, which consider some input parameters as statistical distributions, are currently used for TH safety analyses (focused on core melting) within Best Estimate Plus Uncertainties (BEPU) concept, but not for TH analyses focussed on PTS. Probabilistic FM analyses are used extensively in the US. In other countries, they are used for research purposes only. The challenge of APAL was application of probabilistic approach to both TH and ST/FM analyses. Thus, the propagation of uncertainties in the whole chain of PTS calculations can be analysed.

2.1.2. APAL project – overview and challenges

The APAL project (Advanced PTS Analysis for LTO) was funded by the EU within Euratom research and training programme HORIZON 2020. It lasted from October 2020 till September 2024.

In total, 14 partners from 11 EU countries (including Ukraine and Switzerland) and 2 international partners (from USA, Japan) with in-kind contributions participated in APAL, see Table 1.

The main objectives of the APAL project were the development of advanced probabilistic PTS assessment methods, quantification of safety margins for LTO improvements, and the development of best-practice guidance.

The work within APAL was organised in five technical work packages (WPs) plus Management and Dissemination WPs, see Figure 1.

Generally, both TH and ST/FM analyses can be performed either as deterministic or as probabilistic. The general approach is seen from Figure 2. The green arrows show the current approach to safety analyses focussed on core melting – performing either deterministic or probabilistic TH analyses. The blue arrows show the current approach to PTS assessment – using only deterministic TH analyses for both deterministic (Europe) and probabilistic (US) ST/FM PTS analyses. The yellow arrows show new feature of APAL - using probabilistic TH analyses for both deterministic and probabilistic ST/FM PTS analyses. The probabilistic TH analyses consider uncertainties in TH input data (both some NPP parameters and TH model or computer code parameters can be described as statistical distributions).

2.1.3. Overview of APAL work packages

WP1 – LTO improvements relevant for PTS analysis

Collection of partners’ experience and extensive literature review was performed within WP1 to establish the state of the art of LTO improvements that may have an impact on the PTS analysis results. Four improvements of PTS analyses significant for LTO were selected a priori to be reviewed in more detail in WP1, resulting in the following four tasks:

Task 1.1: State-of-the-art for weld residual stress (WRS).

Task 1.2: State-of-the-art for warm pre-stress (WPS) approach applied in PTS.

Task 1.3: State-of-the-art for the thermal-hydraulic (TH) analysis.

Task 1.4: State-of-the-art of probabilistic PTS analysis and relevant statistical tools.

Moreover, a specific task was established to investigate further potential LTO improvements (due to adjustment of NPP parameters including operator actions) potentially mitigating the course of PTS regimes:

Task 1.5: Identification of further LTO improvements having an impact on PTS and selection for assessment.

Within each task, a technical questionnaire covering the relevant issues was prepared and agreed by all partners. Involved partners filled the questionnaires based on their experience in PTS analyses performed in their countries/companies. Based on the compilation of the answers, the state of the art of the investigated LTO improvements was summarized. Moreover, understanding the best practices on questionable topics was agreed between the partners.

Finally, the discovered gaps, conclusions and recommendations were collected to be assessed quantitatively (on computational benchmarks) in the subsequent WP2, WP3 and WP4 in order to finally incorporate them in the best practice document for PTS analysis in WP5.

WP1 was completed in 2021 and its results are gathered in the summary report [1], which is publicly available on APAL project web page.

WP2 – Improved TH analysis

Identification and evaluation of uncertainties in TH analysis in the frame of PTS assessment was the objective of the WP2. Besides the model uncertainties connected with TH computer code models and plant uncertainties covering initial and boundary conditions and parameters of nuclear power plant systems, the work was also focussed on uncertainties connected with human factors. In addition, the effect of selected LTO improvements relevant for PTS analysis at the TH level was analysed in WP2.

WP2 consisted of 3 tasks:

Task 2.1: Quantification of impact of LTO improvements and human factor on TH analysis boundary conditions.

In the Task 2.1, TH analyses for the base case, i.e., small break loss-of-coolant-accident (SB LOCA) with 50 cm² break in hot leg of KWU-1300 PWR, and for the nine LTO improvements selected in WP1 were performed using different TH computer codes. Output TH data (time variations of pressure, temperature, and heat transfer coefficient) representing the base case and the selected LTO improvements were delivered to WP3 and WP4 for deterministic and probabilistic ST/FM PTS analyses. System and mixing codes used in the TH simulations were RELAP5, ATHLET, TRACE, KWU-MIX, GRS-MIX and ECC-MIX.

The following nine LTO improvements and operator actions were analysed in the Task 2.1:

• Heating of water in the high-pressure safety injection (HPSI) tanks.

• Heating of water in the accumulators (ACC).

• Heating of water in the low-pressure safety injection (LPSI) tanks.

• Decreasing the HPSI head.

• Decreasing the HPSI capacity.

• Reduction of HPSI flow (operator action).

• Decreasing of ACC pressure.

• Change of cooldown rate (operator action).

• Isolation of ACC (operator action).

Task 2.2: Evaluation of uncertainties in TH analysis related to computer code models, plant parameters, and human factors.

In the Task 2.2, uncertainties in thermal-hydraulic analyses for PTS were identified and prepared for application in best-estimate plus uncertainty (BEPU) analyses to be performed in Task 2.3.

Task 2.3: Performance of the TH uncertainty analysis and export of TH data sets.

In the Task 2.3, TH uncertainty analysis and export of TH data sets were performed. A standard BEPU analysis performed in the Task 2.3 consisted of a best-estimate reference calculation, definition of input uncertainties applied, the set of BEPU calculations (with 59 or more samples by the Wilks method), and the sensitivity analysis. WP2 was completed in 2023 and its results are gathered in the summary report [2] which is publicly available on the APAL project web page.

WP3 – Deterministic margin assessment

The WP3 objective was to assess the impact of LTO improvements (established within WP1 and analysed from TH point of view in WP2) and uncertainties in TH data on PTS analysis by deterministic ST and FM analyses. Margins related to selected nine LTO improvements were quantified to evaluate their benefit for PTS analysis. The impact of uncertainties in TH data was determined to quantify the inherent margins related to TH data used in deterministic PTS analysis.

WP3 consisted of 5 tasks:

Task 3.1: Structural assessment.

Structural finite element method (FEM) calculations for the determination of temperature and stress fields in the RPV wall were performed for the base case and for the selected LTO improvements and uncertainties in TH data.

Task 3.2: Definition of fracture-mechanics benchmark.

A benchmark for the deterministic fracture-mechanics assessment was defined, which included:

Task 3.3: First fracture-mechanics benchmark performance.

The first fracture-mechanics benchmark performance was carried out based on the base case set of TH data.

Task 3.4: Margin assessment related to LTO improvements.

The margins related to the LTO improvements were quantified based on the defined FM benchmark based on the set of TH data related to the LTO improvements (from Tasks 2.1 and 3.1). The margins were quantified in terms of maximum allowable reference temperature.

Task 3.5: Margin assessment related to TH uncertainties.

The margins related to the uncertainties in TH data were quantified based on the defined benchmark. The FM assessment was performed by all involved partners for the full statistical data set (e.g., 59 TH data sets for Wilks method from Task 2.3) and the results were compared with results for the base case (BC), the best-estimate set of TH data (BE) and with the conservative case (CC). It was shown that there is no chance to select some “bounding” TH data from the Wilks TH data sets (as was supposed at the beginning of APAL project), because it would lead to a non-physical and over-conservative solution. The margins were quantified in terms of maximum allowable reference temperature. Example or results of the margin assessment based on 59 TH data sets for Wilks method is shown in Figure 3.

WP3 was completed in 2024 and its results can be seen in the summary report [3] which is publicly available on the APAL project web page.

WP4 – Probabilistic margin assessment

The WP4 objective was to determine the impact of LTO improvements and uncertainties in TH data on PTS analysis by performing a probabilistic assessment. The assessment allows the quantification of safety margins in terms of risk of brittle fracture initiation or RPV failure. Probabilistic margins were evaluated and compared with deterministic margins.

WP4 consisted of 6 tasks:

Task 4.1: Structural assessment.

Structural FEM calculations for determining temperature and stress fields in the RPV wall were performed considering uncertainties in TH data. The calculations were performed by all involved partners for the full statistical data set (e.g., 59 TH data sets for Wilks method from Task 2.3).

Task 4.2: Definition of a probabilistic benchmarks.

A benchmark for the probabilistic fracture-mechanics assessment was defined.

Task 4.3: Baseline probabilistic benchmark.

The baseline probabilistic FM benchmark was carried out using the set of TH data for the base case transient (corresponding to the deterministic analysis carried out in WP3). The results were expressed in terms of conditional probabilities of crack initiation or RPV failure.

Task 4.4: Probabilistic benchmark related to LTO improvements.

The probabilistic benchmark related to LTO improvements was performed using the most influencing LTO improvements established according to the results of deterministic analysis done in Task 3.4.

Task 4.5: Probabilistic benchmark related to uncertainties in the thermal-hydraulic data.

The probabilistic benchmark related to uncertainties in the TH data was performed.

Task 4.6: Probabilistic margin assessment.

In this task, a comparison was performed between the baseline probabilistic benchmark and the probabilistic benchmark related to LTO improvements as well as the probabilistic benchmark related to uncertainties in the thermal-hydraulic data. As part of this comparison, a probabilistic margin assessment was carried out.

WP4 was completed in 2024 and its results can be seen in the summary report [4] which is publicly available on the APAL project web page.

WP5 – Definition of best practices for advanced PTS analysis

Throughout the duration of the APAL project, knowledge, experience and conclusions gained during the project performance were collected. Finally, the Best-practice guidance for deterministic and probabilistic RPV integrity assessment was formulated. It can be stated that this Best-practice guidance is the main outcome of the APAL project. It brings improved methodologies and also recommendations for the assessment of LTO improvements. It describes the advanced methods used in the project (both deterministic and probabilistic). Some new features are addressed like treating of various types of uncertainties in TH analysis, propagation of uncertainties in the entire PTS assessment, human factor, weld residual stress solutions, WPS methods, etc. The resulting document entitled “Final report on guidance on best-practice for deterministic and probabilistic RPV integrity assessment” [5] is publicly available on the APAL project web page.

It is expected that the Best-practice guidance will serve in the partners’ countries as the basis for improvements of national standards for RPV integrity assessment. Moreover, it is expected that the probabilistic assessment of PTS will become more acceptable in European countries.

WP6 Training and dissemination

Four workshops or seminars open to public (especially to regulatory bodies and main end-users) were organized to discuss all aspects of PTS analyses and the results of APAL project within WP6. Training courses for students and young engineers were also organized within WP6. Moreover, many journal and conference papers were prepared to disseminate results of the APAL project.

2.2. INCEFA-SCALE

2.2.1. INCEFA-SCALE – Extrapolation of environmentally assisted fatigue results from laboratory to real components

Fatigue assessments are important parts of justifying the structural integrity of nuclear plants, and their safe operation. There are multiple codes and standards that provide methods for accounting for the fatigue behaviour of the materials of plant components under relevant operating conditions to a desired level of reliability [6–9]. For cases that may be beyond the perceived scope of the codes, the technical basis for the methods may be presented separately. Environmentally-Assisted Fatigue (EAF) is one such example where NUREG/CR-6909 presents the methods for accounting for a light water reactor environment [10].

However, it has generally been observed by nuclear plant operators that for the EAF degradation mechanism, there appears to be a discrepancy between the good plant operational experience and perceived difficulty of obtaining an acceptable fatigue assessment result using the EAF methods [11]. The differences between the parameters of laboratory tests and the real-world conditions that plant components are subjected to is thought to be a substantial contributor to this discrepancy. This has been the subject of substantial research efforts to understand the contribution of surface finish [12, 13], strain rate [14], and thermo-mechanical fatigue [14]. These areas have yielded improved methods that are being incorporated into codes and have reduced this gap, but not completely closed it.

The goals of INCEFA-SCALE are to improve assessments of fatigue lifetimes of nuclear power plant components when subjected to EAF loading and provide guidance to the transferability of laboratory-scale testing results to component-scale behaviour.

INCEFA-SCALE will achieve these goals by:

• Evaluating how complex stress and material states can be created in laboratory settings, generate data to evaluate fatigue behaviour under those parameters, and explore how codified or new methods account for that behaviour for stainless steels,

• The development a better understanding of material performance, through characterisation of laboratory tested fatigue specimens and data mining of the MatDB database (the JRC administered database in which INCEF-PLUS and other data is already stored),

• Novel testing focused on defining the effects of Variable Amplitude (VA) loading, surface finish, notches, and multi-axial loading on the fatigue life of 316L stainless steel, an

• Delivering guidance, based on the technical output of the project, on the transferability of laboratory-scale data to component scale and plant relevant loading conditions.

2.2.2. INCEFA-SCALE project structure

The INCEFA-SCALE project comprises six Work Packages (WP), illustrated in Figure 4:

• WP1: Project Managemen

• WP2: Data Mining and Interpretatio

• WP3: Testing

• WP4: Modelling

• WP5: Mechanistic Understanding

• WP6: Dissemination and Trainin

The interaction between the INCEFA-SCALE WPs shown in Figure 5 highlights the substantial level of integration and collaboration within the project. Collaboration between the WPs is essential to the success of INCEFA-SCALE. The WPs need to inform and effectively define requirements between each other. For example, the collaborative definition of the test conditions in WP3 supports the ongoing work in WP4 and WP5. As the remaining test data becomes available from WP3, the assessment methods investigated in WP4 will mature and possibly evolve. An integral part of this way of working has been the development of the existing experimental methods in WP3 and WP5 to support the provision of the information required for fatigue assessments.

The integration of WP2 into WPs 3, 4 and 5 will allow the developments of INCEFA-SCALE to be placed into context with the global position of fatigue assessment for the nuclear industry and ensure decisions made by these WPs are fully informed. This approach will maximise the impact, usefulness, and success of INCEFA-SCALE.

INCEFA-SCALE is using two approaches to generating the complex stress and material states needed. First is Variable Amplitude (VA) loading that can produce complex hardening, stress patterns and mean stresses depending on the VA pattern applied, temperature, environment, and surface finish. Second is through specimen geometries that can produce multi-axial stresses and notch effects that are dependent on temperature and environment. These approaches require the development of new testing methods to enable production of consistent and comparable test data.

Taking the ASME Boiler and Pressure Vessel code (BPVC) as an example, complex loading states often resulting from the combination of geometrical features and thermal shocks are interpreted into values that can be used with fatigue design curves. For example, multiaxial loading is interpreted using methods such as Tresca stress or Von Mises effective strain range. In the case of geometrical features such as notches these may be treated using Fatigue Strength Reduction Factors (FSRF) or stress concentration factors.

Other complex loading aspects such as loading history (including VA loading) are intended to be accounted for in the application of adjustment factors on life and stress or strain to the mean air fatigue curve when forming the design fatigue curves [10]. However, detailed treatment of the effects like loading history and multiaxial loading within an EAF assessment are not presented in NUREG/CR-6909, which creates a gap in dealing with these behaviours that is acknowledged within the method itself [10]. Focussing on VA loading as the example, the method defined in NUREG/CR-6909 for accounting for VA loading within the ASME BPVC is to use the modified Goodman relationship to account for mean stress effects on the best-fit curve. The application of the adjustment factors (12 on life and 2 on stress or strain), which include the loading history transference factors, then leads to construction of the fatigue design curves. Miner's rule is applied to these design curves for each cycle in the VA waveform to calculate partial usage factors. The environmental cumulative usage factor (CUF_en) is calculated by applying the environmental effect factor F_en associated with each cycle in the VA waveform to the partial usage factors. The failure criterion is when the CUF_en reaches unity.

Research on stainless steel has indicated that the effect of VA loading on fatigue life may be accounted for by understanding the materials behaviour and resulting mean stress [15, 16]. Therefore, where methods use a mean stress correction combined with loading history transference factors this could double account for the effect of VA loading. Work on alternate methods that appear to better account for VA fatigue is already underway [17]. The VA conditions being tested in INCEFA-SCALE consist of periodic overloads, periodic underloads, and periodic over and underloads. These waveforms are aimed at understanding how material hardening, and mean stress effects influence the fatigue life of the stainless steel in air and PWR conditions. Additionally, these tests can be used to understand the level of margin contained in design curves, such as ASME Section III. Figure 6 shows how the data obtained can still be bounded using the ASME design curve with an adjustment factor of 1.6 instead of 2 on stress or strain. This indicates that there is the potential to reduce some conservatism in the code, but the combination of this reduction with other approaches such as Fen-Threshold [12] requires careful consideration, which is currently in progress.

In the case of notch effects, FSRF testing rarely sees FSRF values greater than 3 for stainless steel [18, 19] as opposed to the value of 5 in ASME Section VIII [21]. This indicates a level of over-conservatism in the codes for stainless steel for dealing with aspects of plant components that create complex stress states. However, data studying these effects in nuclear plant relevant environments is very limited and further work would be necessary to justify the adoption or development of alternative methods. Data generation and methods development is underway in work package 4 of INCEFA-SCALE, and the results will be made available once they are mature enough.

2.2.3. INCEFA-SCALE outcomes

Since the project kick-off in September 2020, there have been several full project meetings and multiple further virtual gatherings of sub-groups with interests in the mechanistic understanding, testing, data mining and modelling activities. Key achievements at the time of this paper writing are:

• A collaboration with EPRI, NNL and INCEFA-SCALE, through non-disclosure agreements (NDA) have been agreed.

• The International Fatigue Database (IFD) Agreement has been signed and 5000 fatigue datapoints are now available for analysis in addition to the INCEFA-SCALE testing programme.

• WP2 has completed the development of a software application that will facilitate data mining activities using the information stored in MatDB and is updating this tool to enable beneficiaries to use the new IFD data in addition to data generated in INCEFA-SCALE.

• WP3 has completed many tests that are currently being assessed by the Expert panel for completeness and quality.

  • The study of VA loading in INCEFA-SCALE is new to many of the testing laboratories and has required improvements in experimental control and data acquisition as well as new procedures for extracting information from fracture surfaces developed through round robin programmes [20].

  • Manufacturing and testing methods for notched specimen experiments have been developed to enable testing across laboratories and environments to be done in a way that generates comparable data.

• WP4 has analysed the initially available data from the test programme and found preliminary observations that could be valuable.

  • Evolution of material response to complex loading and the resulting stresses.

  • Evaluation of how current and developing methods account for loading history and environment in design curves through mean stress and loading history adjustments.

  • Indications that the material response and the associated plasticity from the loading may have implications for the effect of environment.

• WP5 has generated significant data that will allow for the exploration of approaches to overall plant lifetime, as evaluating the fracture surfaces has yielded data that relates crack depth and the number of applied cycles.

• WP6 have set up project dissemination channels consisting of a public website (https://incefascale.unican.es), Twitter and LinkedIn presences.

  • An initial seminar has been held to provide an Environmentally Assisted Fatigue Workshop for PhD students, professional engineers, and researchers.

  • A final seminar is currently being planned to disseminate the results of INCEFA-SCALE.

The most important outcome for INCEFA-SCALE so far is the production of data that can be used to evaluate how to account for complex material behaviour within a fatigue design curve approach. This information is being used within INCEFA-SCALE and individual beneficiaries to understand current conservatism in fatigue assessments and optimise conservatism in emerging methods.

INCEFA-SCALE is improving the safety of nuclear plant operation by improving fatigue assessments and working towards the resolution of the discrepancy between operating experience and the perceived difficulty in achieving an acceptable fatigue assessment result. This goal will be achieved through improving the understanding of the behaviour of materials subject to fatigue loading through material characterisation, improved experimental methods for studying EAF, and the provision of new methods and guidance for accounting for plant relevant loading conditions in fatigue assessments. The outcomes will be provided in the form of a document that will provide guidance to engineers performing fatigue assessments.

The data and analytical methods evaluated within INCEFA-SCALE will provide the basis for future research that can further define and refine the development of fatigue design procedures within the nuclear industry and the understanding required to technically substantiate those developments.

Future projects based on INCEFA-SCALE should focus on reanalysing the available data to define progress against the knowledge gaps considering the substantial amount of work that has been accomplished in this area. Furthermore, future developments within this area require increasingly complex testing that is especially challenging in relevant environmental conditions. Projects purely focussed on test method development should be undertaken to ensure that suitable standardised methods are available to the community to produce the high-quality data needed for fatigue assessment methods development.

2.3. FRACTESUS

2.3.1. FRACTESUS background

Fracture toughness (FT) of a material characterizes its resistance against crack propagation and is an important design parameter to guarantee the structural integrity of components. The most conservative value for FT is obtained under mode I loading, where the load directly opens the crack. Theoretically, this FT is obtained under plane strain conditions, corresponding to an infinitely long and straight crack front.

Experimentally, the FT is obtained using specimens of finite thickness and hence finite length of the crack front. This leads to the so-called “loss of constraint” which is manifested by curvature in the crack front and the appearance of shear lips during crack growth. Thus, the use of finite thickness test specimens leads to a potential size dependence of the FT. To address these ambiguities, the specimen geometry and test conditions are specified in standards, for example, ASTM E1921 [22] and ASTM E1820 [23]. The Master curve (MC) approach described in ASTM E1921 provides a size correction factor, where FT data is renormalized to a compact tension (C(T)) specimen of 1-inch thickness (1T-C(T)).

In nuclear installations, many steels (both base metals and welds) suffer from irradiation damage [24]. High energy neutrons (>1 MeV) penetrate the material and cause high energy cascades, leading to irradiation damage in its microstructure. Consequently, the material mechanical properties degrade leading to a reduction of its FT. The MC approach is used to quantify this degradation by evaluating the ductile-to-brittle transition temperature, referred to as reference temperature T₀, for unirradiated and irradiated materials. Due to the irradiation induced embrittlement this value increases.

To monitor materials degradation in nuclear power plants, in particular, in the reactor pressure vessel (RPV) and its welds, surveillance capsules containing samples from the same batch that composes the RPV and its welds are placed inside the vessel. As the capsules are placed closer to the reactor core than the vessel wall, they receive a higher dose, allowing to evaluate the mechanical state of the vessel and its welds for future doses [25]. However, surveillance samples are scarce, and to improve statistics in the mechanical analysis, it is convenient to machine smaller fracture toughness specimens from already tested (broken) specimens (typically Charpy-V notch impact specimens).

2.3.2. FRACTESUS project scope

The FRACTESUS project aims to validate the usage of small-scale specimens in fracture toughness testing for the purpose of safety assessment of reactor pressure vessels (RPV) [26–29]. Large inter-laboratory testing was included in the FRACTESUS project to demonstrate the repeatability and reproducibility of the results of fracture toughness testing using small-scale specimens. Various materials relevant for most of the available RPV materials and irradiation conditions were investigated. Finite element models (FEM) were used to investigate the difference between large-size and mini-compact tension (MC(T)) specimens, as well as to quantitatively evaluate the resulting loss of constraints due to specimen size reduction. The optimal range of usability of MC(T) specimens could therefore be determined and evidenced with experimental results. The work undertaken in the project was aimed to convince appropriate authorities to allow the use MC(T) specimens in the safety assessment of RPV using Master Curve approach, and finally to enable the introduction of the MC(T) specimens into the surveillance programs. The scope matrix of the FRACTESUS project is illustrated in Figure 7.

2.3.3. FRACTESUS main results

In the project, more than 600 fracture toughness tests were performed on six (four base and two weld materials) different RPV relevant steels in irradiated and unirradiated state. The investigated steels and their main mechanical properties are summarized in Table 2. All tests were performed according to ASTM E1921 and ASTM E1820 standards. The materials were selected according to the following principles: i) to use both base and weld materials, ii) to cover a wide range of mechanical properties, iii) to cover a range of different chemical compositions, iv) to use pre-irradiated materials as no irradiation campaign is foreseen in the project, and v) to have a well characterized material with large availability of material to make sure that all participants can obtain sufficient amounts of material.

In total, 14 different laboratories participated in the experimental round robin. In addition, numerical FEM models were implemented to support the experimental tests.

For all base materials, agreement between data obtained from MC(T) and large specimens was satisfactory when accounting for macroscopic inhomogeneities present in the steel (A508 Cl.3 and A533B JRQ exhibit macroscopic inhomogeneities). For weld materials, the analysis was more subtle: while agreement between MC(T) and large specimen data was satisfactory for ANP-5 when accounting for material heterogeneity, this was not the case for the 73W weldment, both in irradiated and unirradiated state (see Refs [28–30] and references therein).

For weldments, the scale of inhomogeneity in a weld was such that a 1T-C(T) specimen would sample across several weld beads, whereas a MC(T) specimen would sample less than a single weld bead. This suggests that metals and especially weld metals can exhibit microstructural inhomogeneity on a scale that significantly affects toughness testing. Both weld and base metals were likely to contain multiple constituent regions of substantial size such as recrystallized and as-deposited regions of a weld bead or islands of ferrite, bainite or martensite that reflected the dendrite spacing typical of castings. Each region contained a distribution of weak links, which may or may not overlap with those in other regions. This underscores the necessity of testing a larger number of specimens to ensure an accurate assessment of inhomogeneity (see Ref. [30] and references therein).

Finally, it is noted that all MC analyses on complete MC(T) data sets yielded inhomogeneous data sets, regardless of the homogeneity or inhomogeneity in the reference data set. The small sampling volume combined with the weakest link theory interpretation means that local differences in the material may result in a large variation from one tested MC(T) specimen to another. Thus, rather than MC(T) specimens misinterpreting homogeneous materials as being inhomogeneous, MC(T) specimens are more adept at identifying inhomogeneity than larger specimens (see Refs [28–30] and references therein).

This last point should be taken as a strength of the use of MC(T), i.e., the capability to sample material properties very locally if desired. When a complete block or plate needs to be characterized, MC(T) specimens need to be collected from various locations within the block or plate in the same manner as with larger specimens.

Overall, fractography supported the use of toughness data from MC(T) specimens if the tests were valid according to ASTM E1921-21. Ductility at edges of specimens did not dominate nucleation and the asymmetry of crack front did not dominate nucleation either. The fracture mode was likely to be unaffected by specimen size. A concentration of initiation sites in the central half of the specimen was observed due to the missing side grooves. However, since the relation between the FT and the distance to the initiation site was unaffected by specimen size, the stress and strain conditions at fracture initiation should be the same in the miniature and larger specimens (see Ref. [30] and references therein).

As most important guidelines for MC(T) testing we mention (see Refs [30, 31] and references therein):

• A test temperature selection algorithm was developed based on censoring statistics to minimize the number of invalid tests [32].

• A data set of 30 specimens or more is recommended to perform a reliable assessment of inhomogeneity according to ASTM E1921-21 Appendix X5.

• A set size of at least 16 specimens is recommended to achieve scatter in T₀ values σ_T0≤7 °C.

• In absence of a bimodal or multimodal analysis, an additional uncertainty related to material variability of 7 °C is proposed for a base metal.

• As a result of the FRACTESUS project, a ballot to explicitly mention the use of MC(T) specimens in ASTM E1921 has been submitted, and several other ballots (e.g., concerning size correction) are under preparation. Recently, fracture toughness data using MC(T) specimens have been accepted by U.S. Nuclear Regulatory Commission (NRC), as long as the test data satisfies ASTM E1921.

3. Conclusion

The continuous increase in knowledge on the material aging mechanisms and improved procedures for NPP components integrity and lifetime assessment can enable the operation of the current NPPs in the long term. In this article, based on the work carried out in the 3 European projects APAL, INCEFA-SCALE and FRACTESUS, we present the main results achieved in these projects. All three projects contribute to the safe and economic exploitation of NPPs in the frame of LTO under the common theme: “the link between material property and integrity assessment of components”. INCEFA-SCALE aims to improve the transferability of laboratory scale EAF testing results to component-scale behaviour. APAL aims to derive the most appropriate “loading curve” during a PTS event, while FRACTESUS aims to deliver the “resistance curve” based on small-scale specimen fracture toughness testing.

Within the APAL project, the Best-practice guidance for deterministic and probabilistic RPV integrity assessment [5] was issued, which can be considered as the main outcome of the project. It brings improved methodologies and recommendations for the assessment of LTO improvements and TH uncertainties. It describes the advanced methods for PTS assessment (both deterministic and probabilistic). Some new features are addressed like treating of various types of uncertainties in TH analysis, propagation of uncertainties in the entire PTS assessment, human factor, weld residual stress solutions, WPS methods, etc. It is expected that the Best-practice guidance will serve in the partners’ countries as the basis for improvements of national standards for RPV integrity assessment. The APAL consortium members plan to prepare a new proposal when appropriate EURATOM call will be open, where some open issues found during APAL will be addressed.

The most important outcome for INCEFA-SCALE so far is the production of large amount of experimental data that can be used to evaluate how to account for complex material behaviour within a fatigue design curve approach. The data and analytical methods evaluated within INCEFA-SCALE will provide the basis for potential future research that can further define and refine the development of fatigue design procedures within the nuclear industry. Future projects based on INCEFA-SCALE should focus on reanalysing the available data to define progress against the knowledge gaps.

As a result of the FRACTESUS project, a ballot to explicitly mention the use of MC(T) specimens in ASTM E1921 has been submitted, and several other ballots (e.g., concerning size correction) are under preparation. Recently, fracture toughness data using MC(T) specimens have been accepted by U.S. Nuclear Regulatory Commission (NRC), as long as the test data satisfies ASTM E1921.

Acknowledgments

Vladislav Pištora as coordinator of the APAL project would like to thank many excellent contributors from 14 partner's organisations (+ 2 international partners OCI and JAEA) who participated in the work on APAL, Alec McLennan as coordinator of the INCEFA-SCALE project would like to acknowledge all partners in the project are as well as the support from EPRI and NNL, Giovanni Bonny as coordinator of the FRACTESUS project would like to acknowledge all partners in the FRACTESUS consortium. Special recognition is given to the late coordinator Tomasz Brynk, who passed away on 28 July 2022.

Funding

The APAL project has received funding from Euratom research and training programme HORIZON 2020 under grant agreement number 945253. The INCEFA-SCALE project has received funding from the Euratom research and training program 2014-2018 under grant agreement No 945300. The FRACTESUS project received funding from the Euratom research and training programme 2020-2024 under grant agreement number 900014. The views and opinions expressed herein do not necessarily reflect those of the European Commission.

Conflicts of interest

The authors declare that they have no competing interests to report.

Data availability statement

The data used or generated within the projects are shown or referenced within their relevant deliverables (some of the deliverables are referenced here).

Author contribution statement

Part on APAL - Vladislav Pištora, INCEFA-SCALE - Alec McLennan, FRACTESUS - Giovanni Bonny, common parts – all.

References

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